Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Learning Approach for Surface Crack Classification and Segmentation in Unmanned Aerial Vehicle Assisted Infrastructure Inspections

Surface crack detection is an integral part of infrastructure health surveys. This work presents a transformative shift towards rapid and reliable data collection capabilities, dramatically reducing the time spent on inspecting infrastructures. Two unmanned aerial vehicles (UAVs) were deployed, enabling the capturing of images simultaneously for efficient coverage of the structure. The suggested drone hardware is especially suitable for the inspection of infrastructure with confined spaces that UAVs with a broader footprint are incapable of accessing due to a lack of safe access or positioning data. The collected image data were analyzed using a binary classification convolutional neural network (CNN), effectively filtering out images containing cracks. A comparison of state-of-the-art CNN architectures against a novel CNN layout “CrackClassCNN” was investigated to obtain the optimal layout for classification. A Segment Anything Model (SAM) was employed to segment defect areas, and its performance was benchmarked against manually annotated images. The suggested “CrackClassCNN” achieved an accuracy rate of 95.02%, and the SAM segmentation process yielded a mean Intersection over Union (IoU) score of 0.778 and an F1 score of 0.735. It was concluded that the selected UAV platform, the communication network, and the suggested processing techniques were highly effective in surface crack detection.
Simultaneous Treatment of Agro-Industrial and Industrial Wastewaters: Case Studies of Cr(VI)/Second Cheese Whey and Cr(VI)/Winery Effluents

Hexavalent chromium (Cr(VI)) was co-treated either with second cheese whey (SCW) or winery effluents (WE) using pilot-scale biological trickling filters in series under different operating conditions. Two pilot-scale filters in series using plastic support media were used in each case. The first filter (i.e., Cr-SCW-filter or Cr-WE-filter) aimed at Cr(VI) reduction and the partial removal of dissolved chemical oxygen demand (d-COD) from SCW or WE and was inoculated with indigenous microorganisms originating from industrial sludge. The second filter in series (i.e., SCW-filter or WE-filter) aimed at further d-COD removal and was inoculated with indigenous microorganisms that were isolated from SCW or WE. Various Cr(VI) (5–100 mg L−1) and SCW or WE (d-COD, 1000–25,000 mg L−1) feed concentrations were tested. Based on the experimental results, the sequencing batch reactor operating mode with recirculation of 0.5 L min−1 proved very efficient since it led to complete Cr(VI) reduction in the first filter in series and achieved high Cr(VI) reduction rates (up to 36 and 43 mg L−1 d−1, for SCW and WW, respectively). Percentage d-COD removal for SCW and WE in the first filter was rather low, ranging from 14 to 42.5% and from 4 to 29% in the Cr-SCW-filter and Cr-WE-filter, respectively. However, the addition of the second filter in series enhanced total d-COD removal to above 97% and 90.5% for SCW and WE, respectively. The above results indicate that agro-industrial wastewater could be used as a carbon source for Cr(VI) reduction, while the use of two trickling filters in series could effectively treat both industrial and agro-industrial wastewaters with very low installation and operational costs.
Towards an adaptive execution of applications in heterogeneous embedded networks

Embedded networks are emerging in many application fields, such as the automotive or building and factory automation sector. Compared to other distributed systems, embedded networks offer a new challenge for developers: heterogencity and resource constraints. The nodes contained in these networks can differ greatly w.r. t. their storage, processing and sensing/acting capabilities, ranging from very simple sensor devices with very limited resources over programmable logic controllers to very powerful nodes such as PCs. In order to achieve an efficient execution of applications running on such a network, a middleware is required that automatically adapts the embedded network to the requirements of the installed applications. In this paper, we will present a model driven development approach that allows the specification of application requirements, and a corresponding middleware solution that supports the automatic adaptation of the application execution based on these requirements and the characteristics of the underlying hardware.
Integrating RSS from unknown access points in WLAN positioning

Location based services, as well as security issues, require the knowledge of the user's position within an indoor area. Here, we address the problem of how the location service based on a WLAN can profit of the Received Signal Strengths (RSSs) collected from those Access Points (APs) whose position is not available. For this task, we develop two different algorithms, both relying on a dual estimation approach, in which RSS measures from ‘unknown’ APs are used along with RSS from ‘known’ APs. Simulative and experimental analyses, both corroborating the effectiveness of our solutions, are carried out.
An Integrated Approach for Screening and Identification of Positive Allosteric Modulators of N-Methyl-D-Aspartate Receptors

N-methyl-D-aspartate receptors (NMDARs) are ionotropic glutamate receptors that play an important role in synaptic plasticity and learning and memory formation. Malfunctioning of NMDARs, in particular the reduction in NMDAR activity, is thought to be implicated in major neurological disorders. NMDAR positive allosteric modulators (PAMs) represent potential therapeutic interventions for restoring normal NMDAR function. We report a novel screening approach for identification and characterization of NMDAR-PAMs. The approach combines high-throughput fluorescence imaging with automated electrophysiological recording of glutamate-evoked responses in HEK-293 cells expressing NR1/NR2A NMDAR subunits. Initial high-throughput screening (HTS) of a chemical library containing >810,000 compounds using a calcium flux assay in 1536-well plate format identified a total of 864 NMDAR-PAMs. Concentration response determination in both calcium flux and automated electrophysiological assays found several novel chemical series with EC50 values between 0.49 and 10 µM. A small subset (six series) was selected and analyzed for pharmacological properties, subtype selectivity, mode of action, and activity at native NMDARs. Our approach demonstrates the successful application of HTS functional assays that led to identification of NMDAR-PAMs providing the foundation for further medicinal chemistry work that may lead to novel therapies for treatment of cognitive impairment associated with Alzheimer’s disease and schizophrenia.
Interferometric OBS imaging for wide-angle seismic data

ABSTRACTMarine wide-angle seismic data obtained using air guns and ocean-bottom seismographs (OBSs) are effective for determining large-scale subseafloor seismic velocities, but they are ineffective for imaging details of shallow seismic reflection structures because of poor illumination. Surface-related multiple reflections offer the potential to enlarge the OBS data illumination area. We have developed a new seismic imaging method for OBS surveys applying seismic interferometry, a technique that uses surface-related multiples similarly to mirror imaging. Seismic interferometry can use higher order multiple reflections than mirror imaging, which mainly uses first-order multiple reflections. A salient advantage of interferometric OBS imaging over mirror imaging is that it requires only single-component data, whereas mirror imaging requires vertical geophone and hydrophone components to separate upgoing and downgoing wavefields. We applied interferometric OBS imaging to actual 175 km long wide-angle OBS da.
Beyond Pain Relief: Unveiling the Multifaceted Impact of Anti-CGRP/R mAbs on Comorbid Symptoms in Resistant Migraine Patients

The study aimed to evaluate the effects of monoclonal antibodies (mAbs) acting on the calcitonin gene-related peptide (CGRP) or its receptor (anti-CGRP/R mAbs) on migraine comorbidities of depression, anxiety, and fatigue in patients resistant to traditional therapies. The issue addressed in this study is pivotal to unveiling the role of this neurotransmitter beyond pain processing. We conducted an open-label prospective study assessing comorbidities in patients with high frequency (HFEM) and chronic migraine (CM), medication overuse headache (MOH), and resistance to traditional prophylaxis. All patients were treated with anti-CGRP/R mAbs for 3 months. Seventy-seven patients were enrolled with either HFEM (21%) or CM (79%) with or without MOH (56% and 44%, respectively). We identified 21 non-responders (27%) and 56 responders (73%), defined on the reduction ≥50% of headache frequency. The two groups were highly homogeneous for the investigated comorbidities. Disease severity in terms of headache frequency, migraine-related disability, and affective comorbid symptoms was reduced in both groups with different thresholds; allodynia and fatigue were ameliorated only in responders. We found that anti-CGRP/R antibodies improved pain together with affection, fatigue, and sensory sensitization in a cohort of migraine patients resistant to traditional prophylaxis. Our results offer novel perspectives on the early efficacy of anti-CGRP/R mAbs in difficult-to-treat patients focusing on clinical features other than pain relief.
Effect of air profiling and frother concentration on a flotation bank performance

The impact of air distribution on the metallurgical performance of a flotation bank operating with a mineral slurry having a moderate and high frother concentration is assessed. A flotation bank of two and three 5L Denver cells was implemented, and air flow was distributed down the bank as increasing and decreasing profiles. It was observed that when operating the bank configurations with a moderate frother concentration (10 ppm DF 400) the increasing profile provided the highest Cu enrichment ratio at the expense of a slight reduction in Cu recovery. This increase in selectivity was mainly due to a significant reduction in the water recovery and mass-pull in the first cell. When the bank operated with a high frother concentration, i.e., well beyond the CCC, a significant increase in water recovery was observed, producing a significant loss in selectivity that could not be compensated by air profiling.
Simultaneous Direct and Augmented View Distortion Calibration of Optical See-Through Head-Mounted Displays

In Augmented Reality (AR) with an Optical See-Through Head-Mounted Display (OST-HMD), the spatial calibration between a user's eye and the display screen is a crucial issue in realizing seamless AR experiences. A successful calibration hinges upon proper modeling of the display system which is conceptually broken down into an eye part and an HMD part. This paper breaks the HMD part down even further to investigate optical aberration issues. The display optics causes two different optical aberrations that degrade the calibration quality: the distortion of incoming light from the physical world, and that of light from the image source of the HMD. While methods exist for correcting either of the two distortions independently, there is, to our knowledge, no method which corrects for both simultaneously. This paper proposes a calibration method that corrects both of the two distortions simultaneously for an arbitrary eye position given an OST-HMD system. We expand a light-field (LF) correction approach [8] originally designed for the former distortion. Our method is camera-based and has an offline learning and an online correction step. We verify our method in exemplary calibrations of two different OST-HMDs: a professional and a consumer OST-HMD. The results show that our method significantly improves the calibration quality compared to a conventional method with the accuracy comparable to 20/50 visual acuity. The results also indicate that only by correcting both the distortions simultaneously can improve the quality.
A 15 GHz 256/257 dual-modulus prescaler in 120 nm CMOS

A completely integrated 15 GHz prescaler with programmable division ratios of 256 and 257 is presented. The prescaler uses high speed high speed differential current-mode logic and merged AND-gates. Over a frequency band of 14 GHz the prescaler features an enhanced input sensitivity of less than -5 dBm. The circuit draws 77 mA from a single 1.5 V supply. An output buffer is also included in the circuit to drive 50 /spl Omega/ loads. The circuit is manufactured in 120 nm CMOS technology.
Automatic Removal of Imperfections and Change Detection for Accurate 3D Urban Cartography by Classification and Incremental Updating

In this article, we present a new method of automatic 3D urban cartography in which different imperfections are progressively removed by incremental updating, exploiting the concept of multiple passages, using specialized functions. In the proposed method, the 3D point clouds are first classified into three main object classes: permanently static, temporarily static and mobile, using a new point matching technique. The temporarily static and mobile objects are then removed from the 3D point clouds, leaving behind a perforated 3D point cloud of the urban scene. These perforated 3D point clouds obtained from successive passages (in the same place) on different days and at different times are then matched together to complete the 3D urban landscape. The changes occurring in the urban landscape over this period of time are detected and analyzed using cognitive functions of similarity, and the resulting 3D cartography is progressively modified accordingly. The specialized functions introduced help to remove the different imperfections, due to occlusions, misclassifications and different changes occurring in the environment over time, thus ncreasing the robustness of the method. The results, evaluated on real data, demonstrate that not only is the resulting 3D cartography accurate, containing only the exact permanent features free from imperfections, but the method is also suitable for handling large urban scenes.
Time‐aware and energy‐efficient opportunistic routing with residual energy collection in wireless sensor networks

In 1‐dimensional queue wireless sensor networks, how to balance end‐to‐end latency and energy consumption is a challenging problem. However, traditional best path routing and existing opportunistic routing protocols do not address them well because relay hop counts are usually much more, and the link appears more unreliable compared with general mesh topology. In this work, we formulate these 2 problems as a multiobjective optimization problem. Specifically, we first classify network packets into types of time tolerant and time critical and introduce a residual energy collection mechanism of neighboring nodes for forwarder set selection. We then propose a time‐aware and energy‐efficient opportunistic routing protocol (TE‐OR) to optimize energy consumption and to reduce latency for time‐critical packets. We evaluate TE‐OR by different parameters and compare it with existing protocols. The performance results show that TE‐OR achieves a trade‐off between energy consumption and time delay and balances energy consumption among nodes while guaranteeing the latency of time‐critical packets is minimized.
The CC genotype of the GNAS T393C polymorphism is associated with obesity and insulin resistance in women with polycystic ovary syndrome.

OBJECTIVE Variants in the Gs protein alpha subunit gene (GNAS1) are known to be involved in the pathogenesis of several endocrine and metabolic disorders. To understand genetic determinants of androgen excess, insulin resistance, and obesity in polycystic ovary syndrome (PCOS), we investigated the effect of the common GNAS1 T393C polymorphism on the phenotype of German PCOS women. DESIGN Two hundred and seventy-eight PCOS women and 820 Caucasian controls were genotyped for the common T393C polymorphism in GNAS1. To this end, genomic DNA was amplified by PCR with specific oligonucleotides and genotypes were determined using the restriction enzyme FokI. In addition, we evaluated whether the T393C polymorphism had an influence on the response to 6 months metformin treatment in a subgroup of 105 PCOS women. METHODS Anthropometric variables, metabolic parameters including indices of insulin resistance measured by oral glucose tolerance testing, and endocrine biochemical as well as clinical parameters were measured in all PCOS subjects. RESULTS GNAS1 genotype distributions were not significantly different between PCOS women and controls. In PCOS women, no significant differences in endocrine clinical and biochemical variables were found between the genotypes. However, the C-allele carrier group had significantly higher mean body weight, body mass index, leptin levels, and higher indices of insulin resistance compared with women with GNAS1 TT-genotype. Metformin treatment significantly improved hyperandrogenism, menstrual cyclicity, body weight, and insulin resistance independent of GNAS1 genotype. The major determinant of weight loss was body weight before treatment. CONCLUSION The T393C polymorphism is not associated with PCOS in Caucasian women, but may represent a genetic marker for increased susceptibility for obesity in this cohort.
Planning a tourism landscape in geosite area: Sipiso-piso waterfall

Landscape is one of the valuable assets in tourism, especially in rural tourism. Good landscape planning can be increasing the tourism in one place. In Geopark area, landscape planning is also necessary, and it is because geopark area is also a landscape. This paper aims to create a landscape planning concept in Sipiso-Piso Waterfall that can be used to improve the tourism in Sipiso-piso Waterfall. The method that used in this paper is a qualitative method that is used interviews with related stakeholders like, local figure, government, and academic, and field observation in the study area. The data obtained would be analyzed with four elements of landscape namely, open space, pedestrian path, street circulation, and street furniture. The result shows that the four elements of landscape in Sipiso-Piso Waterfall is still not proper and needs improvement.
Color Recognition by Extended Color Space Method for 64-color 2-D Barcode

Colorizing two–dimensional (2-D) barcodes is a promising modality for improvement on design and data capacity. The number of colors employed in recent 2-D color barcodes is limited less than 8. To increase the number of recognizable colors, we propose a color recognition method which classifies 64 colors, using an extended color space and pattern recognition techniques. The proposed method employs 4 seed colors embedded within 64-color code to prevent influences from environment illumination. In the proposed method, we extend the color representation for each code color in 2-D barcode from three components to higher number component. And a classifier is trained using the extended colors captured from several environmental lighting conditions. The classifier is selectable. In this paper, artificial neural network (ANN), support vector machine (SVM) and nearest neighbor (NN) are employed as the classifier. The experimental results shows that the proposed method achieves MSER less than 3.0%. Comparing the number of seed colors, 4 seed color is comparable to that using 8 seed color. SVM achieves the best recognition performance of 1.17% SER. These results suggest that 64-color recognition is possible by the proposed method.
Lapsiines and hisponines as phylogenetically basal salticid spiders (Araneae: Salticidae)

Increased phylogenetic resolution of the basal lineages of salticid spiders will help us understand their early evolution and provide better outgroups for phylogenetic studies within the major clades. We gathered sequences of nuclear and mitochondrial gene regions (28S, 18S, Histone 3, 16S-ND1, CO1) and used them to reconstruct salticid phylogeny by parsimony, likelihood and Bayesian methods. Our results confirm that lapsiines and hisponines are among the basal salticids, i.e. outside the major clade Salticoida. The lapsiines are resolved as sister group to the spartaeines. The precise placement of hisponines is unclear, but they may represent a deep-branching lineage independent from the spartaeines.
Spatially Coupled Protograph-Based LDPC Codes for Decode-and-Forward in Erasure Relay Channel

We consider spatially-coupled protograph-based LDPC codes for the three terminal erasure relay channel. It is observed that BP threshold value, the maximal erasure probability of the channel for which decoding error probability converges to zero, of spatially-coupled codes, in particular spatially-coupled MacKay-Neal code, is close to the theoretical limit for the relay channel. Empirical results suggest that spatially-coupled protograph-based LDPC codes have great potential to achieve theoretical limit of a general relay channel.
Importance Sampling Tree for Large-scale Empirical Expectation

We propose a tree-based procedure inspired by the Monte-Carlo Tree Search that dynamically modulates an importance-based sampling to prioritize computation, while getting unbiased estimates of weighted sums. We apply this generic method to learning on very large training sets, and to the evaluation of large-scale SVMs. The core idea is to reformulate the estimation of a score - whether a loss or a prediction estimate - as an empirical expectation, and to use such a tree whose leaves carry the samples to focus efforts over the problematic "heavy weight" ones. We illustrate the potential of this approach on three problems: to improve Adaboost and a multi-layer perceptron on 2D synthetic tasks with several million points, to train a large-scale convolution network on several millions deformations of the CIFAR data-set, and to compute the response of a SVM with several hundreds of thousands of support vectors. In each case, we show how it either cuts down computation by more than one order of magnitude and/or allows to get better loss estimates.
Text Entry in the E-Commerce Age: Two Proposals for the Severely Handicapped

People with severe motor disabilities have extreme access difficulties with all kinds of web services especially when they want not only to surf the web, but also write some text, e.g., to participate in an e-activity. Several problems arise when using traditional scanning systems, such as the low text entry rate, the time consuming task of learning the scan matrix layout, or simply, the poor visibility of the web page due to the large surface needed to display the complete scan matrix on the screen. We propose a reduced virtual keyboard based on scanning with only one switch as input device. The scan matrix consists of only three cells, so ambiguity is present due to the assignment of 26 characters to the three keys. Word-level and character-level disambiguation modes are explored using a mathematical model, and the text entry rates for an expert user were 15.9 and 10.3 words per minute respectively, using a scan period of 0.5 seconds. This keyboard could be embedded into a web page using a Java applet, JavaScript code or a Flash application, or be programmed as an independent application.