Miracle-dz/UniFAR-FTUs huggingface dataset:
DEU-Net: Dual-Encoder U-Net for Automated Skin Lesion Segmentation

The computer-aided diagnosis (CAD) of skin diseases relies heavily on automated skin lesion segmentation, albeit presenting considerable challenges due to lesion diversity in shape, size, color, and texture, as well as potential blurry boundaries with surrounding tissues. Traditional Convolutional Neural Networks (CNN) typically underperform in this domain, given their inherent constraints in global context information capture. In the present study, we present a new U-shaped network, Dual-Encoder U-Net (DEU-Net), which is based on an encoder-decoder architecture. DEU-Net integrates a dual-encoder branch comprising a convolutional encoder and a transformer encoder, thereby facilitating the concurrent extraction of local features and global contextual information. Additionally, in order to enhance the performance of DEU-Net, we employ an integrated test-time augmentation technique. To ascertain the efficiency and superiority of our proposed methodology, we performed comprehensive experiments across four widely accessible skin lesion datasets, namely ISIC 2016, ISIC 2017, ISIC 2018, and PH2. The Dice coefficients achieved on these datasets were 92.90%, 87.16%, 90.81%, and 95.65%, respectively. These results demonstrate superior performance compared to most current state-of-the-art methods. The source code is released at https://github.com/alikm6/DEU-Net.
Liang Nabulei Lisa: A late Pleistocene and Holocene sequence from the Aru Islands

The excavation at Liang Nabulei Lisa began on 24 November 1997, approximately one year after the Liang Lemdubu excavation was carried out (see Chapter 4, this volume). The site was selected for excavation as it was located close to a stream-fed sungai, had abundant cultural material on the surface and appeared to have some depth of deposit. The Lemdubu excavation had recovered a Pleistocene sequence dating from ca. 27,000 years ago through to the historic period, but the early to mid-Holocene were not represented. It was hoped that Nabulei Lisa would complement the Lemdubu sequence by providing a full Holocene sequence. Liang Nabulei Lisa is only about 50m inland from the landward end of the southern arm of Sungai Dosi, a tributary of the western end of Sungai Manumbai (Fig. 7.1). It lies near the crest of a low but steep limestone ridge on the eastern side of the tidal sungai, and at about 30m altitude. The sungai is fringed by mangroves and Barringtonia on muds, while the rocky slopes around the cave support a low and not very dense forest in which figs, Eugenia, Litsea legumes, and Fagraea are common, with many palms and large climbers evident. A more varied rainforest occupies the lower areas between ridges although this has been disturbed by cutting and shifting cultivation. Like Lemdubu, Nabulei Lisa is a tunnel cave left stranded near the crest of a ridge, with a collapsed section on the ridge that results in it having two entrances: one northwest and one southeast facing (Figs 7.2–7.4). The cave is capacious and very dry but only a restricted area close to the northwest facing entrance would be useful for habitation. The southeast entrance rises quite steeply to a height of about eight metres above the cave floor, is extremely rocky, and is partially blocked by large boulders of roof-fall and flowstone/stalagmite formations. Extensive stalactite and stalagmite formations occur in the central and southern areas of the cave (Figs 7.2–7.4). A 3–5m wide potential living floor of soft cave earth, interrupted by scattered stalagmite bases, extends along the northeastern wall for about 18m (Fig. 7.2). This surface has dense patches of marine shell scattered about.
Constrained Fixed-Time Terminal Sliding-Mode Control With Prescribed Performance for Space Manipulator System

This article focuses on the high-precision position tracking problem for the free-float space manipulator (FFSM) system subject to parametric uncertainties, input saturation, and state constraints. To this end, the screw-based dynamics using the Newton–Euler iterative method, with a global and unique attitude representation, are established; then, a chattering-free fixed-time sliding-mode control (FTSMC) scheme is developed based on the proposed dynamics model. A radial basis function neural network (RBFNN) with proper hidden layers and nodes is integrated into the proposed controller, while modeling approximation errors are well-inhibited by adaptive laws. Since the tracking error has been explicitly constrained by an exponential decay function, the Lyapunov analysis indicates that the transient and steady performance and state constraints can be guaranteed simultaneously. Simulation experiments taking an FFSM with six-degree-of-freedom (DOF) illustrate the effectiveness of the proposed control scheme from two aspects: joint-space positioning and trajectory tracking.
The Onset of the Monsoon over the Bay of Bengal: The Year-to-Year Variations

Abstract In situ buoy observation data spanning four years (2008–2011) were used to demonstrate the year-to-year variations of the monsoon onset processes in the Bay of Bengal (BoB). A significant early (late) mon-soon onset event in 2009 (2010) was analyzed in detail. It is found that the year-to-year variations of monsoon onset can be attributed to either the interannual variability in the BoB SST or the irregular activities of the intra-seasonal oscillation (ISO). This finding raises concern over the potential difficulties in simulating or predicting the mon-soon onset in the BoB region. This uncertainty largely comes from the unsatisfactory model behavior at the intra-seasonal time scale.
Shedding light on neurofilament involvement in cognitive decline in obstructive sleep apnea and its possible role as a biomarker

Obstructive sleep apnea is one of the most common sleep disorders with a high estimated global prevalence and a large number of associated comorbidities in general as well as specific neuropsychiatric complications such as cognitive impairment. The complex pathogenesis and effects of the disorder including chronic intermittent hypoxia and sleep fragmentation may lead to enhanced neuronal damage, thereby contributing to neuropsychiatric pathologies. Obstructive sleep apnea has been described as an independent risk factor for several neurodegenerative diseases, including Alzheimer's disease and all-cause dementia. The influence of obstructive sleep apnea on cognitive deficits is still a topic of recent debate, and several mechanisms, including neurodegeneration and depression-related cognitive dysfunction, underlying this correlation are taken into consideration. The differentiation between both pathomechanisms of cognitive impairment in obstructive sleep apnea is a complex clinical issue, requiring the use of multiple and costly diagnostic methods. The studies conducted on neuroprotection biomarkers, such as brain-derived neurotrophic factors and neurofilaments, are recently gaining ground in the topic of cognition assessment in obstructive sleep apnea patients. Neurofilaments as neuron-specific cytoskeletal proteins could be useful non-invasive indicators of brain conditions and neurodegeneration, which already are observed in many neurological diseases leading to cognitive deficits. Additionally, neurofilaments play an important role as a biomarker in other sleep disorders such as insomnia. Thus, this review summarizes the current knowledge on the involvement of neurofilaments in cognitive decline and neurodegeneration in obstructive sleep apnea patients as well as discusses its possible role as a biomarker of these changes.
Characterization of the damage and its effect on the production of pitayas Stenocereus pruinosus and S. stellatus under different forms of management in Central Mexico

The use of several columnar cacti stretches back several thousand years and is still going at the present. In many of them, their populations within their range of distribution, are subject to three forms of management, including wild, in situ, and cultivated, each of one exposed to different types of damage, biological or physical. This study attempted to characterize the types of damage affecting pitayas (Stenocereus pruinosus and S. stellatus) in central and southern Mexico, and to analyze the relationship between some types of damage under different forms of management, and its effect on reproductive success. The two species were sampled in three populations with different forms of management. The branches (167-180) were selected and measured the amount of damage, characterized the types of damage, and a percentage of the total damage together with the number of fruits that were produced throughout their annual fruiting season were estimated. A x2 test was used to determine the variations in the characterized damage, and the effects of the damage on fruit production in populations with different forms of management was assessed using linear regressions. The eight different types of damage were listed, its frequency, and the effect of damage on fruit production varies in populations. Depending on human management, populations managed demonstrate acceptance of some types of damage by pitaya farmers: bird nests and external black scar, which do not affect the production of fruits; and reduction of others, branch rot and ant herbivory, the latter of which does affect the production of fruits. The fisheye disease, anthropogenic, gray scab and sooty mold and branch rot damage should be addressed in plant health activities in all populations, and the exchange of knowledge with pitaya farmers should be developed to propose measures to attend to these damages. This will support the sustainable use of the species.
Unsupervised VSP Up- and Downgoing Wavefield Separation via Dual Convolutional Autoencoders

Vertical seismic profiling (VSP) is widely applied in the field of seismic exploration to deliver high-quality subsurface images and enable quantitative characterization around the wellbore region. The separation of VSP upgoing and downgoing wavefields is a practical step in the wavefield processing, which sets the foundation for the final imaging quality. With recent advances in deep learning (DL), a number of new attempts in seismic signal processing have proven effective. Inspired by that, we propose an unsupervised VSP wavefield separation framework via dual convolutional autoencoders (dualCAEs). Our method is based on two characteristics of up- and downgoing wavefields: one is directional continuity, and the other is the zero-mean feature. Two regularizers are proposed to constrain these two features. Thanks to this, our method does not require any training data other than the input data itself. Ablation and comparison studies on synthetic data validate the effectiveness of our method. Generalization tests on SEAM open data and field VSP records in the Dong area show the superiority of robustness and fidelity over traditional <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> and median filtering methods.
SIMULATING FUTURE FOREST FRAGMENTATION IN A CONNECTICUT REGION UNDERGOING SUBURBANIZATION

Forests in the Northeast United States are becoming fragmented as a result of suburban development. Fragmentation negatively impacts a forest’s ecological quality and its value as a timber resource. This study developed a methodology to simulate suburban development over the next three decades. The impact of the projected development was analyzed using a forest fragmentation model. Maps depicting land suitability for development were created, for each town in the study area, using Multi-Criteria Evaluation (MCE) techniques developed in ArcGIS. MCEs were based on soil types, distance from roads, and constraints specified in the town zoning regulations. The Buildout tool in CommunityViz’s Scenario360 extension for ArcGIS was used to populate each town with buildings according to the town zoning regulations. Scenario 360’s TimeScope tool was used to assign a build year to each potential future building based on land suitability. Building locations with higher suitability were assigned earlier build years. Square buffers were created at each building location to represent areas in which landcover change would potentially occur. The buffers were used to modify a 2002 Landsat-derived landcover map to depict landcover for several future dates. A forest fragmentation model was applied to the modified landcover maps to quantify the states of forest fragmentation. The simulation predicts a 3 percent loss of forest cover, in the study area, over the next 30 years. Unfragmented forest area is expected to decline more rapidly than the total forest cover with interior forest losses of 27.7 percent predicted by 2036.
The macroscopic charcoal record in forested boreal peatlands in southeast Norway

We present a spatial and quantitative analysis of the macroscopic charcoal record in 11 forested peat basins in a boreal forest landscape in southeast Norway. The areas of the basins ranged from 200 to 6400 m2 and our study is based on 247 peat sequences that were sampled from randomly selected positions. In total we found 1228 charcoal layers in these peat sequences, which altogether had a length of 551 m. The number of charcoal layers per sequence ranged from 0 to 31 and the average number of charcoal layers per sequence was 3.99±0.3 SE, giving a mean fire frequency of one per 475 years. The average charcoal layer contained 170 charcoal particles > 0.28 mm per 10 cm2, of which 30 were > 1 mm. Peat sequences that totally lacked macroscopic charcoal were found at all sites. Nonetheless, all sites have burned recurrently during the Holocene, since at least one sequence from each site had seven or more charcoal layers. Neither the number of layers nor particle size decreased with increasing distance to the surrounding forests, indicating an in situ charcoal production and deposition. The charcoal record was characterized by a striking spatial variability. Geostatistical analyses revealed a strong spatial structuring on fine scales, ie, c. 0.25-1.5 m, but there was no evidence for spatial structuring at between-site scales, demonstrating that the sites have idiosyncratic fire histories. The rate of charcoal deposition was greater for the period prior to establishment of Picea abies than for the post-establishment period.
Investigating the Inhibitory Effect of Lactic Acid on Biofilm Production by Raw Chicken Meat Campylobacter spp. Isolates in Pure and Mixed Cultures

: Campylobacter spp. are the main cause of foodborne gastroenteritis worldwide, and the bioﬁlm growth mode seems to play a key role in their prevalence. In this work, the effect of lactic acid (LA) on planktonic growth and bioﬁlm production by eight Campylobacter spp. raw chicken meat isolates was investigated using polystyrene and stainless steel as the abiotic substrata. Results revealed that the minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and minimum bioﬁlm inhibitory concentration (MBIC) values of LA against the Campylobacter isolates ranged from 1024 to 4096 µ g/mL depending on the isolate, mode of growth (planktonic vs. bioﬁlm; single vs. mixed culture), and the growth medium. Overall, the results of this work offer insights into the bioﬁlm control of a pathogen of public health importance.
Differential Development of Two Palsa Fields in a Peatland Located Near Whapmagoostui-Kuujjuarapik, Northern Québec, Canada

Abstract Macrofossil analysis of the peat and topographic surveys of two palsa fields (Fields 3 and 4) within a permafrost peatland located in subarctic Québec was conducted to trace the factors that contributed to their differential development. The two palsa fields are visibly different in terms of their geomorphology, ecology, and hydrology. According to our results, the evolution of the two fields was largely synchronous in terms of the respective trophic conditions. Moreover, the climate certainly played a significant role in the evolution of this ecosystem. For example, the cooling of the Neoglacial period would have favored the ombrotrophication of both sites, whereas the Little Ice Age conditions would have contributed to palsa formation. Despite the synchronous changes within the two fields, significant differences were also noted. These include the rate of peat accumulation, the number of species found in the peat monoliths, and the presence or absence of forest cover during the ombrotrophic phase. The topography of the underlying substrate would also have influenced the hydrological conditions. For example, a light slope toward the northwest engendered a faster trophic impoverishment in Field 4. In addition, streaming water in Field 3 favored the preservation of wet and rich conditions that triggered the following changes: more diverse vegetation, the significant accumulation of peat, and the formation of higher palsas.
Cross-subject emotion EEG signal recognition based on source microstate analysis

Electroencephalogram (EEG) signals are very weak and have low spatial resolution, which has led to less satisfactory accuracy in cross-subject EEG-based emotion classification studies. Microstate analyses of EEG sources can be performed to determine the important spatiotemporal characteristics of EEG signals. Such analyses can be used to cluster rapidly changing EEG signals into multiple brain prototype topographies, fully utilizing the spatial information contained in the EEG signals and providing a neural representation for emotional dynamics. To better utilize the spatial information of brain signals, source localization analysis on the EEG signals was first conducted. Then, a microstate analysis on the source-reconstructed EEG signals is conducted to extract the microstate features of the data. We conducted source microstate analysis on the participant data from the odor-video physiological signal database (OVPD-II) dataset. The experimental results show that the source microstate feature topologies of different participants under the same emotion exhibited a high degree of correlation, which was proven by the analysis of microstate feature topographic maps and the comparison of two-dimensional feature visualization maps of the differential entropy (DE) and power spectral density (PSD). The microstate features represent more abstract emotional information and are more robust. The extracted microstate features were then used with the style transfer mapping method to transfer the feature data from the source domain to the target domain and were then used in support vector machines (SVMs) and convolutional neural networks (CNNs) for emotion recognition. The experimental results show that the cross-subject classification accuracies of the microstate features in SVMs were 84.90 ± 8.24% and 87.43 ± 7.54%, which were 7.19 and 6.95% higher than those obtained with the PSD and 0.51 and 1.79% higher than those obtained with the DE features. In CNN, the average cross-subject classification accuracies of the microstate features were 86.44 and 91.49%, which were 7.71 and 19.41% higher than those obtained with the PSD and 2.7 and 11.76% higher than those obtained with the DE features.
Multistakeholder Service Design Framework for Design of Patient Care Pathways - A case of joint management of pain patients in the health, labour and welfare services in Norway

Offering pain management services is a cohesive effort involving several stakeholders such as health, labour and welfare services. Globally, efforts are being made to develop streamlined patient care pathways, but there remains a specific need to improve and build the health care delivery services, for pain prevention, assessment, treatment, management, and follow-up. This raises the need for customised service design frameworks that provide guidance to both designers and healthcare providers in systematically involving multiple stakeholders to design patient-centered health care pathways. This paper presents a human-centered design case of service design for joint management of pain patients in the health, labour, and welfare services in Norway. This paper contributes directly to both the service design literature and health practice by proposing a multistakeholder service design framework. Paper also highlights strategic challenges and proposed solutions in the ongoing efforts in planning new national patient care pathways
Autoethnographic study of applying service journeys to asylum applications

Just as the COVID-19 pandemic was starting to fade, the Russia–Ukraine conflict erupted into a large-scale war. This caused a new influx of refugees to many European countries. In this case study, through an autoethnographic service journey, we investigate the process of applying for asylum in Norway. We aim to understand the parts of the journey that work well and the issues that still require attention. By analysing the data collected, we can see that there are two main underlying issues, which are creating transparency and building trust between the applicant and the service system involved in asylum seeking processes. We noted that in this individual case, trust was constructed by the extent to which the officials treated the applicant as an equal. Making the entire asylum application journey visible from the first step is one way to find approaches to improve this process in the future.
Network Traffic Prediction with Reduced Power Consumption towards Green Cellular Networks

The increased number of cellular network subscribers is giving rise to the network densification in next generation networks further increasing the greenhouse gas emission and the operational cost of network. Such issues have ignited a keen interest in the deployment of energy-efficient communication technologies rather than modifying the infrastructure of cellular networks. In cellular network largest portion of the power is consumed at the Base stations (BSs). Hence application of energy saving techniques at the BS will help reduce the power consumption of the cellular network further enhancing the energy efficiency (EE) of the network. As a result, BS sleep/wake-up techniques may significantly enhance cellular networks' energy efficiency. In the proposed work traffic and interference aware BS sleeping technique is proposed with an aim of reducing the power consumption of network while offering the desired Quality of Service (QoS) to the users. To implement the BS sleep modes in an efficient manner the prediction of network traffic load is carried out for future time slots. The Long Short term Memory model is used for prediction of network traffic load. Simulation results show that the proposed system provides significant reduction in power consumption as compared with the existing techniques while assuring the QoS requirements. With the proposed system the power saving is enhanced by approximately 2% when compared with the existing techniques. His proposed system will help in establishing green communication networks with reduced energy and power consumption.