Initial Map Research Articles

The Growth and Development of a Research Division in the Plastic Surgery Department of a Large Urban Academic Medical Center.

Research is a critical component of academic medicine that may or may not be prioritized in centers with high clinical volumes. The benefits of research expansion go beyond notoriety and industry partnerships, expanding into resident training and preparation of the next generation of physician-scientists. Improving a division or department's research portfolio requires a commitment to reorganizing structure, personnel, resources, and a dedication to innovative funding models. To improve research productivity and quality, our group placed several initiatives into motion beginning in August 2017 that we have outlined and evaluated in the present study. Some of these initiatives included restructuring leadership, resourcing both bench and clinical outcomes research, providing initial funding directly from clinical profits and rewarding research fiscally. Reviews of hiring records, publications, grant allocations, and interviews with key personnel were used to generate a road map of initiatives. Average impact factor was calculated by averaging journal impact factors for all publications from the department each year, excluding any publications with greater than 5 times the raw average, and creating a corrected average that more accurately represented the work. Student t tests were used to compare mean number of publications and impact factors from 2010 to 2017 to those from 2018 to 2022. Prior to restructuring (2010-2017), the department published an average of 9 articles annually, which increased to an average of 42 articles since that time (P < 0.01). Average impact increased from 0 in 2010 to 4.02 in 2022, with the number of publications in top 10 plastic surgery journals following a similar trajectory with 1 publication in 2010 and 31 in 2023. Following an initial $1 million investment to create an institutionally directed fund in 2018, the department leveraged its research to earn $3 million in endowments, $1.25 million in industry partnerships, $3.23 million in Department of Defense funding, and $1.65 million from a multi-institutional National Institutes of Health grant. Deliberate prioritization of research initiatives as noted above has led to remarkable growth in academic output.

Local Development Based on Non-Timber Forest Products: Evidence from a Mapping of Initiatives in the Brazilian Amazon since Rio 1992

Over recent decades, the sustainable use of non-timber forest products (NTFPs) has offered the promise of reconciling the dilemma between forest conservation and economic development, generating income for local populations. Accordingly, since the 1990s, manifold initiatives, often supported by international donors, have been investing in developing such NTFP value chains. Specifically in the Brazilian context, initiatives have proliferated as a response to the challenges of the largest remaining tropical forest ecosystem—the Amazon region and its inhabitants. Have they worked? What do we know about their impact? To provide for the first time an adequate basis for a more systematic analysis of the impact of NTFP initiatives in the Brazilian Amazon, this article presents a mapping of current and past initiatives supporting NTFP extractivism. The mapping drew on databases of funding organizations and interviews with key informants. The analysis revealed a large number of initiatives (nearly 200) strongly clustered in some areas, like around Belém, along major roads such as the BR 364, and along the Juruá rivers, concentrated on a few products with relatively high commercial value, mostly implemented by civil society organizations and targeting traditional peoples and communities. As interventions for NTFP development, the initiatives offered capacity building, financial support, and facilitate market access. With regard to the latter, the initiatives varied considerably. The vast majority promoted products, while a much smaller number engaged in company partnerships or government procurement. Despite the large number of initiatives, there is insufficient knowledge to gauge the impacts of different strategies of local resources and their users. A clear understanding of the effects of NTFP-based development initiatives and how impacts are generated is essential to seize the potential of the bioeconomy for achieving sustainable development for the Brazilian Amazon.

Hyperspectral Anomaly Detection Using Reconstruction Fusion of Quaternion Frequency Domain Analysis.

Most existing techniques consider hyperspectral anomaly detection (HAD) as background modeling and anomaly search problems in the spatial domain. In this article, we model the background in the frequency domain and treat anomaly detection as a frequency-domain analysis problem. We illustrate that spikes in the amplitude spectrum correspond to the background, and a Gaussian low-pass filter performing on the amplitude spectrum is equivalent to an anomaly detector. The initial anomaly detection map is obtained by the reconstruction with the filtered amplitude and the raw phase spectrum. To further suppress the nonanomaly high-frequency detailed information, we illustrate that the phase spectrum is critical information to perceive the spatial saliency of anomalies. The saliency-aware map obtained by phase-only reconstruction (POR) is used to enhance the initial anomaly map, which realizes a significant improvement in background suppression. In addition to the standard Fourier transform (FT), we adopt the quaternion FT (QFT) for conducting multiscale and multifeature processing in a parallel way, to obtain the frequency domain representation of the hyperspectral images (HSIs). This helps with robust detection performance. Experimental results on four real HSIs validate the remarkable detection performance and excellent time efficiency of our proposed approach when compared to some state-of-the-art anomaly detection methods.

Landslide susceptibility prediction modelling based on semi‐supervised XGBoost model

In the process of landslide susceptibility prediction (LSP) modelling, there are some problems in the model dataset relating to landslide and non‐landslide samples, such as landslide sample errors, subjective randomness and low accuracy of non‐landslide sample selection. In order to solve the above problems, a semi‐supervised machine learning model for LSP is innovatively proposed. Firstly, Yanchang County of Shanxi Province, China, is taken as the study area. Secondly, the frequency ratio values of 12 environmental factors (elevation, slope, aspect, etc.) and the randomly selected twice non‐landslides are used to form the initial model datasets. Thirdly, an extreme gradient boosting (XGBoost) model is adopted for training and testing the initial datasets, so as to produce initial landslide susceptibility maps (LSMs) which are divided into very low, low, moderate, high and very high susceptibility levels. Next, the landslide samples in initial LSMs with very low and low susceptibility levels are excluded to improve the accuracy of landslide samples, and the unlabelled twice non‐landslide samples in initial LSMs with low and very low susceptibility levels are randomly selected to ensure the accuracy of non‐landslide samples. These new obtained landslide and non‐landslide samples are reimported into XGBoost model to construct the semi‐supervised XGBoost (SSXGBoost) model. Finally, accuracy, kappa coefficient and statistical indexes of susceptibility indexes are adopted to assess the LSP performance of XGBoost and SSXGBoost models. Results show that SSXGBoost model has remarkably better LSP performance than that of XGBoost model. Conclusively, the proposed SSXGBoost model effectively overcomes the problems that the accuracy of landslide samples needs to be further improved and that non‐landslide samples are difficult to select accurately.

Atypical Pre-excitation Pattern in Asymptomatic Wolff-Parkinson-White—A Hallmark for High Risk?

Abstract Introduction Delta wave morphology during sinus rhythm on 12 lead ECG usually allows for a relatively precise prediction of the anatomic location of an accessory pathway (AP) provided that the rhythm is fully pre-excited. However, the pre-excitation pattern during sinus rhythm usually offers little information about sudden cardiac death (SCD) risk in adult patients with asymptomatic Wolf-Parkinson-White (WPW). Case Presentation A 42-year-old policeman was referred for an asymptomatic WPW pattern with an unusual delta wave. Initial mapping showed that atrio-ventricular (A-V) fusion on the lateral mitral annulus and radiofrequency (RF) ablation induced a shift in the pre-excitation pattern. Re-mapping identified A-V fusion on the left postero-septal area. Another RF application eliminated pre-excitation. Re-mapping identified A-V fusion on the postero-septal aspect of the mitral annulus and another RF application almost instantaneously induced an A-V split and loss of pre-excitation. Post-ablation testing proved normal anterograde A-V conduction without pre-excitation and no ventriculo-atrial conduction. Serial ECGs proved the persistent loss of pre-excitation at one-month of follow-up. Conclusion This case highlights the diagnostic challenges associated with atypical pre-excitation patterns and the importance of a thorough evaluation in asymptomatic individuals. Patients with atypical pre-excitation may harbor multiple accessory pathways (MAPs) and should be carefully assessed to mitigate the risk of SCD.

Adaptive convolutional sparsity with sub-band correlation in the NSCT domain for MRI image fusion

Objective. Multimodal medical image fusion (MMIF) technologies merges diverse medical images with rich information, boosting diagnostic efficiency and accuracy. Due to global optimization and single-valued nature, convolutional sparse representation (CSR) outshines the standard sparse representation (SR) in significance. By addressing the challenges of sensitivity to highly redundant dictionaries and robustness to misregistration, an adaptive convolutional sparsity scheme with measurement of the sub-band correlation in the non-subsampled contourlet transform (NSCT) domain is proposed for MMIF. Approach. The fusion scheme incorporates four main components: image decomposition into two scales, fusion of detail layers, fusion of base layers, and reconstruction of the two scales. We solved a Tikhonov regularization optimization problem with source images to obtain the base and detail layers. Then, after CSR processing, detail layers were sparsely decomposed using pre-trained dictionary filters for initial coefficient maps. NSCT domain’s sub-band correlation was used to refine fusion coefficient maps, and sparse reconstruction produced the fused detail layer. Meanwhile, base layers were fused using averaging. The final fused image was obtained via two-scale reconstruction. Main results. Experimental validation of clinical image sets revealed that the proposed fusion scheme can not only effectively eliminate the interference of partial misregistration, but also outperform the representative state-of-the-art fusion schemes in the preservation of structural and textural details according to subjective visual evaluations and objective quality evaluations. Significance. The proposed fusion scheme is competitive due to its low-redundancy dictionary, robustness to misregistration, and better fusion performance. This is achieved by training the dictionary with minimal samples through CSR to adaptively preserve overcompleteness for detail layers, and constructing fusion activity level with sub-band correlation in the NSCT domain to maintain CSR attributes. Additionally, ordering the NSCT for reverse sparse representation further enhances sub-band correlation to promote the preservation of structural and textural details.

Machine learning-based field geological mapping: A new exploration of geological survey data acquisition strategy

Geological mapping, as the fundamental core of geological research and investigation, provides indispensable basic understanding and exploration data for mineral prospecting, disaster prevention and control, and other related fields. The emergence of big data algorithms and models, such as machine learning and deep learning, has brought new assistance to the task of geological mapping. By utilizing these advanced technologies, long-term accumulated exploratory data has been deeply mined and analyzed, significantly enriching the information obtained through conventional mapping methods and laying a solid foundation for subsequent research. However, challenges faced by machine learning in the field of geological mapping are gradually being recognized: the vast demand for data volume, interference from irrelevant information, the “black box” problem, limitations in computing power, algorithm applicability, and data security, among others. To address these challenges, this study takes the Duolong mineral district, Tibet, China, a region with a high degree of exploration, as an example, assuming it to be a blank area without mapping, and attempts to integrate the light gradient boosting machine (LightGBM) algorithm into the field geological mapping process. Firstly, based on remote sensing data, the distribution of alteration was identified to design the initial mapping route. Subsequently, geological units along the route were labeled, and the model was trained using geochemical sampling point data and remote sensing data as feature inputs. During the model prediction stage, the probability distribution obtained through the Softmax function was utilized to guide the subsequent design and planning of field mapping routes. After five iterations, based on field mapping that covers 20% of the entire area, 90% of the lithological units were successfully predicted. This study explores an effective combination of machine learning algorithms with field geological mapping that establishes a new method for field geological mapping based on machine learning. It not only improves the efficiency and accuracy of mapping but also provides a new strategy for balancing geological work with structured data acquisition.

Full-automatic high-precision scene 3D reconstruction method with water-area intelligent complementation and mesh optimization for UAV images

ABSTRACT Fast and high-precision urban scene 3D modeling is the foundational data infrastructure for the digital earth and smart cities. However, due to challenges such as water-area matching difficulties and issues like data redundancy and insufficient observations, existing full-automatic 3D modeling methods often result in water-area missing and many small holes in the models and insufficient local-model accuracy. To overcome these challenges, full-automatic high-precision scene 3D reconstruction method with water-area intelligent complementation on depth maps and mesh optimization is proposed. Firstly, SfM was used to calculated image poses and PatchMatch was used to generated initial depth maps. Secondly, a simplified GAN extracted water-area masks and ray tracing was used achieve high-precision auto-completed water-area depth values. Thirdly, fully connected CRF optimized water-areas and arounds in depth maps. Fourthly, high-precision 3D point clouds were obtained using depth map fusion based on clustering culling and depth least squares. Then, mesh was generated and optimized using similarity measurement and vertex gradients to obtain refined mesh. Finally, high-precision scene 3D models without water-area missing or holes were generated. The results showed that: to compare with the-state-of-art ContextCapture, the proposed method enhances model completeness by 14.3%, raises average accuracy by 14.5% and improves processing efficiency by 63.6%.

MISNet: multi-source information-shared EEG emotion recognition network with two-stream structure.

When constructing machine learning and deep neural networks, the domain shift problem on different subjects complicates the subject independent electroencephalography (EEG) emotion recognition. Most of the existing domain adaptation methods either treat all source domains as equivalent or train source-specific learners directly, misleading the network to acquire unreasonable transfer knowledge and thus resulting in negative transfer. This paper incorporates the individual difference and group commonality of distinct domains and proposes a multi-source information-shared network (MISNet) to enhance the performance of subject independent EEG emotion recognition models. The network stability is enhanced by employing a two-stream training structure with loop iteration strategy to alleviate outlier sources confusing the model. Additionally, we design two auxiliary loss functions for aligning the marginal distributions of domain-specific and domain shared features, and then optimize the convergence process by constraining gradient penalty on these auxiliary loss functions. Furthermore, the pre-training strategy is also proposed to ensure that the initial mapping of shared encoder contains sufficient emotional information. We evaluate the proposed MISNet to ascertain the impact of several hyper-parameters on the domain adaptation capability of network. The ablation experiments are conducted on two publically accessible datasets SEED and SEED-IV to assess the effectiveness of each loss function. The experimental results demonstrate that by disentangling private and shared emotional characteristics from differential entropy features of EEG signals, the proposed MISNet can gain robust subject independent performance and strong domain adaptability.

How is Portugal addressing disinformation? Results of a mapping of initiatives (2010-2023)

The brisky transformation of the internet and the rise of digital technologies have been causing a deep impact on the media and in the ways audiences access, consume and participate. Although in this era information is closer and more available to citizens, concerns have been raised regarding 1) the quality of the information disseminated by digital platforms, 2) the impacts it may have on democracies and 3) the urgency of promoting citizens' MIL competencies. Disseminating unverified content can pose threats towards the status of the Internet as a space for free and informative debate, and as a tool for social inclusion. Portugal is pointed out as the second European country where citizens are most concerned about authenticity and validity of information (Newman et al., 2020). However data also shows that mechanisms of disinformation are quite participated by them. In this way, it becomes relevant to understand how the various sectors of society have been promoting the empowerment of citizens, and their resilience and competence toward information disorder.&#x0D; Within the scope of the project Read Twice, the team carried out a mapping of initiatives focused on tackling disinformation. The aim was to identify good practices and outline Portugal’s experience concerning this issue. After an initial listing of activities (N=20) promoted over the last 13 years, the team identified 3 Top Initiatives and eleven Best Practices. Overall results suggest that media literacy has been in the spotlight in the Portuguese context since the 1980s. There is also a tendency for media literacy initiatives to engage target audiences through active, participatory and creative approaches. However, and although the longest-running activity dates from the late 1980s, the initiatives identified are limited to a specific timeframe, suggesting that there is a lack of continuity repeatedly emphasised in the research.

Effects of intercropping on soil greenhouse gas emissions - A global meta-analysis

Diversified cropping systems, such as intercropping, have shown multifunctionality in agronomic productivity promotion, pest control, and soil health improvement. However, the intense interaction between crop species stimulates soil carbon and nitrogen turnover, and intercropping systems cause inexplicit effects on soil greenhouse gas emissions (GHG). Therefore, a comprehensive meta-analysis using 52 published articles (531 paired observations) was conducted to elucidate the effects of intercropping on soil N2O, CO2, and CH4 emissions under different environmental conditions and field practices to identify the primary driving factors, such as climate, soil and field practices. The results showed that intercropping treatment had a non-significant impact on the three GHG emissions on average. However, using a cereal-legume intercropping regime, adopting moderate N application rate or intercropping in alkaline soils could significantly mitigate soil N2O emission. Additionally, intercropping in soils with high soil organic carbon reduce soil CH4 emission. On the contrary, increasing intercropping duration, or adopted in soils with moderate soil total N tended to stimulate CO2 emission. The mixed-effect model selection indicated that initial soil pH, MAP, MAT, tillage regime, and intercropping duration and type were significant moderators in regulating soil GHG emissions. Our findings explicitly elucidated soil GHG responses to intercropping practice. Further studies are warranted on the evaluation of long-term intercropping effects to improve the comprehensive understanding of C and N balance and global warming potential under intercropping.

Anatomically Based Ablation of Left Ventricular Summit Premature Ventricular Complexes Guided by Intracardiac Echocardiography.

Catheter ablation of premature ventricular contractions (PVCs) arising from the left ventricular summit (LVS) presents technical challenges due to the regional anatomy and frequent intramural site of origin (SOO). Intracardiac echocardiography (ICE) and the CARTOSOUND® (Biosense Webster, Diamond Bar, CA, USA) module allow the operator to directly reconstruct and visualize the dimensions and orientation of the LVS live and present it in relation to neighboring structures. We retrospectively reviewed consecutive cases between January 2021 and December 2022 of patients undergoing PVC ablation for a presumed LVS origin. The LVS was reconstructed by creating a three-dimensional representation of the left ventricular septum, using two-dimensional ICE sections. The earliest site in each chamber was tagged on the reconstructed LVS, and the presumed SOO was localized using a geometrical center point from all sites. Ablation was first delivered to the earliest site, except when the presence of coronary branches precluded radiofrequency delivery within the great cardiac vein. Of 20 patients (8 women, 62.4 ± 7.1 years old) with a presumed LVS origin, 12 had PVC recurrence within the monitoring period after the initial ablation for 192.5 ± 37.2 s at the earliest site. Among them, earliest activation was seen at the sinus of Valsalva (SoV), coronary venous system (CVS), and left ventricular endocardium (LVE) in four, six, and two patients, respectively. Using the reconstructed LVS, the anatomically closest site to the SOO was identified in the SoV, CVS, and LVE in four, two, and six cases, respectively. Throughout the study period (14.5 months; range, 9.3-19.7 months), 17 patients (85%) had complete elimination of PVCs as evaluated by 24-h event monitors at the 12-month visit. In 50% of cases, among patients in whom ablation at the earliest signal was unsuccessful, the site of successful ablation did not correlate with the second earliest signal or had no identifiable signal during initial activation mapping. The reconstructed LVS not only guided activation mapping but also identified sites proximal to the center point that had either a late activation signal, a low-amplitude signal, or no signal at all.

Disparity Refinement for Stereo Matching of High-Resolution Remote Sensing Images Based on GIS Data

With the emergence of the Smart City concept, the rapid advancement of urban three-dimensional (3D) reconstruction becomes imperative. While current developments in the field of 3D reconstruction have enabled the generation of 3D products such as Digital Surface Models (DSM), challenges persist in accurately reconstructing shadows, handling occlusions, and addressing low-texture areas in very-high-resolution remote sensing images. These challenges often lead to difficulties in calculating satisfactory disparity maps using existing stereo matching methods, thereby reducing the accuracy of 3D reconstruction. This issue is particularly pronounced in urban scenes, which contain numerous super high-rise and densely distributed buildings, resulting in large disparity values and occluded regions in stereo image pairs, and further leading to a large number of mismatched points in the obtained disparity map. In response to these challenges, this paper proposes a method to refine the disparity in urban scenes based on open-source GIS data. First, we register the GIS data with the epipolar-rectified images since there always exists unignorable geolocation errors between them. Specifically, buildings with different heights present different offsets in GIS data registering; thus, we perform multi-modal matching for each building and merge them into the final building mask. Subsequently, a two-layer optimization process is applied to the initial disparity map based on the building mask, encompassing both global and local optimization. Finally, we perform a post-correction on the building facades to obtain the final refined disparity map that can be employed for high-precision 3D reconstruction. Experimental results on SuperView-1, GaoFen-7, and GeoEye satellite images show that the proposed method has the ability to correct the occluded and mismatched areas in the initial disparity map generated by both hand-crafted and deep-learning stereo matching methods. The DSM generated by the refined disparity reduces the average height error from 2.2 m to 1.6 m, which demonstrates superior performance compared with other disparity refinement methods. Furthermore, the proposed method is able to improve the integrity of the target structure and present steeper building facades and complete roofs, which are conducive to subsequent 3D model generation.

Socio-Sufism Practices within the Indonesia’s Tariqah Practitioners

Sufism activities in the teachings of tarekat have shown the socio practice of a Muslim. This study addresses the practice of dhikr by the community of Tarekat Khalwatiyah Sheikh Yusuf Al-Makassary (TKSYA) in improving daily life social personality. This study relies on qualitative research. Data were collected from observation, interviews, and literature review. The study results indicate that socio-sufism activities can build social congregation, which has implications for social life. It is implemented in the sharia or worship, tauhid or faith, and akhlak or ethics and muamalah as seen in everyday life. Sufism activists can interpret a complex life manifested through the nature of tawadhu; they respect each other, even towards their environment. Thus, the implication of dhikr continuously shapes community character and has a strong and calm personality. This study recommends further studies on the tarekat community with a contextual approach as an initial mapping of the potentiality of tarekat teachings in responding to the threat of an ongoing moral and social crisis.

Iran's comprehensive heat flow map generated by the Random Forest method and the Sequential Gaussian Simulation

The objective of this research is to present a comprehensive heat flow map for Iran by utilizing a novel approach that combines geostatistical distributions, machine learning algorithms, and deterministic calculations. In order to enhance the accuracy and quality of the initial geothermal map obtained from a recent study, a machine learning technique called the Random Forest method was employed in the mapping process, taking into consideration approximately 100 processed data points. To accomplish this, various geological features such as active faults, hot springs, geological domains, and hot spots were identified as effective attributes for generating a geology-based geothermal map. This geology-based map was then used as a trend to create a geostatistics-based geothermal gradient map. The sequential Gaussian co-simulation method was adopted for the geostatistical distribution, enabling the extension of the available geothermal gradient data points (which were calculated in a recent study) to cover the entire surface of Iran on a map. Consequently, an integrated geothermal gradient map was derived. Afterwards, thermal conductivity values corresponding to the prevalent lithology of each location in Iran were incorporated to calculate the heat flow values. This process resulted in the generation of a comprehensive heat flow map. The introduction of such an extensive map would greatly contribute to future geothermal-related explorations and complementing research in this field.

Occlusion Boundary Prediction and Transformer Based Depth-Map Refinement From Single Image

Due to the numerous applications of boundary maps and occlusion orientation maps (ORI-maps) in high-level vision problems, accurate estimation of these maps is a crucial task. The existing deep networks employ a single-stream network to estimate the relation between boundary map and ORI-map estimation. However, these networks fail to explore significant individual information separately. To resolve this problem, in this paper, we propose a novel two-stream generative adversarial network (GAN) for boundary map and ORI-map estimation, named OBP-GAN. The proposed OBP-GAN consists of two streams known as BP-GAN and OR-GAN. The BP-GAN estimates the boundary map, and the OR-GAN predicts the ORI-map. The boundary and ORI-map can also be useful cues for the task of depth-map refinement from single images. Therefore, in this work, we propose a transformer-based depth-map refinement network (TRANSDMR-GAN) for refining the depth estimated from monocular images using boundary and ORI-map. We conducted extensive analyses on indoor and outdoor datasets to validate our proposed OBP-GAN and TRANSDMR-GAN. The extensive experimental analysis and ablation study demonstrate the ability of the proposed OBP-GAN to generate state-of-the-art occlusion boundary maps. Furthermore, we show that the proposed network, TRANSDMR-GAN, can generate an edge-enhanced depth map without degrading the accuracy of the initial depth map.

DFNet-Trans: An end-to-end multibranching network for depth estimation for transparent objects

Transparent objects play a vital role in modern industries and find widespread applications across various engineering scenarios. However, capturing accurate depth maps of transparent objects remains challenging due to their reflective and refractive properties, which pose difficulties for most commercial-grade optical sensors. In this paper, we propose a novel depth estimation method called DFNet-Trans, designed to estimate depth from a noisy RGB-D image input. Initially, a multiscale feature fusion module (FFM) is incorporated into the existing depth estimation network to generate the initial depth map. Subsequently, we enhance the network by adding a confidence branch and a mask branch on the same encoder, enabling improved distortion correction and real scene restoration in the depth estimation. Based on the framework representation, missing depth can be completed. Comprehensive experiments demonstrate that the proposed approach significantly outperforms the current state-of-the-art methods on the recently popular large-scale real dataset TransCG. the proposed approach achieves a remarkable 27.7% reduction in RMSE and a notable 34.6% reduction in REL. The generalization experiment shows that the proposed approach outperforms existing methods when generalized to an unknown real dataset.

Development of a 30 m resolution global sand dune/sheet classification map (GSDS30) using multi-source remote sensing data

Accurate information of sand dune/sheet (SDS) spatial distribution is required by global-scale environmental assessment. However, the great spatio-temporal heterogeneity of SDS and the confusion of SDS to similar land cover types lead to variant feature representations and inadequate classification samples; thus, existing SDS mapping focuses on the regional scale, and the high-quality global SDS map is still lacking. In this study, we proposed a classification strategy and for the first time developed a 30 m resolution global SDS map (GSDS30) in the year 2017 that contained two SDS types, i.e., shifting SDS and semi-fixed/fixed SDS. The proposed strategy started by determining the initial mapping extent of SDS. Then, multi-source features were extracted to capture SDS variations and enlarge the separability of diverse land covers. Thirdly, we developed a prior-constraining K-nearest neighbor method to collect global SDS samples. Finally, a local random forest classifier was applied to generate classification results. The evaluation based on two validation sample sets showed good performance on the overall classification accuracy (88.73% and 87.92%), the Kappa coefficient (0.852 and 0.843), and the producer's and user's accuracies for two SDS types. Based on GSDS30, we found that global SDS occupied an area of 10.43 million km2, in which shifting SDS accounted for 60.41% and semi-fixed/fixed SDS accounted for the remaining 39.59%. The majority of SDS was distributed in Africa, Asia, and Oceania, where Australia had the greatest area of SDS. The combination of GSDS30 and population data showed that global SDS was inhabited by 14.5 million people, and 94.95% of them lived in semi-fixed/fixed SDS. This study is the first attempt to automatically produce global SDS map, which is not only necessary for representing the distribution of SDS, but also valuable in managing aeolian desertification and promoting sustainable development.

Deep Reinforcement Learning for Mapping Quantum Circuits to 2D Nearest‐Neighbor Architectures

AbstractRecently, quantum computing is considered as a promising future computing paradigm. However, when implementing quantum circuits on a quantum device, it is necessary to ensure that quantum circuits satisfy nearest‐neighbor architecture constraints. When performing nearest‐neighbor architecture mapping for quantum circuits, it is inevitable to introduce SWAP gates, which will increase the overhead and reduce the fidelity. Therefore, it is crucial to complete the mapping with the minimum SWAP. In this paper, a 2D nearest‐neighbor architecture mapping method for quantum circuits is proposed based on deep reinforcement learning. In the initial mapping, an isomorphic graph initial mapping search algorithm is designed to quickly find the isomorphic graph between quantum circuit and architecture constraint graph. For quantum circuits without isomorphic graph mapping, the reordering algorithm of qubit impact factors is designed to obtain the initial placement position of qubits. In the SWAP gate addition, a qubit local reordering algorithm based on dueling deep‐Q‐network is designed to reduce SWAP gates. Experiments on the benchmark set B131 and IBM Q20 Tokyo verify that the proposed method can add fewer SWAP gates. Compared with the state‐of‐the‐art method, the average running time is accelerated by 63.1%, and the average SWAP gates added are reduced by 24.7%.

POLA YANG DIUSULKAN DALAM MENDUKUNG KEBERLANJUTAN KEWIRAUSAHAAN SOSIAL

Social entrepreneurship is currently receiving attention in a number of countries, including Indonesia. The existence of social entrepreneurship can make a real contribution to improving the community's economy. This research was conducted in Deli Serdang district, North Sumatra with informants who were social entrepreneurs. From the initial map, information was obtained that in the absence of a brand, the need for support from an institution and differences in the behavior of social entrepreneurship actors in viewing institutional support. This research uses qualitative research with a phenomenological approach. The research results show a number of categories, memo fund themes which then produce patterns in supporting the desire for social entrepreneurial activities.