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This literature review emphasizes the importance of stakeholder-responsive success criteria and appropriate evaluation techniques for the construction industry. Traditional evaluation methods can lead to subjective and biased results. Disagreements between industry professionals and researchers on criteria and assessment techniques contribute to ineffective project evaluation. The review aims to identify the most important holistic and stakeholder-responsive success criteria and methods from the literature. The review utilized a systematic approach, downloading 102 references from various databases, including published journal articles, conference proceedings, books, and book sections, using search engines and keywords. The review process involved refining and limiting the number of references using eligibility criteria, with 55 references from 2001 to 2024 being used for review. The review flow chart outlines each step. The findings indicate five main evaluation criteria, such as efficiency, effectiveness, sustainability, environmental impact, and satisfaction. The five evaluation criteria include quality, cost, time, health and safety, environmental impact, stakeholder satisfaction, and lesson learning and development. Furthermore, the quantitative success evaluation method is the best technique due to its reduced subjectivity. The review provides valuable knowledge among industry stakeholders, professionals, academics, and policymakers on the most suitable project success evaluation criteria and methods to evaluate future construction projects. [ABSTRACT FROM AUTHOR]

Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed. [ABSTRACT FROM AUTHOR]

Dc microgrids (MG) have drawn more attention due to the demand for renewable energy sources, energy storage devices in distributed generation, and the separation of DC loads requiring more power converters. This system needed protection DC circuit breakers (DCCB) that could quickly isolate the faults in the system. These devices must be as small and light as possible but should only produce low power loss during the ON state. The impedance source DC circuit breaker was introduced as an improved structure (SSCB) which provided automatic turns off without needing external command due to its natural commutation operation principle. In addition, it had many advantages such as faster response, simpler control design, restricted fault current magnitude, which makes the system more fault-resilient, the fault current will not affect the source or the SCR, coordination of cascaded breakers is automatic, and convenience for protection with power converters. This paper presents different impedance source DC circuit breakers such as ZCB, CCB, TCB, and YCB. It will show their benefits and drawbacks, which could be used as a helping guide while choosing which type of impedance source DC circuit breakers is suitable for a specific application. [ABSTRACT FROM AUTHOR]

This paper introduces a compact, circularly polarized spidron-fractal slot antenna with a rectangular island. The reflection-coefficient |S11| of the proposed antenna covers the band from 7 GHz to 10 GHz. The proposed antenna is circularly polarized with an axial-ratio (AR) bandwidth that extends from 7 GHz to 10 GHz. It offers simultaneous dual circular polarization (RHCP and LHCP). The gain of the proposed antenna varies between 4 dBi and 6.9 dBi. The simulated efficiency reaches 97%. The overall dimensions of the antenna are 19.7 x 23 x 0.508 mm³ (0.55λ0 x 0.65λ0 x 0.014λ0), making it suitable for CubeSats with limited surface area. The proposed antenna can be employed for both the X-band satellite downlink (from 7.25 GHz to 7.75 GHz) and uplink (from 7.9 GHz to 8.4 GHz) frequency bands. Additionally, the antenna can be utilized in small satellite Synthetic-Aperture-Radar (SAR) systems operating from 9 GHz to 10 GHz, military applications, and RFID tag tracking-equipment. A prototype of the proposed antenna has been fabricated and then measured using Vector-Network-Analyzer (VNA) and inside an Anechoic chamber. The measurement results of the proposed antenna are in excellent match with the simulated results. [ABSTRACT FROM AUTHOR]

Copyright of European Journal of Psychotraumatology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of European Journal of Psychotraumatology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of European Journal of Psychotraumatology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Primary lateral sclerosis (PLS) is a rare, adult-onset and slowly progressive motor neuron disorder whose clinical core is characterized by upper motor neuron (UMN) dysfunction. Its formal diagnosis is clinically based and disease duration-dependent. Differentiating PLS from other disorders involving UMN can be challenging, particularly in the early stages. Our review covers and discusses different aspects of the PLS field, including the diagnostic criteria and its limitations, its differential diagnosis and their major pitfalls, and the actual role of neurophysiology, neuroimaging, genetics, and molecular biomarkers. Symptomatic treatment of the different manifestations is also addressed. The authors searched MEDLINE and Scopus. They also searched the reference lists of articles identified by our search strategy and reviewed and selected those deemed relevant. They selected papers and studies based on the quality of the report, significance of the findings, and on the author's critical appraise and expertise. It is important to investigate novel molecular biomarkers and plan multicenter clinical trials for PLS. However, this will require a large international project to recruit enough patients, particularly given the diagnostic uncertainty of the current clinical criteria. A better understanding of PLS pathophysiology is crucial for designing disease-targeted therapies. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate agricultural sustainability as a collective issue involving multiple rather than individual farms. Through the utilization of energy consumption as a proxy, we propose a novel methodology that evaluates the impact of farm consolidations on agricultural sustainability while accounting for resource preferences. Our approach incorporates the ordered weighted averaging (OWA) operator within an inverse data envelopment analysis (IDEA) model to identify post-merger farms that meet preset efficiency targets. We employ a DEA cross-efficiency (DEA-CE) procedure to select merger plans that maximize agricultural sustainability for each preference scenario. By analysing a case study of 43 tomato greenhouse farms in Biskra, northern Algeria, our findings demonstrate that mergers can significantly enhance agricultural sustainability, surpassing the potential of individual farms by a factor of over 15. Additionally, the adoption of the most sustainable merger plan can lead to energy savings of more than 69%. Irrespective of the preference scenario, substantial energy savings in machinery, fertilizers, diesel, and electricity ranging from 22.92% to 73.73% were observed. These results emphasize the strategic role of merger processes in promoting agricultural sustainability and optimizing resource utilization. [ABSTRACT FROM AUTHOR]

Product bundling is receiving increasing attention in sustainable agricultural development as a means to ensure access to and enhance the uptake of, agricultural technologies by smallholders. Yet, the how and why of bundling for smallholders are not well-understood. The current paper, therefore, brings bundling theory from the marketing literature to the smallholder context. We use a conjoint experiment, a proven marketing technique for designing new products, services and bundles, to design agricultural input and service bundles for soybean-producing smallholders in rural Ethiopia. The empirical findings from 252 smallholders suggest that product bundling enhances smallholders’ preferences and hence intentions to adopt technologies but that bundles must be designed carefully following a smallholder-centric approach. Drawing on our findings and the literature, we delineate the different steps that need to be taken to develop bundles for the successful uptake of new technologies by smallholders. [ABSTRACT FROM AUTHOR]

Pesticides are crucial for increasing agricultural productivity, but they have also been linked to a range of health and environmental risks. In this paper, we used nationally representative data from 557 agro-dealers in Uganda to assess the role of agro-dealer certification in improving knowledge and practices related to environmentally-friendly pest control. We found that almost half of the sampled agro-dealers were not certified or accredited by regulatory bodies, even though this is a prerequisite for selling pesticides in the country. Results further showed that only 16% of the agro-input shops were selling biopesticide products, largely due to a lack of awareness, access and demand from farmers. Regression results showed that certified agro-dealers were 9-12 percentage points more likely to know about biopesticides and integrated pest management, and 8-10 percentage points more likely to sell biopesticide products, compared to their non-certified counterparts. Our findings imply that agro-dealer certification courses can play an important role in raising knowledge and stimulating the supply of environmentally-benign pest control products. We identified regulatory enforcement, a decentralized certification system and agro-dealer associations as some of the potential pathways for incentivising compliance with certification requirements, thereby promoting lower-risk pest control products and strategies. [ABSTRACT FROM AUTHOR]

Goat raising has increased in Lao People's Democratic Republic due to meat demand from Vietnam. Goat production is low input using free grazing and minimal husbandry. Technical constraints are well known but socio-cultural contexts are poorly understood. This paper describes qualitative research on farmer's motivations, experiences and learning pathways, to improve goat husbandry. Semi-structured interviews were conducted with 30 smallholder goat farmers in Lao's South-Central province of Savannakhet. The interviews revealed that farmers relied on their experiences and observations of goat raising in their communities to make their management decisions. Trial and error was a valued learning strategy. Farmers took guidance from the exchange of goat raising experiences with other goat farmers and preferred participatory learning that fostered discussion in familiar village settings. Lao smallholder goat farming systems are a product of compromise across a number of resources. Project interventions that required low investments of labour, land or capital were most readily implemented and adapted by farmers. This study questions the assumption that project interventions are inherently optimal and beneficial for smallholder farming systems. Recommendations propose how farmer's experiences, constraints and preferences can be incorporated into development approaches to ensure management system changes reflect the desires of smallholder farmers. [ABSTRACT FROM AUTHOR]

As a typical tight sandstone gas reservoir in the Ordos Basin, the second member of the Shanxi Formation in the XQ gas field in the Ordos Basin has had rich gas reservoirs and good development effects in the past. On the basis of previous studies, this paper brings new understanding to the sedimentary model of this area. Nine kinds of single lithofacies and seven kinds of lithofacies combinations are determined. Braided fluvial fan deposits in distributive fluvial systems likely developed in this area. The study area is part of the middle subfacies of this sedimentary facies, and five kinds of sedimentary microfacies environments developed. Finally, establishing sedimentary model based on sedimentary characteristics. The braided fluvial fan sedimentary model based on DFS is of great significance for deepening the understanding of terrestrial sedimentary systems and providing guidance for the exploration and development of oil and gas bearing basins. [ABSTRACT FROM AUTHOR]

In this paper, we introduce general sequence of twice-iterated Δw -(degenerate) Gould–Hopper Appell polynomials (TI-DGHAP) via discrete Δw -Gould–Hopper Appell convolution. We obtain some of their characteristic properties such as explicit representation, determinantal representation, recurrence relation, lowering operator (LO), raising operator (RO), difference equation (DE), integro-partial lowering operator (IPLO), integro-partial raising operator (IPRO) and integro-partial difference equation (IPDE). As special cases of these general polynomials, we present TI degenerate Gould–Hopper Bernoulli polynomials, TI degenerate Gould–Hopper Poisson–Charlier polynomials, TI degenerate Gould–Hopper Boole polynomials and TI degenerate Gould–Hopper Poisson–Charlier–Boole polynomials. We also state their corresponding characteristic properties. [ABSTRACT FROM AUTHOR]

This paper presents a complete investigation into the phenomenon of dual convection flow within a bulging enclosure along with non-Newtonian power-law fluids and employs the Galerkin finite element method for simulation. The main objective is to study the heat and mass transfer and entropy generation within the enclosure. Various factors, such as the Rayleigh number, power law coefficient, Lewis number, and effects of magnetic inclination are evaluated for their influence on flow dynamics and heat distribution. The study also investigates the relationship between these parameters and entropy generation, providing insights into the irreversible processes within the system. A comparative analysis of averaged Nusselt and Sherwood coefficients reveals distinct thermal and fluidic behaviours across varying powerlaw indices, emphasizing differences between shear thickening, shear thinning, and Newtonian fluid behaviours. The findings provide valuable insights into non-Newtonian power-law fluids in complex flows, enhancing our understanding of flow and heat transfer in bulging enclosures. [ABSTRACT FROM AUTHOR]

This paper intended to study the mechanism and active ingredients of ACB anti-epilepsy. The antiepileptic activity of ACB validated in PTZ kindled rats, ACB could increase the seizure latency and reduce seizure duration, attenuate spatial learning and memory deficits, improve hippocampus neuronal damage and regulate unbalanced neurotransmitters. Furthermore, network pharmacology and molecular docking analysis predicted four potential active compounds, in addition, PI3K/Akt signal pathway may be the main signal pathway of ACB anti-epilepsy. In vitro, ACB greatly increased the vitality and reduced apoptosis of PC12 cells exposed to H2O2. Additionally, ACB elevated Bcl-2 and downregulated C-caspase-3 and Bax proteins expression. Importantly, ACB improved the phosphorylation of PI3K and Akt in H2O2-stimulated PC12 cells, and stimulated the nuclear transfer of Nrf2. These findings indicated that ACB has effective on antiepileptic by activating of the PI3K/Akt/Nrf2 pathway to reduce oxidative stress and neuronal cell apoptosis. [ABSTRACT FROM AUTHOR]

Higher-order nonlinear wave models have been a source of attraction to a huge number of researchers in recent times as a result of their significance in mathematical physics, other nonlinear sciences as well as engineering. In consequence, we outline in this paper the analytical studies entrenched on a generalized structure of a nonlinear breaking soliton equation with higher-order nonlinearity in four variables which have applications in science as well as engineering. Lie group theory is utilized to generate an 11-dimensional Lie algebra associated with the equation under consideration and in addition one parametric group of transformations related to the algebra is calculated. Besides, the technique is further invoked in performing reductions of the various subalgebras of the understudy model. Moreover, in conjunction with the theory, the direct integration technique is engaged to secure an analytic solution of the equation and as a result, a general analytic solution with regard to the second-kind elliptic-integral function is furnished. Moreover, we engage the novel simplest equation technique to gain more general solutions to the equation. In consequence, solitonic solutions comprising periodic, dark-bright, topological kink as well as singular solutions are achieved. We complemented that by exhibiting the dynamics of the secured solutions with the aid of graphical representations. In conclusion, we calculate conserved quantities associated with the aforementioned equation by invoking the well-celebrated classical Noether theorem technique. [ABSTRACT FROM AUTHOR]

In this paper, we explore local dynamics at equilibrium points, the existence of bifurcation sets and codimension-one bifurcation analysis of a discrete prey–predator system with Holling typeII functional response. Further, OGY and Hybrid control strategies are utilized to control chaos in the under study discrete model due to the occurrence of Neimark–Sacker and flip bifurcations. Finally, numerical simulations are given to verify theoretical results. [ABSTRACT FROM AUTHOR]

Control charts play a vital role in process monitoring to ensure the product’s desired standards. Due to rapid improvements in data collection methods, multivariate charts are preferred over univariate charts. This paper proposes a bivariate exponentially weighted moving average chart for the simultaneous monitoring of the mean vector of Gumbel’s bivariate Pareto type II (also known as the Lomax distribution) time-between-events data. The performance of the proposed chart is assessed through average run length, median run length, and the standard deviation of the run length criteria. To show the implementation of the chart in the real world, illustrative examples are also presented. [ABSTRACT FROM AUTHOR]

Polyhydroxyalkanoates (PHA), due to their remarkable biodegradability, biocompatibility, and favorable physical and chemical properties, are tested for applications in the agricultural, industrial, and medical fields in place of conventional plastic. Sustainable microbial PHA production is a challenge because of the economic aspects accrued due to the cost-effective substrates and downstream processing. Different approaches are adopted to circumvent the high economic price of PHA production by co-production, employing recombinant strain, mixed cultures, and low-cost feedstock. The review focuses on the co-production of intracellular microbial PHA with an extracellular value-added product. The present review highlights the feasibility of other bio-based products from the feedstock to offset the cost of PHA production with emphasis on another viable option. The paper discusses the recent progress in the simultaneous production of PHA with exopolysaccharides, pigments, enzymes, bio-surfactants, biofuels which primarily fulfill the criteria of higher yield of PHA, and another high-value product that can be achieved. This approach not only maximizes resource utilization but also provides opportunities for the development of a wide range of sustainable bio-based products to improve the overall feasibility of the process. [ABSTRACT FROM AUTHOR]

Accurately predicting the geometric structure of a building's roof as a vectorized representation from a raster image is a challenging task in building reconstruction. In this paper, we propose an efficient and precise parsing method called Roof-Former, based on a vision Transformer. Our method involves three steps: (1) Image encoder and edge node initialization, (2) Image feature fusion with an enhanced segmentation refinement branch, and (3) Edge filtering and structural reasoning. Our method outperforms previous works on the vectorizing world building dataset and the Enschede dataset, with vertex and edge heat map F1-scores increasing from 87.1%, 76.2% to 89.1%, 78.1%, and from 69.7%, 68.8% to 71.2%, 69.5%, respectively. Furthermore, our method demonstrates superior performance compared to the current state-of-the-art based on qualitative evaluations, indicating its effectiveness in extracting global image information while maintaining the consistency and topological validity of the roof structure. [ABSTRACT FROM AUTHOR]

The emergence of the Digital Twin of Earth (DTE) signifies a pivotal advancement in the field of the Digital Earth, which includes observations, simulations, and predictions regarding the state of the Earth system and its temporal evolution. While the Geospatial Data Cubes (GDCs) hold promise in probing the DTE across time, existing GDC approaches are not well-suited for real-time data ingestion and processing. This paper proposes the design and implementation of a real-time ingestion and processing approach in Geospatial Data Cube (RTGDC), achieved by ingesting real-time observation streams into data cubes. The methodology employs a publish/subscribe model for efficient observation ingestion. To optimize observation processing within the data cube, a distributed streaming computing framework is utilized in the implementation of the RTGDC. This approach significantly enhances the real-time capabilities of GDCs, providing an efficient solution that brings the realization of the DTE concept within closer reach. In RTGDC, two cases involving local and global scales were implemented, and their real-time performance was evaluated based on latency, throughput, and parallel efficiency. [ABSTRACT FROM AUTHOR]

With the development of social media, it has become increasingly important to quickly and accurately identify social media texts related to disasters (e.g. typhoon) to aid in rescue and recovery efforts. Currently, multi-class classification and pre-trained language model Bidirectional Encoder Representations from Transformers (BERT) are widely used for text classification. However, most studies on typhoon damage classification are multi-class single-label, which contradicts to the reality that a social media text may correspond to multiple types of damage. Moreover, the outputs of the hidden layers of BERT are not fully utilized. This paper proposes a two-stage multi-class multi-label classification method for typhoon damage assessment by fully integrating the outputs of the hidden layers of BERT. In the first stage, sentence vectors are adopted to identify typhoon damage-related texts. In the second stage, word matrices are applied for multi-class multi-label classification to further classify the texts into five damage categories (i.e. transportation, public, electricity, forestry, and waterlogging). The two stages are trained end-to-end to identify typhoon damage from social media texts. Experiments on SinaWeibo texts during typhoon landfall in Chinese coastal regions demonstrate that the proposed method can effectively improve the accuracy of text classification and comprehensively assess typhoon damage. [ABSTRACT FROM AUTHOR]

Mangroves are one of the important components of Earth's carbon sinks. The current problems of base-model composition strategy of ensemble learning and image features combination are still major challenges in mangrove species classification. This paper constructed two novel adaptive ensemble learning frameworks (AME-EL and AOS-EL) to explored the effect of combing different spatial-resolution optical and SAR images on classification performance, and evaluated the ability in mangrove species classification between dual-polarization and fullpolarization SAR images. Finally, we used the SHAP method to explore the effects of different feature interactions on mangrove species classification. The results indicated that: (1) AME-EL and AOS-EL achieve the fine classification of mangrove species with overall accuracies between 77.50% and 94.77%. (2) Combination of Gaofen-7 multispectral and Gaofen-3 SAR improved the classification accuracy for Kandelia candel, with the F1 score increasing from 26.4% to 40.2%. (3) The VV/VH polarization performed better in the classification, with the F1 scores for Aegiceras corniculatum and Kandelia candel were higher than those of HH/HV and AHV polarization by 7%-16.1% and 5.9%-16.1%, respectively. (4) SAR features interacted well with other spectral features, which made a strong contribution to the classification accuracy of mangrove species, and effectively affect the prediction results. [ABSTRACT FROM AUTHOR]

OpenStreetMap (OSM) has been playing increasingly important roles in location-based services, urban planning, tourism, and disaster management. In this paper, we examine a fundamental issue: can OSM reliably provide its services in the long run? To address this sustainable issue, we propose (1) inner cycle of career stages (i.e. activity levels) for monitoring the sustainable status of OSM and (2) critical mass theory for identifying driving factors that make OSM sustain. These new tools are used to track individual trajectories of editing behavior in 2005-2021. Our results suggest that OSM has been self-sustaining up to now and does not show a sign of decline yet. This is supported by the ever-decreasing turnover time and stabilized turnover rate. Together with the finding that most contributors transitioned to a higher career stage within a month, we identify an accelerated socialization process within OSM. We find further that it is the critical mass of people rather than content that makes OSM sustain. This contrasts with the common wisdom: recruiting more people with small contributions is far more important to the long-term growth of OSM than relying on a group of core members with large contributions. Implications and recommendations are critically discussed. [ABSTRACT FROM AUTHOR]

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, highprecision 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%. [ABSTRACT FROM AUTHOR]

It is very important to construct digital twin scenes, which can accurately describe the dynamically changing geographical environment and improve the level of refined management in bridge construction. This article proposes a feature constrained automatic diagnostic analysis method for geometric deformation of bridge digital twins. The geometric deformation feature library of bridge twins was first created to accurately describe structural relationships and behavior characteristics. Secondly, line surface feature constraints were used to extract geometric deformation information from bridge digital twins. Then, a geometric deformation diagnosis algorithm was designed based on an improved Hausdorff method. Finally, a case study was conducted to implement experimental analysis. The experimental results show that the method proposed in this paper can automatically extract the geometric morphology and rapidly calculate line and surface deformations for point cloud bridge digital twins. It achieves an efficiency improvement above 90% and with millimeter-level accuracy, which effectively enhances the diagnostic analysis capabilities for geographical digital twin models. [ABSTRACT FROM AUTHOR]

Road extraction from high-resolution remote sensing images (HRSI) is confronted with the challenge that roads are occluded by other objects, including opaque obstructions and similarly colored areas. This paper proposes a dual convolutional network based on hypergraph and multilevel feature fusion (DHM) for road extraction to address these challenges. The DHM consists of two branch networks (HGNN branch and CNN branch) and a bimodal feature fusion module (BFFM). In the HGNN branch, an algorithm is developed to exploit the shape features of roads and construct hypergraphs on the HRSI. Then, hypergraph neural networks are employed for the first time to capture the longrange context of roads to enhance road connectivity. In the CNN branch, a bottleneck fusion module integrated with an encoderdecoder network structure is built to aggregate multiscale local features. In BFFM, the long-range context from the HGNN branch and the local features from the CNN branch are fused through the designed position converter and enhanced graph reasoning module to achieve the complementary advantages of the dual-branch network. Extensive experiments on three datasets show that DHM outperforms other stateof- the-art methods, especially on the GS-Mountain road dataset. Furthermore, DHM significantly improves road extraction in occluded and similar road regions. [ABSTRACT FROM AUTHOR]

Earth Observation (EO) satellites have produced vast image collections that are freely accessible to society. However, handling these images often surpasses the capabilities of traditional hardware and software for EO data storage and processing, posing challenges for traditional Spatial Data Infrastructure (SDI). To overcome these challenges, innovative cloud computing and distributed systems have been developed, such as matrix databases, MapReduce systems, and web services. These technologies are now integrated into leading-edge platforms, forming a new generation of SDI for big EO data. These platforms have different characteristics in terms of governance, technologies, data access, infrastructure abstractions, data processing, and flexibility to extend their functionality. Our work contributes to the area of SDI for big EO data by proposing a server-side data-processing tool called Brazil Data Cube Workflow Engine (BDC-WE), based on workflow orchestration approach. BDC-WE provides a high-level interface using the openEO API for big EO data accessing and processing, allowing SDI maintainers to easily describe sequences of processes and integrate new algorithms. The architecture proposed in this study was implemented and the prototype was evaluated in two case studies described in this paper. [ABSTRACT FROM AUTHOR]

Burned area (BA) plays a pivotal role in fire management and the assessment of fire impact on earth-atmosphere system. Threshold-based segmentation from a single image is an efficient and operational method for detecting BA. However, the great diversity of fire conditions necessitates an adaptive threshold that considers environmental variations. This paper presents a maximum curvature segmentation method to capture the adaptative thresholds. The spectral contrasts in near-infrared (NIR) and shortwave infrared (SWIR) bands were utilized to distinguish BA. The decreased NIR threshold was employed to obtain the burned candidates, and the increased SWIR threshold was then applied to confirm the candidates. Experiments were conducted in different biomes, covering the boreal forest, tropical forest, savanna, and Mediterranean, and different seasons including growing and nongrowing seasons. The thresholds changed in each tile, indicating the algorithm adapted the spatial and temporal variations. Comparison with the Burned Area Reference Database was performed at different biomes, resulting in overall dice coefficient (DC), omission error (OE), commission error (CE), and relative Bias (relB) being 0.86, 0.18, 0.10, and -0.08, respectively. The algorithm provides an avenue for adaptive detection of burned areas, and the single-image based approach can provide real-time burned information for wildfire management systems. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) images enable high-resolution information collection from complex ground objects and are increasingly utilized in the earth observation research. Recently, RS technologies are continuously enhanced by various characterized platforms and sensors. Simultaneously, artificial intelligence vision algorithms are also developing vigorously and playing a significant role in RS image analysis. In particular, aiming to divide images into different ground elements with specific semantic labels, RS image segmentation could realize the visual acquisition and interpretation. As one of the pioneering methods with the advantages of deep feature extraction ability, deep learning (DL) algorithms have been exploited and proved to be highly beneficial for precise segmentation in recent years. In this paper, a comprehensive review is performed on remote sensing survey systems and various kinds of specially designed deep learning architectures. Meanwhile, DL-based segmentation methods applied on four domains are also illustrated, including geography, precision agriculture, hydrology, and environmental protection issues. In the end, the existing challenges and promising research directions in RS image segmentation are discussed. It is envisioned that this review is able to provide a comprehensive and technical reference, deployment and successful exploitation of DL empowered RS image segmentation approaches. [ABSTRACT FROM AUTHOR]

Communication towers provide reliable wireless communication services and play a vital role in modern society. This paper presents an automatic 3D reconstruction framework for communication towers based on point clouds. The antenna is reconstructed by a model-driven method, which constructs an energy function including the data and smoothing terms to evaluate the model accuracy. The parameters of the antenna model are obtained by minimizing energy function with hybrid Genetic Algorithm (GA) and Simulated Annealing (SA). And the predefined model is transformed using these parameters to achieve antenna 3D reconstruction. For tower body reconstruction, the point cloud of the tower body is finely extracted with the region growing followed by piecewise fitting to obtain the model. The performance of the proposed method is evaluated on point clouds from six scenes. The average root-mean-square error (RMSE) of antenna reconstruction is 1.3 cm. The average precision, recall, and F1-score values of the tower body point extraction are 99.9%, 93.5%, and 96.6%, respectively. The average RMSE of tower body reconstruction is 1.2 cm. The experimental results indicate that the method can effectively reconstruct the antenna and tower body with point clouds, providing a feasible framework for the 3D reconstruction of communication towers. [ABSTRACT FROM AUTHOR]

Multiple tasks within the field of geographical information retrieval and geographical information sciences necessitate toponym matching, which involves the challenge of aligning toponyms that share a common referent. The multiple string similarity approaches struggle when confronted with the complexities associated with unofficial and/ or historical variants of identical toponyms. Also, current state-of-the-art approaches/tools to supervised machine learning rely on labeled samples, and they do not adequately address the intricacies of character replacements either from transliterations or historical shifts in linguistic and cultural norms. To address these issues, this paper proposes a novel matching approach that leverages a deep neural network model empowered by geographic language representation model, known as GeoBERT, which stands for geographic Bidirectional Encoder Representations from Transformers (BERT). This model harnesses the groundbreaking capabilities of the GeoBERT framework by extending a generalized Enhanced Sequential Inference Model architecture and integrating multiple features to enhance the accuracy and robustness of the toponym matching. We present a comprehensive evaluation of the proposed method's performance using three extensive datasets. The findings clearly illustrate that our approach outperforms the individual similarity metrics used in previous studies. [ABSTRACT FROM AUTHOR]

Background: Xenograft kidney transplantation has been receiving increasing attention. The purpose of this study is to use bibliometric analysis to identify papers in this research field and explore their current status and development trends. Methods: Using the data in the Web of Science core database from Clarivate Analytics as the object of study, we used ‘TS = Kidney OR Renal AND xenotransplantation’ as the search term to find all literature from 1980 to 2 November 2022. Results: In total, 1005 articles were included. The United States has the highest number of publications and has made significant contributions in this field. Harvard University was at the forefront of this study. Professor Cooper has published 114 articles in this field. Xenotransplantation has the largest number of relevant articles. Transplantation was the most cited journal. High-frequency keywords illustrated the current state of development and future trends in xenotransplantation. The use of transgenic pigs and the development of coordinated co-stimulatory blockers have greatly facilitated progress in xenotransplantation research. We found that ‘co-stimulation blockade’, ‘xenograft survival’, ‘pluripotent stem cell’, ‘translational research’, and ‘genetic engineering’ were likely to be the focus of attention in the coming years. Conclusions: This study screened global publications related to xenogeneic kidney transplantation; analyzed their literature metrology characteristics; identified the most cited articles in the research field; understood the current situation, hot spots, and trends of global research; and provided future development directions for researchers and practitioners. [ABSTRACT FROM AUTHOR]

Diabetic kidney disease (DKD) is a serious complication of diabetes mellitus (DM) and has become the main cause of end-stage renal disease worldwide. In recent years, with the increasing incidence of DM, the pathogenesis of DKD has received increasing attention. The pathogenesis of DKD is diverse and complex. Extracellular vesicles (EVs) contain cell-derived membrane proteins, nucleic acids (such as DNA and RNA) and other important cellular components and are involved in intercellular information and substance transmission. In recent years, an increasing number of studies have confirmed that EVs play an important role in the development of DKD. The purpose of this paper is to explain the potential diagnostic value of EVs in DKD, analyze the mechanism by which EVs participate in intercellular communication, and explore whether EVs may become drug carriers for targeted therapy to provide a reference for promoting the implementation and application of exosome therapy strategies in clinical practice. [ABSTRACT FROM AUTHOR]

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With Construction Management being a relatively new discipline, the relationship between academia and industry is underdeveloped. Studies have identified the gap between academia and industry, with recommendations made as to how this gap may be closed. However, little research is done into what is restricting Higher Education Institutions from acting on these findings. The aim of this paper is to address this. Five key themes emerge in the literature: Human, Resources, Organizational Structure, External Influences, and Administrative Factors. To complement this, semi-structured interviews are undertaken with 12 Construction Management representatives from each of the 11 Higher Education Institutions offering a Construction Management program in the Republic of Ireland. Using the five themes that emerged in the literature, each of the interviews is thematically coded. The results indicate key factors inhibiting the change implementation processes within construction management departments. These include difficulties in the number of boards and committees involved in change, a lack of incentive for lecturing staff undertaking what is considered additional work, disparity in the definition of a "successful program" within the Higher Education Institutions, extensive amounts of reports required for change proposal, lecturing staff being largely overworked, and organizational structures that do not facilitate collaboration between departments. [ABSTRACT FROM AUTHOR]

Much research has been carried out in the field of business management to understand why some organizations perform better than others, while others fail. Strategic management field has a strong focus on exploring how firms operate and the performance differentials among them. In strategic management, predominantly, three theories are applied to assess firm performance. These theories are Resource-Based View (RBV), Dynamic Capabilities theory (DC), and Contingency Theory (CT). Through a systematic literature review, this paper explores how these three theories have been applied to construction management literature to review and examine the performance of the construction firms. Review of 46 articles was carried out to identify the factors that affect the performance of the construction firms in line with the frameworks presented by the aforementioned theories. The findings of the study hold significance for future researches on performance determinants of construction firms by evidencing the application of RBV, DC, and CT in construction management literature, which paves the way to future research aiming at combining the underlying principles of these theories to explore construction firm's performance. [ABSTRACT FROM AUTHOR]

The sheer number of publications that deal with the topic of optimizing the management of river basins has grown exponentially since the early 1980s, and this growth is still on the rise. Despite this, the practical actions of most reservoir operators are still based on their gut feelings, or at best on straightforward rules that did not originate from rigorous scientific studies but are rather the result of the operator's experience or simple spreadsheet calculations. Many publications have already pointed out the gap between theory and practice over the past few decades; however, none have so far offered clear guidelines on how to overcome this gap. This paper presents an extensive literature review to examine potential reasons for this gap. In addition to this, a numerical test problem demonstrates a novel way of using linear programming for constructing Pareto-optimal solutions for a large class of multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear elasto-plastic analysis method under incremental external loads is presented to determine static pushover curves of 3D frame structures. Geometric nonlinearity is taken into account with stability functions, and the nonlinear behaviour of material is considered by plastic hinge hypothesis. Improved matrices and matrix operations for 3D frame elements are given, and the results obtained from a six-storey 3D frame, which is used as a benchmark example, are given comparatively with the related studies. Furthermore, in order to perform these analyses with the developed algorithms, the upgraded computer program TUNAL is used to present pushover curves of a 2-storey RC building for an assumption on torsional stiffness caused by plastic hinges. It is shown that the proposed methodology and the computer program give accurate and reliable results. [ABSTRACT FROM AUTHOR]

This paper represents a detailed investigation of tall hexagonal buildings in the presence of a similar type of single-interfering buildings and double-interfering buildings. The building models are analysed using CFD in ANSYS CFX module. The k-ε turbulence model is used to carry out the numerical simulations. For two buildings case (single interfering building), the wind incidence angles (WIA) vary from 0° to 90° at an interval of 15° and from 90° to 360° at an interval of 30°. The variable spacing of 200 mm, 300 mm, 500 mm and 800 mm has been kept apart for two building case. For the three building case (double interfering buildings), the orientation of the interfering buildings kept varying from 15° to 90° with a regular interval of 15°. The variable spacing between the principal building and interfering buildings is kept 300 mm and 500 mm apart. The model height for both cases is 500 mm in 1:300 length scale. The variation of interference factors for both pressure and force coefficients are evaluated and graphically represented to investigate the hexagonal building model. The interference Factor is found to be minimum at 90° WIA in each of the faces irrespective of spacing between buildings. [ABSTRACT FROM AUTHOR]

Because the cement content is inexorably growing, CO2 emissions from the cement industry are becoming unavoidable. The use of rice husk ash (RHA) makes the system environmentally benign. This paper investigates the effect of RHA and manufactured sand (M-Sand) on the axial capacity of circular Concrete Filled Steel Tube (CFST) specimens subjected to axial compression. Thirty-two CFST specimens infilled with self-compacting concrete (SCC) with a replacement of RHA and M-Sand to cement and fine aggregate in 0%, 15%, 20% and 25% proportions are subjected to axial compression. Results show that use of RHA beyond 20% decreased the compressive strength of concrete and eventually decreased the axial capacity of CFST and occurred higher local buckling irrespective of their L/D ratios. Failure modes, strength index, confinement along with steel and concrete contribution on axial capacities of CFST specimens are studied. The axial capacities of the specimens were compared with various design code results such as: American concrete institute, Australian Standards, Chinese code, Eurocode, and Japanese code. Among all the codes, Chinese code overestimated the test results and hence, the axial capacity equation is altered and compared the results with test data along with 278 circular CFST data available from literature and found the results to be accurate. [ABSTRACT FROM AUTHOR]

Infectious bronchitis virus (IBV) strains of genotype GVIII have been emerging in Europe in the last decade, but no biological characterization has been reported so far. This paper reports the extensive genetic and biological characterization of IBV strain D2860 of genotype GVIII which was isolated from a Dutch layer flock that showed a drop in egg production. Whole genome sequencing showed that it has a high similarity (95%) to CK/DE/IB80/2016 (commonly known as IB80). Cross-neutralization tests with antigens and serotype-specific antisera of a panel of different non-GVIII genotypes consistently gave less than 2% antigenic cross-relationship with D2860. Five experiments using specified pathogen-free chickens of 0, 4, 29 and 63 weeks of age showed that D2860 was not able to cause clinical signs, drop in egg production, false layers or renal pathology. There was also a distinct lack of ciliostasis at both 5 and 8 days post-inoculation at any age, despite proof of infection by immunohistochemical (IHC) staining, RT–PCR and serology. IHC showed immunostaining between 5 and 8 days post inoculation in epithelial cells of sinuses and conchae, while only a few birds displayed immunostaining in the trachea. In vitro comparison of replication of D2860 and M41 in chicken embryo kidney cells at 37°C and at 41°C indicated that D2860 might have a degree of temperature sensitivity that might cause it to prefer the colder parts of the respiratory tract. [ABSTRACT FROM AUTHOR]

Salmonellosis represents a significant economic and public health concern for the poultry industry in Africa, leading to substantial economic losses due to mortality, reduced productivity, and food safety problems. However, comprehensive information on the burden of poultry salmonellosis at the continental level is scarce. To address this gap, a systematic review and meta-analysis were conducted to consolidate information on the prevalence and circulating serotypes of poultry salmonellosis in African countries. This involved the selection and review of 130 articles published between 1984 and 2021. A detailed systematic review protocol was structured according to Cochrane STROBE and PRISMA statement guideline. From the 130 selected articles from 23 different African countries, the overall pooled prevalence estimate (PPE) of poultry salmonellosis in Africa was found to be 14.4% (95% CI = 0.145–0.151). Cameroon reported the highest PPE at 71.9%. The PPE was notably high in meat and meat products at 23%. The number of research papers reporting poultry salmonellosis in Africa has shown a threefold increase from 1984 to 2021. Salmonella Enteritidis and Typhimurium were the two most prevalent serotypes reported in 18 African countries. Besides, Salmonella Kentucky, Virchow, Gallinarum, and Pullorum were also widely reported. Western Africa had the highest diversity of reported Salmonella serotypes (141), in contrast to southern Africa, which reported only 27 different serotypes. In conclusion, poultry salmonellosis is highly prevalent across Africa, with a variety of known serotypes circulating throughout the continent. Consequently, it is crucial to implement strategic plans for the prevention and control of Salmonella in Africa. RESEARCH HIGHLIGHTS The pooled sample prevalence of poultry salmonellosis in Africa is high (14.4%). The highest PPE was recorded in meat and meat products. Salmonella serotypes of zoonotic importance were found in all sample types. Salmonella Enteritidis and Typhimurium are common serotypes spreading in Africa. [ABSTRACT FROM AUTHOR]

Nigerian online performance humour in different forms of comedy skits deploys a range of linguistic resources. However, existing conceptualisations of Nigerian online comedy skits ignore the significance of the language forms to the genre. In this paper, I argue that given the Nigerian multilingual context with the domineering presence of the English language, the pervasive use of ungrammatical English utterances in online comedy skits is an intentional strategy meant to contextualise the scripted humorous narratives as interactions with the viewers. Ungrammatical utterances are forms of deliberate language play which, together with digital play, facilitate the recipients’ participation in the performance of online humour in the Nigerian ESL multilingual context. [ABSTRACT FROM AUTHOR]

Crop abandonment is when farmers decide not to harvest their previously planted crop. There is limited but emerging literature on crop abandonment or failure predominantly examining weather and crop failure rates. Consistent with these existing scant studies, it is not immediately clear to what extent historical relationships can be extrapolated in the long run under climate change. This paper seeks to improve our understanding of determinants of crop abandonment decisions in Zambia for maize production at subnational level. We find that crop abandonment (harvesting) is positively (negatively) related to fertiliser use, rainfall and temperature and negatively (positively) related to cost of living, price of maize, index-based insurance cover, town and random shocks. Therefore, fertiliser, rainfall and temperature increase the likelihood of crop abandonment in Zambia whereas increasing cost of living, maize price, insurance participation, town specific and random shock reduce it. [ABSTRACT FROM AUTHOR]

AbstractHigher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it. In this paper we propose the (subgaussian) tensor mixed-membership blockmodel, a generalization of the tensor blockmodel positing that memberships need not be discrete, but instead are convex combinations of latent communities. We establish the identifiability of our model and propose a computationally efficient estimation procedure based on the higher-order orthogonal iteration algorithm (HOOI) for tensor SVD composed with a simplex corner-finding algorithm. We then demonstrate the consistency of our estimation procedure by providing a per-node error bound under subgaussian noise, which showcases the effect of higher-order structures on estimation accuracy. To prove our consistency result, we develop the ℓ2,∞ tensor perturbation bound for HOOI under independent, heteroskedastic, subgaussian noise that may be of independent interest. Our analysis uses a novel leave-one-out construction for the iterates, and our bounds depend only on spectral properties of the underlying low-rank tensor under nearly optimal signal-to-noise ratio conditions such that tensor SVD is computationally feasible. Finally, we apply our methodology to real and simulated data, demonstrating some effects not identifiable from the model with discrete community memberships. [ABSTRACT FROM AUTHOR]

AbstractA dose-response curve is essential for determining the safe dosage of a drug and is widely used in bioassay and in phase 1 clinical trials. It is generally accepted that the probability of death or the probability of dose-limiting toxicity is a non-decreasing function of the dose. This paper proposes and develops an effective point-wise confidence interval for an isotonic dose-response curve, a problem without a satisfactory solution so far. We show that one subset of the observations informs the lower limit while the other subset informs the upper limit. A partial likelihood ratio test using these subsets of observations is fully investigated. The resulting confidence interval is compared to three existing methods (Morris, Meyer, and Oron) in an extensive simulation study. The new method produces tight intervals while maintaining coverage. It is therefore preferred when maintaining an actual coverage probability is crucial as often in a regulatory environment. The new method extends to one-way ANOVA of a continuous response variable in a straightforward way. An R function is provided. [ABSTRACT FROM AUTHOR]

AbstractLow-rank approximation is a popular strategy to tackle the “big n problem” associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structures are crucial and should be carefully specified. Predictive processes simplify the problem by inducing basis functions with a covariance function and a set of knots. The existing literature suggests certain practical implementations of knot selection and covariance estimation; however, theoretical foundations explaining the influence of these two factors on predictive processes are lacking. In this paper, the asymptotic prediction performance of the predictive process and Gaussian process predictions are derived and the impacts of the selected knots and estimated covariance are studied. The use of support points as knots, which best represent data locations, is advocated. Extensive simulation studies demonstrate the superiority of support points and verify our theoretical results. Real data of precipitation and ozone are used as examples, and the efficiency of our method over other widely used low-rank approximation methods is verified. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, we study the transfer learning problem in functional classification, aiming to improve the classification accuracy of the target data by leveraging information from related source datasets. To facilitate transfer learning, we propose a novel transferability function tailored for classification problems, enabling a more accurate evaluation of the similarity between source and target dataset distributions. Interestingly, we find that a source dataset can offer more substantial benefits under certain conditions than another dataset with an identical distribution to the target dataset. This observation renders the commonly-used debiasing step in the parameter-based transfer learning algorithm unnecessary under some circumstances to the classification problem. In particular, we propose two adaptive transfer learning algorithms based on the functional Distance Weighted Discrimination (DWD) classifier for scenarios with and without prior knowledge regarding informative sources. Furthermore, we establish the upper bound on the excess risk of the proposed classifiers, providing the statistical gain via transfer learning mathematically provable. Simulation studies are conducted to thoroughly examine the finite-sample performance of the proposed algorithms. Finally, we implement the proposed method to Beijing air-quality data, and significantly improve the prediction of the PM 2.5 level of a target station by effectively incorporating information from source datasets. [ABSTRACT FROM AUTHOR]