bluuebunny/crossref_metadata_2025 huggingface dataset:
Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection
Authors: Zhao Wei , Yamada William , Li Tianxin , Digman Matthew , Runge Troy
Published: December 2020

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.
Magnetic Survey at the Roman Military Camp of el Benian in Mauretania Tingitana (Morocco): Results and Implications
Authors: Martorella Francesco
Published: December 2020

The topic of military settlements and the role of troops in the northern provinces of North Africa during the age of the Roman Empire has recently gained a strong interest in historical, archaeological, epigraphical, and economic studies. In particular, at Mauretania Tingitana (in the north-east area of modern-day Morocco), the presence of numerous military camps in the Early and Later Roman Empire has now been assessed. In this framework, the present work deals with the geophysical survey, by means of magnetometry, at the site of el Benian, where the largest military camp is located. In particular, the magnetic survey has highlighted the organization of the camp, almost totally unknown previously. The result of the magnetic survey has confirmed intense building activity over the centuries and made it possible to identify and characterize the structures typical of a military field.
Security and Privacy Techniques in IoT Environment
Authors: Lin Jerry Chun-Wei , Yeh Kuo-Hui
Published: December 2020

Due to rapid technical advancements, many devices in the Internet of Things (IoT) environment, such as embedded systems, mobile devices, actuators, and sensors (all of which can be referred to as smart things), can receive huge amounts of information through data exchanging and interconnection [...]
Quantifying the Varying Predictive Value of Physical Activity Measures Obtained from Wearable Accelerometers on All-Cause Mortality over Short to Medium Time Horizons in NHANES 2003–2006
Authors: Tabacu Lucia , Ledbetter Mark , Leroux Andrew , Crainiceanu Ciprian , Smirnova Ekaterina
Published: December 2020

Physical activity measures derived from wearable accelerometers have been shown to be highly predictive of all-cause mortality. Prediction models based on traditional risk factors and accelerometry-derived physical activity measures are developed for five time horizons. The data set contains 2978 study participants between 50 and 85 years old with an average of 13.08 years of follow-up in the NHANES 2003–2004 and 2005–2006. Univariate and multivariate logistic regression models were fit separately for five datasets for one- to five-year all-cause mortality as outcome (number of events 46, 94, 155, 218, and 297, respectively). In univariate models the total activity count (TAC) was ranked first in all five horizons (AUC between 0.831 and 0.774) while the active to sedentary transition probability (ASTP) was ranked second for one- to four-year mortality models and fourth for the five-year all-cause mortality model (AUC between 0.825 and 0.735). In multivariate models age and ASTP were significant in all one- to five-year all-cause mortality prediction models. Physical activity measures are consistently among the top predictors, even after adjusting for demographic and lifestyle variables. Physical activity measures are strong stand-alone predictors and substantially improve the prediction performance of models based on traditional risk factors.
Imaging Scatterometry with Extrapolation of Missing BRDF Data for Materials Used in Laser Material Processing
Authors: Zakrzewski Adrian , Jurewicz Piotr , Ćwikła Michał , Koruba Piotr , Reiner Jacek
Published: December 2020

Imaging scatterometry is a method for determining the reflection distribution based on bidirectional reflectance distribution function (BRDF) measurements. However, it has a well-known limitation that results obtained by imaging scatterometry for small illumination angles are practically useless. Therefore, we propose an approach for reconstruction of the reflection distribution based on a series of measurements at different illumination angles and extrapolation of the missing results to overcome this limitation. The developed algorithm was validated using bidirectional transmittance distribution function (BTDF) measurements. The BRDF measurements were carried out for materials that are commonly used in laser material processing, i.e. substrates and functional coatings. The obtained data were subsequently used to determine the total reflection intensity from all considered materials, which were characterized by reconstructed distributions.
Real-Time Single Image Depth Perception in the Wild with Handheld Devices
Authors: Aleotti Filippo , Zaccaroni Giulio , Bartolomei Luca , Poggi Matteo , Tosi Fabio , Mattoccia Stefano
Published: December 2020

Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image would represent the most versatile solution since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit the practical deployment of monocular depth estimation methods on such devices: (i) the low reliability when deployed in the wild and (ii) the resources needed to achieve real-time performance, often not compatible with low-power embedded systems. Therefore, in this paper, we deeply investigate all these issues, showing how they are both addressable by adopting appropriate network design and training strategies. Moreover, we also outline how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time, depth-aware augmented reality and image blurring with smartphones in the wild.
Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data
Authors: Haghayegh Shahab , Khoshnevis Sepideh , Smolensky Michael H. , Diller Kenneth R. , Castriotta Richard J.
Published: December 2020

Background: Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the actigraph hardware but also on the attributes of the interpretative algorithm (IA). The objective of our research was to improve assessment of sleep quality, relative to existing IAs, through development of a novel IA using deep learning methods, utilizing as input activity count and heart rate variability (HRV) metrics of different window length (number of epochs of data). Methods: Simultaneously recorded polysomnography (PSG) and wrist actigraphy data of 222 participants were utilized. Classic deep learning models were applied to: (a) activity count alone (without HRV), (b) activity count + HRV (30-s window), (c) activity count + HRV (3-min window), and (d) activity count + HRV (5-min window) to ascertain the best set of inputs. A novel deep learning model (Haghayegh Algorithm, HA), founded on best set of inputs, was developed, and its sleep scoring performance was then compared with the most popular University of California San Diego (UCSD) and Actiwatch proprietary IAs. Results: Activity count combined with HRV metrics calculated per 5-min window produced highest agreement with PSG. HA showed 84.5% accuracy (5.3–6.2% higher than comparator IAs), 89.5% sensitivity (6.2% higher than UCSD IA and 6% lower than Actiwatch proprietary IA), 70.0% specificity (8.2–34.3% higher than comparator IAs), and 58.7% Kappa agreement (16–23% higher than comparator IAs) in detecting sleep epochs. HA did not differ significantly from PSG in deriving sleep parameters—sleep efficiency, total sleep time, sleep onset latency, and wake after sleep onset; moreover, bias and mean absolute error of the HA model in estimating them was less than the comparator IAs. HA showed, respectively, 40.9% and 54.0% Kappa agreement with PSG in detecting rapid and non-rapid eye movement (REM and NREM) epochs. Conclusions: The HA model simultaneously incorporating activity count and HRV metrics calculated per 5-min window demonstrates significantly better sleep scoring performance than existing popular IAs.
MME-YOLO: Multi-Sensor Multi-Level Enhanced YOLO for Robust Vehicle Detection in Traffic Surveillance
Authors: Zhu Jianxiao , Li Xu , Jin Peng , Xu Qimin , Sun Zhengliang , Song Xiang
Published: December 2020

As an effective means of solving collision problems caused by the limited perspective on board, the cooperative roadside system is gaining popularity. To improve the vehicle detection abilities in such online safety systems, in this paper, we propose a novel multi-sensor multi-level enhanced convolutional network model, called multi-sensor multi-level enhanced convolutional network architecture (MME-YOLO), with consideration of hybrid realistic scene of scales, illumination, and occlusion. MME-YOLO consists of two tightly coupled structures, i.e., the enhanced inference head and the LiDAR-Image composite module. More specifically, the enhanced inference head preliminarily equips the network with stronger inference abilities for redundant visual cues by attention-guided feature selection blocks and anchor-based/anchor-free ensemble head. Furthermore, the LiDAR-Image composite module cascades the multi-level feature maps from the LiDAR subnet to the image subnet, which strengthens the generalization of the detector in complex scenarios. Compared with YOLOv3, the enhanced inference head achieves a 5.83% and 4.88% mAP improvement on visual dataset LVSH and UA-DETRAC, respectively. Integrated with the composite module, the overall architecture gains 91.63% mAP in the collected Road-side Dataset. Experiments show that even under the abnormal lightings and the inconsistent scales at evening rush hours, the proposed MME-YOLO maintains reliable recognition accuracy and robust detection performance.
The Sensitive Element of Acoustic Sensor on Circular Polarized Waves: From Theoretical Considerations towards Perspective Rotation Rate Sensors Design
Authors: Shevelko Michail , Lutovinov Andrey , Peregudov Aleksandr , Popkova Ekaterina , Durukan Yasemin , Shevchenko Sergey
Published: December 2020

In this paper, the perspectives of using the features of acoustic wave propagation to design rotation rate sensors (RRS) are discussed. The possibility of developing the solid-state sensitive elements (SE) of RRS on acoustic waves of circular polarization is shown. The theoretical basis of bulk acoustic wave propagation under rotation is given. The direct excitation of circularly polarized acoustic wave (CPAW) is considered, the design of the CPAW emitting transducer is offered. The results of experimental studies that indicated the circular nature of the particle motions in the radiated wave are discussed. The principally new concept of the RRS SE design on CPAW, being able to operate under high vibration and acceleration, is proposed. The experimental results revealed a high correlation with theoretical and numerical predictions and confirmed RRS on CPAW operability.
Trend Analysis of Temperature Data for the Narayani River Basin, Nepal
Authors: Chand Mohan Bahadur , Bhattarai Bikas Chandra , Pradhananga Niraj Shankar , Baral Prashant
Published: December 2020

The study of spatiotemporal variation in temperature is vital to assess changes in climate, especially in the Himalayan region, where the livelihoods of billions of people living downstream depends on water coming from the melting of snow and glacier ice. To this end, temperature trend analysis is carried out in the Narayani River basin, a major river basin of Nepal, characterized by three climatic regions: tropical, subtropical and alpine. Temperature data from six stations located within the basin were analyzed. The elevation of these stations ranges from 460 to 3800 m a.s.l. and the time period of available temperature data ranges from 1960–2015. Multiple regression and empirical mode decomposition (EMD) methods were applied to fill in missing data and to detect trends. Annual as well as seasonal trends were analyzed and a Mann–Kendall test was employed to test the statistical significance of detected trends. The results indicate significant cooling trends before 1970s, and warming trends after 1970s in the majority of the stations. The warming trends range from 0.028 to 0.035 °C year−1 with a mean increasing trend of 0.03 °C year−1 after 1971. Seasonal trends show the highest warming trends in the monsoon season, followed by winter and the premonsoon and postmonsoon season. However, the difference in warming rates between different seasons was not significant. An average temperature lapse rate of −0.006 °C m−1 with the steepest value (−0.0064 °C m−1) in the premonsoon season and the least negative (−0.0052 °C m−1) in the winter season was observed for this basin. A comparative analysis of the gap-filled data with freely available global climate dataset show reasonable correlation, thus confirming the suitability of the gap filling methods.
Sparse Measurement-Based Coordination of Electric Vehicle Charging Stations to Manage Congestions in Low Voltage Grids
Authors: Schultis Daniel-Leon
Published: December 2020

The increasing use of distributed generation and electric vehicle charging stations provokes violations of the operational limits in low voltage grids. The mitigation of voltage limit violations is addressed by Volt/var control strategies, while thermal overload is avoided by using congestion management. Congestions in low voltage grids can be managed by coordinating the active power contributions of the connected elements. As a prerequisite, the system state must be carefully observed. This study presents and investigates a method for the sparse measurement-based detection of feeder congestions that bypasses the major hurdles of distribution system state estimation. Furthermore, the developed method is used to enable congestion management by the centralized coordination of the distributed electric vehicle charging stations. Different algorithms are presented and tested by conducting load flow simulations on a real urban low voltage grid for several scenarios. Results show that the proposed method reliably detects all congestions, but in some cases, overloads are detected when none are present. A minimal detection accuracy of 73.07% is found across all simulations. The coordination algorithms react to detected congestions by reducing the power consumption of the corresponding charging stations. When properly designed, this strategy avoids congestions reliably but conservatively. Unnecessary reduction of the charging power may occur. In total, the presented solution offers an acceptable performance while requiring low implementation effort; no complex adaptations are required after grid reinforcement and expansion.
Sustainable Swine Manure Management: A Tale of Two Agreements
Authors: Deviney Alison , Classen John , Bruce Jackie , Sharara Mahmoud
Published: December 2020

Intensification and concentration of swine farming has provided economic benefit to rural communities but also negative environmental and human health impacts, particularly from the use of the lagoon-sprayfield system for manure management. Although cost effective, this system is susceptible to poor management, unpleasant odor and other emissions, and inundation during extreme weather events. Competition for manure-spreading acres with other livestock or encroaching development can also pose a problem. This study examines two agreements between industry and government designed to develop and implement improved manure management technologies for swine farms: a voluntary agreement between the attorney general of North Carolina and Smithfield Foods and a consent judgment between the State of Missouri and Premium Standard Farms. Individuals involved in executing these agreements were interviewed to gain insight from their perspective on those processes and lessons they learned from their experience. Common themes among participant responses to support transition processes included the need to involve multiple stakeholder groups, clearly define goals, understand the system, allow time for incremental change, and provide adequate “protected space” for technology development and implementation. Viewing these themes through the lens of multi-level perspective theory identifies leverage points throughout the system to support transitioning farms to a more sustainable path of manure management.
Bacterial Flagellum versus Carbon Nanotube: A Review Article on the Potential of Bacterial Flagellum as a Sustainable and Green Substance for the Synthesis of Nanotubes
Authors: Chun Charles Ng Wai , Tajarudin Husnul Azan , Ismail Norli , Azahari Baharin , Makhtar Muaz Mohd Zaini , Yan Leong Kah
Published: December 2020

Bacterial flagella are complex multicomponent structures that help in cell locomotion. It is composed of three major structural components: the hook, the filament and basal body. The special mechanical properties of flagellar components make them useful for the applications in nanotechnology especially in nanotube formation. Carbon nanotubes (CNTs) are nanometer scale tube-shaped material and it is very useful in many applications. However, the production of CNTs is costly and detrimental to the environment as it pollutes the environment. Therefore, bacterial flagella have become a highly interesting research area especially in producing bacterial nanotubes that could replace CNTs. In this review article, we will discuss about bacterial flagellum and carbon nanotubes in the context of their types and applications. Then, we will focus and review on the characteristics of bacterial flagellum in comparison to carbon nanotubes and subsequently, the advantages of bacterial flagellum as nanotubes in comparison with carbon nanotubes.
UAV Photogrammetry Surveying for Sustainable Conservation: The Case of Mondújar Castle (Granada, Spain)
Authors: Orihuela Antonio , Molina-Fajardo María Aurora
Published: December 2020

Mondújar Castle is an Andalusi fortress located in the Valle de Lecrín (Granada, Spain). It had strategic importance in the final years of the Kingdom of Granada. The king Muley Hacén lived there before passing away, resulting in the popularisation of Romantic legends around its construction. Despite these folktales, the fortress has never been surveyed or restored and a complete architectural graphic study of this place is lacking. Therefore, it is essential to document the architectural heritage to collect relevant information for conservation work. Our main goal is to better understand the origin, architectural influences and building phases of the fortress, which requires historical and surveying methods. We present a historical approximation, followed by a photogrammetric survey. This is the first study on the medieval fortress and its subsequent Castilian refortification (executed around 1500). We conclude that it is not plausible that this place was the location of any legendary palaces. Apart from its historical and constructive significance, the use of Islamic funerary elements, probably coming from the Royal Nasrid Cemetery, makes this castle unique. Therefore, the preservation and understanding of this monument should be a priority within the sustainable development of the region.
Compost and Sewage Sludge for the Improvement of Soil Chemical and Biological Quality of Mediterranean Agroecosystems
Authors: Picariello Enrica , Pucci Luca , Carotenuto Maurizio , Libralato Giovanni , Lofrano Giusy , Baldantoni Daniela
Published: December 2020

Conventional fertilization practices in agroecosystems concern the supply of bioavailable nutrients, such as mineral fertilizers. A consolidated alternative to restoring the long-term fertility of agricultural soils is their amendment with organic fertilizers. Soil amendment with biowaste compost or sewage sludge represents a sustainable strategy to avoid the landfilling of organic matter derived from urban waste and sewage sludge. This study aims at validating the use of quality biowaste compost and sewage sludge from secondary sedimentation (alone or in combination with mineral fertilizers) in a Mediterranean agroecosystem and their effects on soil chemical and biological quality, with a view to verifying the maintenance of soil fertility and functionality. In particular, the dynamics of soil organic matter, pH, potentially toxic elements and microbial community functionality were assessed, in experimental mesocosms, during 6 months of incubation. The research showed that, while soil amendment with biowaste compost induces positive effects on soil organic matter and phosphorous concentrations, as well as on microbial community functionality, the amendment with the selected sewage sludge does not determine any benefit to the microbial community or any danger in relation to soil potentially toxic element concentrations and toxicity. The quantity of sewage sludge employed, chosen according to regional directives, was thus not enough to stimulate the edaphic microflora activity.
Urban Geometry Optimization to Mitigate Climate Change: Towards Energy-Efficient Buildings
Authors: Mahmoud Hatem , Ragab Ayman
Published: December 2020

The density of building blocks and insufficient greenery in cities tend to contribute dramatically not only to increased heat stress in the built environment but also to higher energy demand for cooling. Urban planners should, therefore, be conscious of their responsibility to reduce energy usage of buildings along with improving outdoor thermal efficiency. This study examines the impact of numerous proposed urban geometry cases on the thermal efficiency of outer spaces as well as the energy consumption of adjacent buildings under various climate change scenarios as representative concentration pathways (RCP) 4.5 and 8.5 climate projections for New Aswan city in 2035. The investigation was performed at one of the most underutilized outdoor spaces on the new campus of Aswan University in New Aswan city. The potential reduction of heat stress was investigated so as to improve the thermal comfort of the investigated outdoor spaces, as well as energy savings based on the proposed strategies. Accordingly, the most appropriate scenario to be adopted to cope with the inevitable climate change was identified. The proposed scenarios were divided into four categories of parameters. In the first category, shelters partially (25–50% and 75%) covering the streets were used. The second category proposed dividing the space parallel or perpendicular to the existing buildings. The third category was a hybrid scenario of the first and second categories. In the fourth category, a green cover of grass was added. A coupling evaluation was applied utilizing ENVI-met v4.2 and Design-Builder v4.5 to measure and improve the thermal efficiency of the outdoor space and reduce the cooling energy. The results demonstrated that it is better to cover outdoor spaces with 50% of the overall area than transform outdoor spaces into canyons.
Bottom-Up Generation of Peak Demand Scenarios in Water Distribution Networks
Authors: Creaco Enrico , Galuppini Giacomo , Campisano Alberto , Franchini Marco
Published: December 2020

This paper presents a two-step methodology for the stochastic generation of snapshot peak demand scenarios in water distribution networks (WDNs), each of which is based on a single combination of demand values at WDN nodes. The methodology describes the hourly demand at both nodal and WDN scales through a beta probabilistic model, which is flexible enough to suit both small and large demand aggregations in terms of mean, standard deviation, and skewness. The first step of the methodology enables generating separately the peak demand samples at WDN nodes. Then, in the second step, the nodal demand samples are consistently reordered to build snapshot demand scenarios for the WDN, while respecting the rank cross-correlations at lag 0. The applications concerned the one-year long dataset of about 1000 user demand values from the district of Soccavo, Naples (Italy). Best-fit scaling equations were constructed to express the main statistics of peak demand as a function of the average demand value on a long-time horizon, i.e., one year. The results of applications to four case studies proved the methodology effective and robust for various numbers and sizes of users.
Correction: Ronzon, T., et al. Developments of Economic Growth and Employment in Bioeconomy Sectors across the EU. Sustainability 2020, 12, 4507
Authors: Ronzon Tévécia , Piotrowski Stephan , Tamosiunas Saulius , Dammer Lara , Carus Michael , M’barek Robert
Published: December 2020

The authors would like to make the following corrections to the published paper [...]
Uncertainty Assessment of CFD Investigation of the Nonlinear Difference-Frequency Wave Loads on a Semisubmersible FOWT Platform
Authors: Wang Lu , Robertson Amy , Jonkman Jason , Yu Yi-Hsiang
Published: December 2020

Current mid-fidelity modeling approaches for floating offshore wind turbines (FOWTs) have been found to underpredict the nonlinear, low-frequency wave excitation and the response of semisubmersible FOWTs. To examine the cause of this underprediction, the OC6 project is using computational fluid dynamics (CFD) tools to investigate the wave loads on the OC5-DeepCwind semisubmersible, with a focus on the nonlinear difference-frequency excitation. This paper focuses on assessing the uncertainty of the CFD predictions from simulations of the semisubmersible in a fixed condition under bichromatic wave loading and on establishing confidence in the results for use in improving mid-fidelity models. The uncertainty for the nonlinear wave excitation is found to be acceptable but larger than that for the wave-frequency excitation, with the spatial discretization error being the dominant contributor. Further, unwanted free waves at the difference frequency have been identified in the CFD solution. A wave-splitting and wave load-correction procedure are presented to remove the contamination from the free waves in the results. A preliminary comparison to second-order potential-flow theory shows that the CFD model predicted significantly higher difference-frequency wave excitations, especially in surge, suggesting that the CFD results can be used to better calibrate the mid-fidelity tools.
The Prut River under Climate Change and Hydropower Impact
Authors: Corobov Roman , Ene Antoaneta , Trombitsky Ilya , Zubcov Elena
Published: December 2020

Climate change and intensive hydropower development pose serious challenges to the sustainable water supply of natural and social systems. This study’s purpose is to statistically estimate the complex influence of these factors on the Prut River flow. Its methodical approach is based on a comparative analysis of the flow in 1961–1990 and 1991–2018, reflecting the regional climate before and after an intensive global warming onset, and in 1950–1975 and 1980–2017, before and after the construction of the Stanca-Costesti hydropower plant (HPP) on the Prut’s riverbed. The compared statistics include annual, seasonal and monthly averages and trends of climatic and hydrological parameters for each period. Since the 1990s a statistically significant increase has been demonstrated in the basin-wide annual mean, maximum and minimum temperatures, of 1.04, 1.11 and 1.21 °C, respectively. Negative trends of annual precipitation were negligibly small and statistically insignificant in both periods (−2.1 mm and −0.6 mm/year, respectively), with annual totals differing by 1.4 mm. The seasonal transformation and changes of the Prut river flow, showing a small annual increase in the period 1961–1990 and a 1.8% decrease over next decades, are in good agreement with variability patterns of temperature (positive tendencies) and precipitation (negative tendencies). The operation of mean-capacity HPP has no significant impact on the total water discharge downstream, but modifies the seasonal distribution of the river’s streamflow.
Innovative Strategies for the Use of Reflective Foils for Fruit Colouration to Reduce Plastic Use in Orchards
Authors: Hess Patrick , Kunz Achim , Blanke Michael M.
Published: December 2020

(1) Background: Plastic in fruit orchards represents an environmental issue due to large CO2eq emissions associated with its production from fossil fuel and disposal (often incineration). (2) Materials and methods: Apple cv. “Braeburn Hillwell” trees on M9 rootstocks under a hail net were used at Campus Klein-Altendorf (CKA), Germany (50 °N) in 2018. In order to reduce the use of plastics to improve the red colouration of fruit particularly under hail nets, three alternatives to the current use of reflective mulch in each alleyway between the tree rows were explored, with uncovered grass alleyways as control. About 2800 colour measurements were done in the four weeks prior to harvest on 720 attached fruit below and above 1 m height in the field, and ca. 6900 additional colour measurements were conducted at harvest. (3) Results: The underlying regulatory mechanisms contrasted between the diffusive reflection of the white woven ground cover (such as LumilysTM or ExtendayTM) in the alleyways and aluminium foil under the trees with regular (straight) light reflection. Good fruit colouring and a plastic reduction were achieved (a) through spreading the white woven ground cover in every other row, and (b) through substituting the white ground cover with aluminium foil (80% recycled). Both methods can reduce greenhouse gas (GHG) emissions (75–110 kg CO2eq/ha for the first option a). (4) Conclusion: Plastic use in fruit orchards can be reduced by multiple use of the material in the same or several years, spreading it in every other row or substituting it by another reflective material, a relevant step towards an environment-friendly sustainable horticulture.
Examining the Factors Affecting SME Performance: The Mediating Role of Social Media Adoption
Authors: Ali Qalati Sikandar , Li Wenyuan , Ahmed Naveed , Ali Mirani Manzoor , Khan Asadullah
Published: December 2020

Small and medium enterprises (SMEs) have become a vibrant and dynamic sector of the world economy. Information technology plays a vital role in improving the productivity and competitiveness of SMEs. The dynamic business environment has brought fierce competition among SMEs and, therefore, requires the owners to interact with internal and external members actively. Hence, this study aims to investigate the impact of technology, organization, and environment as important factors in the performance of small and medium-sized enterprises. It also examines the mediating role of social media adoption. Items were developed to measure the various purposes of social media use in organizations, which contribute to increasing the measurement of social media usage. For the empirical investigation, this paper used a closed-ended questionnaire. Using a quantitative method, we observed 423 responses through structural equation modeling. The findings of the study indicate that technology, organization, and the environment play effective roles for SME performance. More importantly, social media adoption positively mediates the relationship between technology, organization, environment, and SME performance. The study also helps organizations realize the advantages of using social media and specifies the rationale behind an organization’s investment in social media.
What Does Gender Yield Gap Tell Us about Smallholder Farming in Developing Countries?
Authors: Nchanji Eileen B. , Collins Odhiambo A. , Katungi Enid , Nduguru Agness , Kabungo Catherine , Njuguna Esther M. , Ojiewo Chris O.
Published: December 2020

This study examines the extent of the productivity gap between male and female bean producers, its discriminatory nature and implications for the policymakers in agriculture in Tanzania. Generally, women are distinctively “invisible” in agriculture, due to social norms and even from the national agricultural policy perspective. Their discrimination arises from uncounted and unaccounted for farm work, and their productivity is reduced by triple roles, limited access to education, having triple effects on access to technology, training and land rights. In research, issues of concern to them such as nutritious food crops, varietal selection on important attributes, household food security, convenient home storage and small-scale processing are widely ignored through unfavourable policy design. Given the above discriminatory issues surrounding women in agriculture, they are hypothesised to be less productive and often lag behind male counterparts in crop production. To test the above hypothesis, a three-stage stratified sampling method was used to collect cross-sectional data in 2016 across four regions of Tanzania. Then, an Oaxaca-Blinder decomposition method (at means) was used to apportion the sources of the difference between men and women into explained and unexplained variations. Further improvements through the newly developed Re-Centered Influence Functions (RIFs) remarkably improved outcomes as the differences were analysed through unconditional partial effects on quantiles. Using a counterfactual approach and correcting for selection bias, the model provided consistent estimates for easy comparison of the two groups. Besides this, it emerged that interventions such as providing improved bean seed varieties and training farmers on good agricultural practices reduced the gender yield gap and provided a potential avenue for addressing the discrimination observed in productivity among males and females. Controlling for selection bias also improved the model, but the real discrimination was observed at the 50th percentile, where the majority of the respondents lay within. However, if a female’s age, family size, additional years of schooling and discretion to spend income from beans were taken away, they would be worse off. Our study finds that females comprised 25 percent of the sample, had 6 percent lower productivity, provided 64.70 percent on-farm labour and had 0.32 hectares less land compared to males, ceteris paribus. Access to improved varieties contributed to a 35.4 percent improved productivity compared to growing indigenous/local varieties. The implication is that the gender yield gap can be reduced significantly if efforts are focused on preventing or correcting factors causing discrimination against women.
Why Pandemics, Such as COVID-19, Require a Metropolitan Response
Authors: Angel Shlomo , Blei Alejandro
Published: December 2020

We introduce evidence from the COVID-19 pandemic in the United States that lends support to future political efforts to include multi-county metropolitan areas as an additional and critical institutional layer—over and above municipalities, countries, states, or the federal government—for the effective management of present and future pandemics. Multi-county metropolitan statistical areas (MSAs) accommodated 73% of the U.S. population and, as of 27 September 2020, they were home to 78% of reported cases of COVID-19 and 82% of reported deaths. The rationale for a renewed focus on these spatial units is that they are found to be densely interconnected yet easily identifiable locales for the spread of pandemics and, therefore, for their proper management as well. The paper uses available data on cases and deaths in U.S. counties as of 27 September 2020 to lend statistical support to four hypotheses: (1) The Onset Hypothesis: The onset of COVID-19 cases and deaths commenced earlier in multi-county metropolitan areas than in small-city counties or rural counties; (2) The Peak Hypothesis: The current peak of COVID-19 cases and deaths occurred earlier in multi-county metropolitan areas; (3) The Scaling Hypothesis: Multi-county metropolitan areas had more than their shares of COVID-19 cases and deaths than their shares in the population; and (4) The Neighbor Hypothesis: Levels of COVID-19 cases and deaths in counties within multi-county metropolitan areas were more strongly related to respective levels in their neighboring counties than small-city counties or rural counties. The reported statistical results demonstrate the value of adopting a metropolitan perspective on pandemics and working to empower effective institutional arrangements at the metropolitan level for managing the present and future pandemics.
Roadmap for Innovators in the Process of Innovation for Development
Authors: Koottatep Thammarat , Sukavejworakit Krisakorn , Virasa Thanaphol
Published: December 2020

This study aims to provide valuable insights into the process of innovation for development. We selected the “Reinvent the Toilet Challenge”, an initiative of the Gates Foundation, with the objective of creating sustainable sanitation solutions for the 2.5 billion people across the globe who lack access to safe and affordable sanitation. The Naturally Acceptable and Technologically Sustainable (NATS) team at the Asian Institute of Technology (AIT), Thailand was appointed by the Gates Foundation to serve as an innovative fecal sludge management (FSM) hub in Southeast Asia and collaborate with other researchers in the region, as well as with other teams from around the world to develop innovative FSM techniques that can help to solve the world sanitation problem. By gaining insights into how innovators interact with key stakeholders, we can understand the process of innovation for development and the role of innovation brokers in the innovation process, and then suggest a roadmap from the perspective of responsible research and innovation (RRI) to guide innovators, project leaders, industry partners, local government, and policy makers in the process of innovation for development.
Effectiveness of Focal Loss for Minority Classification in Network Intrusion Detection Systems
Authors: Mulyanto Mulyanto , Faisal Muhamad , Prakosa Setya Widyawan , Leu Jenq-Shiou
Published: December 2020

As the rapid development of information and communication technology systems offers limitless access to data, the risk of malicious violations increases. A network intrusion detection system (NIDS) is used to prevent violations, and several algorithms, such as shallow machine learning and deep neural network (DNN), have previously been explored. However, intrusion detection with imbalanced data has usually been neglected. In this paper, a cost-sensitive neural network based on focal loss, called the focal loss network intrusion detection system (FL-NIDS), is proposed to overcome the imbalanced data problem. FL-NIDS was applied using DNN and convolutional neural network (CNN) to evaluate three benchmark intrusion detection datasets that suffer from imbalanced distributions: NSL-KDD, UNSW-NB15, and Bot-IoT. The results showed that the proposed algorithm using FL-NIDS in DNN and CNN architecture increased the detection of intrusions in imbalanced datasets compared to vanilla DNN and CNN in both binary and multiclass classifications.
A Decomposition Method for a Fractional-Order Multi-Dimensional Telegraph Equation via the Elzaki Transform
Authors: Shah Nehad Ali , Dassios Ioannis , Chung Jae Dong
Published: December 2020

In this article, the Elzaki decomposition method is used to evaluate the solution of fractional-order telegraph equations. The approximate analytical solution is obtained within the Caputo derivative operator. The examples are provided as a solution to illustrate the feasibility of the proposed methodology. The result of the proposed method and the exact solution is shown and analyzed with figures help. The analytical strategy generates the series form solution, with less computational work and a fast convergence rate to the exact solutions. The obtained results have shown a useful and straightforward procedure to analyze the problems in related areas of science and technology.
An RBF-Based h-Adaptive Cartesian Grid Refinement Method for Arbitrary Single/Multi-Body Hull Modeling and Reconstruction
Authors: Ma Lin , Li Tingqiu
Published: December 2020

Complex single/multi-body structures are generally found in ship and ocean engineering. They have the smooth, sharp, concave, and convex surface features in common. Precise modeling of the structures is the basis of numerical simulation. However, the most widely used explicit modeling method requires considerable manual operations. The result is also difficult to reproduce. Therefore, this paper presents a Radial basis function (RBF) based hierarchical (h-) adaptive Cartesian grid method. The RBF is introduced for arbitrary implicit modeling over the Cartesian framework. Thanks to its natural properties, the RBF is easy to use, highly automated, and only needs a set of scatter points for modeling. The block-based h-adaptive mesh refinement (AMR) combined with the RBF aims to enhance the local grid resolution. It accelerates the calculation of intersecting points compared with the uniform Cartesian grid. The accuracy, efficiency, and robustness of the proposed method are validated by the simulation of the 3D analytical ellipsoidal surface and the unclosed conic surface. To select suitable parameters, we thoroughly investigated the uncertainty factors including sample points, RBF functions, and h-AMR grids. The simulation results of the single/multi-body Wigley hull and KCS hull forms verified the proper selection of the factors and the feasibility of our method to solve practical problems.
Engineering Botulinum Neurotoxins for Enhanced Therapeutic Applications and Vaccine Development
Authors: Rasetti-Escargueil Christine , Popoff Michel R.
Published: December 2020

Botulinum neurotoxins (BoNTs) show increasing therapeutic applications ranging from treatment of locally paralyzed muscles to cosmetic benefits. At first, in the 1970s, BoNT was used for the treatment of strabismus, however, nowadays, BoNT has multiple medical applications including the treatment of muscle hyperactivity such as strabismus, dystonia, movement disorders, hemifacial spasm, essential tremor, tics, cervical dystonia, cerebral palsy, as well as secretory disorders (hyperhidrosis, sialorrhea) and pain syndromes such as chronic migraine. This review summarizes current knowledge related to engineering of botulinum toxins, with particular emphasis on their potential therapeutic applications for pain management and for retargeting to non-neuronal tissues. Advances in molecular biology have resulted in generating modified BoNTs with the potential to act in a variety of disorders, however, in addition to the modifications of well characterized toxinotypes, the diversity of the wild type BoNT toxinotypes or subtypes, provides the basis for innovative BoNT-based therapeutics and research tools. This expanding BoNT superfamily forms the foundation for new toxins candidates in a wider range of therapeutic options.
Crouching Tiger, Hidden Protein: Searching for Insecticidal Toxins in Venom of the Red Tiger Assassin Bug (Havinthus rufovarius)
Authors: Wait Laura C. , Walker Andrew A. , King Glenn F.
Published: December 2020

Assassin bugs are venomous insects that prey on other arthropods. Their venom has lethal, paralytic, and liquifying effects when injected into prey, but the toxins responsible for these effects are unknown. To identify bioactive assassin bug toxins, venom was harvested from the red tiger assassin bug (Havinthus rufovarius), an Australian species whose venom has not previously been characterised. The venom was fractionated using reversed-phase high-performance liquid chromatography, and four fractions were found to cause paralysis and death when injected into sheep blowflies (Lucilia cuprina). The amino acid sequences of the major proteins in two of these fractions were elucidated by comparing liquid chromatography/tandem mass spectrometry data with a translated venom-gland transcriptome. The most abundant components were identified as a solitary 12.8 kDa CUB (complement C1r/C1s, Uegf, Bmp1) domain protein and a 9.5 kDa cystatin. CUB domains are present in multidomain proteins with diverse functions, including insect proteases. Although solitary CUB domain proteins have been reported to exist in other heteropteran venoms, such as that of the bee killer assassin bug Pristhesancus plagipennis, their function is unknown, and they have not previously been reported as lethal or paralysis-inducing. Cystatins occur in the venoms of spiders and snakes, but again with an unknown function. Reduction and alkylation experiments revealed that the H. rufovarius venom cystatin featured five cysteine residues, one of which featured a free sulfhydryl group. These data suggest that solitary CUB domain proteins and/or cystatins may contribute to the insecticidal activity of assassin bug venom.
Respiratory Epithelial Cells Respond to Lactobacillus plantarum but Provide No Cross-Protection against Virus-Induced Inflammation
Authors: Mai Eric , Percopo Caroline M. , Limkar Ajinkya R. , Sek Albert C. , Ma Michelle , Rosenberg Helene F.
Published: December 2020

Virus-induced inflammation plays a critical role in determining the clinical outcome of an acute respiratory virus infection. We have shown previously that the administration of immunobiotic Lactobacillus plantarum (Lp) directly to the respiratory tract prevents lethal inflammatory responses to subsequent infection with a mouse respiratory virus pathogen. While Lp-mediated protective responses involve non-redundant contributions of both Toll-like receptor 2 (TLR2) and NOD2, the cellular basis of these findings remains unclear. Here, we address the impact of Lp and its capacity to suppress inflammation in virus-infected respiratory epithelial cells in two cell culture models. We found that both MLE-12 cells and polarized mouse tracheal epithelial cells (mTECs) were susceptible to infection with Influenza A and released proinflammatory cytokines, including CCL2, CCL5, CXCL1, and CXCL10, in response to replicating virus. MLE-12 cells express NOD2 (81 ± 6.3%) and TLR2 (19 ± 4%), respond to Lp, and are TLR2-specific, but not NOD2-specific, biochemical agonists. By contrast, we found that mTECs express NOD2 (81 ± 17%) but minimal TLR2 (0.93 ± 0.58%); nonetheless, mTECs respond to Lp and the TLR2 agonist, Pam2CSK4, but not NOD2 agonists or the bifunctional TLR2-NOD2 agonist, CL-429. Although MLE-12 cells and mTECS were both activated by Lp, little to no cytokine suppression was observed in response to Lp followed by virus infection via a protocol that replicated experimental conditions that were effective in vivo. Further study and a more complex approach may be required to reveal critical factors that suppress virus-induced inflammatory responses.