bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial Intelligence Project Success Factors—Beyond the Ethical Principles
Authors: Miller Gloria J. 
Published: March 2022

AbstractThe algorithms implemented through artificial intelligence (AI) and big data projects are used in life-and-death situations. Despite research that addresses varying aspects of moral decision-making based upon algorithms, the definition of project success is less clear. Nevertheless, researchers place the burden of responsibility for ethical decisions on the developers of AI systems. This study used a systematic literature review to identify five categories of AI project success factors in 17 groups related to moral decision-making with algorithms. It translates AI ethical principles into practical project deliverables and actions that underpin the success of AI projects. It considers success over time by investigating the development, usage, and consequences of moral decision-making by algorithmic systems. Moreover, the review reveals and defines AI success factors within the project management literature. Project managers and sponsors can use the results during project planning and execution.
Association between migration status and subsequent labour market marginalisation among individuals with posttraumatic stress disorder: a Swedish nationwide register-based cohort study
Authors: He Jiangchuan , Hollander Anna-Clara , Rahman Syed 
Published: March 2022

Abstract
Purpose
The high prevalence of posttraumatic stress disorder (PTSD) among migrants in Europe is widely reported. Our research aimed to investigate the association between migration status and subsequent labour market marginalisation (LMM) events, i.e., long-term unemployment (LTU), long-term sickness absence (LTSA), and disability pension (DP) among individuals with PTSD, and to elucidate how the sociodemographic factors and the pre-existing health conditions influence such association.

Methods
We established a cohort of 36,714 individuals born between 1960 and 1995, living in Sweden during 2004–2009, aged 19 years or above, with PTSD diagnosis during 2006–2009. Migration status, categorized as refugees, non-refugees, second-generation migrants, and Swedish-born with Swedish-born parents (reference group) was considered as exposure and LMM events as outcome. The cohort was followed from 01-Jan-2010 until LMM, death, or end of follow-up (31-Dec-2016). Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated by Cox regression with a seven-year follow-up.

Results
Refugees (HR 2.07, 95% CI 1.86–2.30), and non-refugees (HR 1.96, 95% CI 1.85–2.07) had almost doubled relative risk of long-term unemployment, compared with the Swedish-born. The hazards of long-term sickness absence were similar across the groups. Refugees (HR 1.49, 95% CI 1.24–1.77), and non-refugees (HR 1.42, 95% CI 1.30–1.56) also had elevated relative risk of disability pension, whereas second-generation migrants had moderately increased relative risks for all three labour market marginalisation events compared with the Swedish-born.

Conclusion
Among the individuals with PTSD, being a migrant increases the risk of LMM, refugees being the foremost among migrants. Further research may benefit from including more recent migrant population, pre-migration information and measuring PTSD clinical severity.
Cross-validation of a semantic segmentation network for natural history collection specimens
Authors: de la Hidalga Abraham Nieva , Rosin Paul L. , Sun Xianfang , Livermore Laurence , Durrant James , Turner James , Dillen Mathias , Musson Alicia , Phillips Sarah , Groom Quentin , Hardisty Alex 
Published: March 2022

AbstractSemantic segmentation has been proposed as a tool to accelerate the processing of natural history collection images. However, developing a flexible and resilient segmentation network requires an approach for adaptation which allows processing different datasets with minimal training and validation. This paper presents a cross-validation approach designed to determine whether a semantic segmentation network possesses the flexibility required for application across different collections and institutions. Consequently, the specific objectives of cross-validating the semantic segmentation network are to (a) evaluate the effectiveness of the network for segmenting image sets derived from collections different from the one in which the network was initially trained on; and (b) test the adaptability of the segmentation network for use in other types of collections. The resilience to data variations from different institutions and the portability of the network across different types of collections are required to confirm its general applicability. The proposed validation method is tested on the Natural History Museum semantic segmentation network, designed to process entomological microscope slides. The proposed semantic segmentation network is evaluated through a series of cross-validation experiments designed to test using data from two types of collections: microscope slides (from three institutions) and herbarium sheets (from seven institutions). The main contribution of this work is the method, software and ground truth sets created for this cross-validation as they can be reused in testing similar segmentation proposals in the context of digitization of natural history collections. The cross-validation of segmentation methods should be a required step in the integration of such methods into image processing workflows for natural history collections.
When to operate after SARS-CoV-2 infection? A review on the recent consensus recommendation of the DGC/BDC and the DGAI/BDA
Authors: Noll J. , Reichert M. , Dietrich M. , Riedel J. G. , Hecker M. , Padberg W. , Weigand M. A. , Hecker A. 
Published: March 2022

Abstract
Since the eruption of the worldwide SARS-CoV-2 pandemic in late 2019/early 2020, multiple elective surgical interventions were postponed. Through pandemic measures, elective operation capacities were reduced in favour of intensive care treatment for critically ill SARS-CoV-2 patients. Although intermittent low-incidence infection rates allowed an increase in elective surgery, surgeons have to include long-term pulmonary and extrapulmonary complications of SARS-CoV-2 infections (especially “Long Covid”) in their perioperative management considerations and risk assessment procedures. This review summarizes recent consensus statements and recommendations regarding the timepoint for surgical intervention after SARS-CoV-2 infection released by respective German societies and professional representatives including DGC/BDC (Germany Society of Surgery/Professional Association of German Surgeons e.V.) and DGAI/BDA (Germany Society of Anesthesiology and Intensive Care Medicine/Professional Association of German Anesthesiologists e.V.) within the scope of the recent literature. The current literature reveals that patients with pre- and perioperative SARS-CoV-2 infection have a dramatically deteriorated postoperative outcome. Thereby, perioperative mortality is mainly caused by pulmonary and thromboembolic complications. Notably, perioperative mortality decreases to normal values over time depending on the duration of SARS-CoV-2 infection.
ApplianceNet: a neural network based framework to recognize daily life activities and behavior in smart home using smart plugs
Authors: Fahim Muhammad , Kazmi S. M. Ahsan , Khattak Asad Masood 
Published: March 2022

AbstractA smart plug can transform the typical electrical appliance into a smart multi-functional device, which can communicate over the Internet. It has the ability to report the energy consumption pattern of the attached appliance which offer the further analysis. Inside the home, smart plugs can be utilized to recognize daily life activities and behavior. These are the key elements to provide human-centered applications including healthcare services, power consumption footprints, and household appliance identification. In this research, we propose a novel framework ApplianceNet that is based on energy consumption patterns of home appliances attached to smart plugs. Our framework can process the collected univariate time-series data intelligently and classifies them using a multi-layer, feed-forward neural network. The performance of this approach is evaluated on publicly available real homes collected dataset. The experimental results have shown the ApplianceNet as an effective and practical solution for recognizing daily life activities and behavior. We measure the performance in terms of precision, recall, and F1-score, and the obtained score is 87%, 88%, 88%, respectively, which is 11% higher than the existing method in terms of F1-score. Furthermore, our scheme is simple and easy to adopt in the existing home infrastructure.
Pre-processing Waste Tomatoes into Separated Streams with the Intention of Recovering Protein: Towards an Integrated Fruit and Vegetable Biorefinery Approach to Waste Minimization
Authors: Baker Paul W. , Preskett Dave , Krienke Dominik , Runager Kasper S. , Hastrup Anne C. S. , Charlton Adam 
Published: March 2022

Abstract
Purpose
A large proportion of the European Union’s tomato crop is discarded during harvesting and there is a valorisation potential to recover proteins from this waste.

Methods
Cherry tomatoes were segregated into three separate components: juice, pomace (peels and skins), and seeds. The peels and skins, and seeds were separately hydrolyzed with carbohydrases to determine whether protein recovery could be increased. In addition, a strategy to fractionate the seeds was developed using sequential washing of milled tomato seeds, followed by low-speed centrifugation to remove the denser seed hulls and to collect the protein rich kernels remaining in suspension.

Results
The protein content of the seeds was highest with 27.4% while the peels and skins contained 7.6%. Carbohydrase mediated hydrolysis resulted in a minor increase in protein recovery of 10% from seeds using Filta 02L (cellulase, xylanase and β-glucanase), and the quantity of protein recovered from peels and skins increased by 210% using Tail 157 (pectinase, hemicellulase). The strategy to separate the seeds into two fractions, revealed that a higher proportion of the fibre (65%) was associated with the hull fraction compared with the original seeds (47%). A significant proportion of the fibre in this fraction was composed of lignin although the protein contents between both fractions was similar ranging from 27.4 to 29.9%.

Conclusions
These results reveal that carbohydrases were quite effective in protein extraction from peels and skins, but not from seeds. An alternative strategy was developed to remove the seed hulls from the milled seeds and to collect a fraction containing protein and dietary fibre where oil could be removed at this stage.

Graphical Abstract
Mapping human papillomavirus, Epstein–Barr virus, cytomegalovirus, adenovirus, and p16 in laryngeal cancer
Authors: Schindele Alexandra , Holm Anna , Nylander Karin , Allard Annika , Olofsson Katarina 
Published: March 2022

Abstract
Purpose
Apart from tobacco and alcohol, viral infections are proposed as risk factors for laryngeal cancer. The occurrence of oncogenic viruses including human papilloma virus (HPV) and Epstein–Barr virus (EBV), in laryngeal squamous cell carcinoma (LSCC) varies in the world. Carcinogenesis is a multi-step process, and the role of viruses in LSCC progression has not been clarified. We aimed to analyze the presence and co-expression of HPV, EBV, human cytomegalovirus (HCMV) and human adenovirus (HAdV) in LSCC. We also investigated if p16 can act as surrogate marker for HPV in LSCC.

Methods
Combined PCR/microarrays (PapilloCheck®) were used for detection and genotyping of HPV DNA, real-time PCR for EBV, HCMV and HAdV DNA detection, and EBER in situ hybridization (EBER-ISH) for EBV detection in tissue from 78 LSCC patients. Additionally, we analyzed p16 expression with immunohistochemistry.

Results
Thirty-three percent (26/78) of LSCC tumor samples were EBV positive, 9% (7/78) HCMV positive and 4% (3/78) HAdV positive. Due to DNA fragmentation, 45 samples could not be analyzed with PapilloCheck®; 9% of the remaining (3/33) were high-risk HPV16 positive and also over-expressed p16. A total of 14% (11/78) of the samples over-expressed p16.

Conclusion
These findings present a mapping of HPV, EBV, HCMV and HAdV, including the HPV surrogate marker p16, in LSCC in this cohort. Except for EBV, which was detected in a third of the samples, data show viral infection to be uncommon, and that p16 does not appear to be a specific surrogate marker for high-risk HPV infection in LSCC.
Estimation Method of Parking Space Conditions Using Multiple 3D-LiDARs
Authors: Yamada Shunya , Watanabe Yousuke , Kanamori Ryo , Sato Kenya , Takada Hiroaki 
Published: March 2022

AbstractIn the early stages of the spread of autonomous vehicles, it is conceivable to operate an automated valet parking system in parking lots where autonomous vehicles and non-autonomous vehicles coexist. Since non-autonomous vehicles may park beyond the parking space, it is necessary to estimate parking space conditions three-dimensionally. This paper proposes a method to estimate the parking space conditions using multiple 3D-LiDARs that can detect the space three-dimensionally. In the evaluation experiment, multiple 3D-LiDARs were installed in the parking lot of a public facility, and the estimation accuracy of the proposed method was evaluated in various situations.
GIS-based multi-criteria approach to delineate groundwater prospect zone and its sensitivity analysis
Authors: Kumar Mukesh , Singh Sudhir Kumar , Kundu Arnab , Tyagi Krishan , Menon Jagadeesh , Frederick Alex , Raj Aditya , Lal Deepak 
Published: March 2022

AbstractThis study was aimed at delineating groundwater potential zones (GWPZ) using geo-spatial techniques for Ranchi district, Jharkhand (India). Data including Cartosat-1 digital elevation model (DEM), Landsat 8 satellite images, lithology, geology, soil, and water yield data were utilised in this study. The relative importance of multiple parameters including lithology, soil, slope, geology, rainfall, drainage density, and land use/land cover (LULC) that influence the availability of groundwater was determined subjectively. Analytical hierarchy process (AHP) along with pair-wise comparison decision theory was utilized to calculate the weights for each aforementioned parameter. The delineated GWPZ were categorized into four classes viz., very good zone (31.57%), good zone (45.43%), moderate zone (13.09%), and poor zone (8.53%). The sensitivity analysis indicated lithology and soil type as the most and least sensitive parameters, respectively influencing the presence of groundwater in the study area. Comparison between well discharge data and delineated GWPZ yielded a coefficient of determination (R2) of 0.59. This study contributes to identifying priority areas where appropriate water conservation programs as well as strategies for sustainable groundwater development can be implemented.
Trivariate joint frequency analysis of water resources deficiency signatures using vine copulas
Authors: Nazeri Tahroudi Mohammad , Ramezani Yousef , De Michele Carlo , Mirabbasi Rasoul 
Published: March 2022

AbstractInvestigating the interaction of water resources such as rainfall, river flow and groundwater level can be useful to know the behavior of water balance in a basin. In this study, using the rainfall, river flow and groundwater level deficiency signatures for a 60-day duration, accuracy of vine copulas was investigated by joint frequency analysis. First, while investigating correlation of pair-variables, tree sequences of C-, D- and R-vine copulas were investigated. The results were evaluated using AIC, Log likelihood and BIC statistics. Finally, according to the physics of the problem and evaluation criteria, D-vine copula was selected as the best copula and the relevant tree sequence was introduced. Kendall’s tau test was used to evaluate the correlation of pair-signatures. The results of the Kendall’s tau test showed that pair-signatures studied have a good correlation. Using D-vine copula and its conditional structure, the joint return period of groundwater deficiency signature affected by rainfall and river flow deficiency signatures was investigated. The results showed that the main changes in the groundwater level deficiency is between 0.3 and 2 m, which due to the rainfall and the corresponding river flow deficiency, return periods will be less than 5 years. Copula-based simulations were used to investigate the best copula accuracy in joint frequency analysis of the studied signatures. Using copula data of the studied signatures, the groundwater deficiency signature was simulated using D-vine copula and a selected tree sequence. The results showed acceptable accuracy of D-vine copula in simulating the copula values of the groundwater deficiency signature. After confirming the accuracy of D-vine copula, the probability of occurrence of groundwater deficiency signature was obtained from the joint probability of occurrence of other signatures. This method can be used as a general drought monitoring system for better water resources management in the basin.
Review on generating optimal operation for dam and reservoir water system: simulation models and optimization algorithms
Authors: Saab Saad Mawlood , Othman Faridah Binti , Tan Chee Ghuan , Allawi Mohammed Falah , El-Shafie Ahmed 
Published: March 2022

AbstractAccurate and reliable optimization and simulation of the dam reservoir system to ensure optimal use of water resources cannot be achieved without precise and effective models. Providing insight into reservoir system operation and simulation modeling through a comprehensive overview of the previous studies and expanding research horizons can enhance the potential for accurate and well-designed models. The current research reviews previous studies that have used optimization methods to find optimal operating policies for a reservoir system over the past 20 years. Indeed, successful operating policies cannot be obtained without achieving accurate predictions of the main hydrological parameters in the reservoir system, which are inflow and evaporation. The present study focuses on giving an overview of the applications of AI-based models for predicting reservoir inflow and evaporation. The advantages and disadvantages of both optimization algorithms and predictive models have been summarized. Several recommendations for future research have also been included in the present review paper.
Groundwater quality evaluation: a case study of Igando waste dumpsite, southwestern Nigeria
Authors: Ige Olusegun O. , Owolabi Adebowale T. , Olabode Oluwaseun F. , Obasaju Daniel O. 
Published: March 2022

AbstractThe quality of groundwater samples around Igando area, Lagos State, Nigeria was investigated with the aim of detecting possible influence of dumpsites on nearby aquifers. Thirty-seven (37) groundwater samples were evaluated for their suitability in drinking and agricultural purposes. Physical parameters were determined in situ using portable multi-meter. Flame absorption was used to determine the concentrations of major cations while ion chromatography and titrimetry was used for anions’ concentration. Bacteriological test was conducted using multiple tube technique. Results of in-situ physical parameters revealed pH range of 7.01–7.79, EC range of 20–880 μS/cm while TDS range from 13–590 mg/L. Laboratory analyses showed Ca2+ concentration ranging 1.60–72.80 mg/L and HCO3− ranging 19.52–204.96 mg/L as the dominant ions. None of the ions’ concentrations exceed the WHO drinking limit. Water Quality Index (WQI) classification showed that all samples fall within good to excellent water categories. The majority of the samples are suitable for irrigation using indices such as Sodium Adsorption Ratio (SAR), Kelly’s ratio (KR), and Magnesium Hazard (MH). Bacteriological analyses revealed low total bacterial count and E-coli absence making the samples safe for drinking. Piper diagram indicated two main water-facies (Ca–HCO3 and the Mixed Ca–Mg–Cl). Gibbs’s plots showed that the samples are primarily influenced by rock weathering, while precipitation also contributed. Chloro-Alkaline Index confirmed direct ion-exchange process. Cluster analyses revealed four groups: the first revealed influence of groundwater by anthropogenic activities in the study area, the second and third revealed lithological origin of ions in the water while group 4 indicated pH low influence on the dissolved ions in the waters. The groundwater quality can be said to be fair-good, thus suggestive of minimal interactions between the dumpsites and the groundwater in the area at the time of testing.
Identification of groundwater potential zones in southern India using geospatial and decision-making approaches
Authors: Rajasekhar M. , Upendra B. , Raju G. Sudarsana , Anand  
Published: March 2022

AbstractThe present study was carried out to identify of the groundwater potential zones (GWPZ) in the northern part of the Anantapur district of Andhra Pradesh State, India using Remote Sensing (RS), Geographical information system (GIS), and Analytical Hierarchy Process (AHP) approaches. In this study, various thematic maps categorized viz. geomorphology (GM), lineament density (LD), drainage density (DD), geology, land use/land cover (LULC), soils, slope, and rainfall for assessment of GWPZs, which is generated using RS and GIS technique. Furthermore, the relative weights were allocated to various thematic maps using the AHP approach and the relative rank assigned to each sub-criterion based on expert advice. The combination of the eight thematic layers in ArcGIS resulted in a groundwater potential map, providing the information about very good 2.45% (87.06 km2), good 12.76 (452.56 km2), moderate 63.47% (2250.75 km2), poor 15.99% (567.16 km2), and very poor 5.32% (188.73 km2) groundwater possible zones. The acquired outcomes were validated with the area under the curve (AUC/ROC) method. The results show that there is a strong positive correlation between the GWPZs with 78% validation high performance and decreases to the low yield potential with poor areas. This study concludes that the AHP model will be a more reliable for the assessment of the GWP. Any groundwater management project carried out in these favourable regions would benefit the stack holders.
Experimental and numerical comparison of heat accumulation during laser powder bed fusion of 316L stainless steel
Authors: Chaudry Mohsin Ali , Mohr Gunther , Hilgenberg Kai 
Published: March 2022

AbstractHeat accumulation during laser powder bed fusion (LPBF) of metallic build parts can adversely affect their microstructure and mechanical properties. To study the heat accumulation during 316L steel based parts manufactured by LPBF, a finite element method (FEM) based numerical study is carried out. For the investigation, a computationally efficient FEM based model, where the whole layer is simultaneously exposed to a heat source, is used. The simulation results are compared with experimental results to validate the numerical model. While considering different influencing factors such as volumetric energy density (VED) and inter-layer time (ILT), the FEM model is shown to successfully simulate the process of heat accumulation during LPBF based manufacturing of a cuboidal shaped geometry. It is shown that ILT and VED have a significant effect on heat accumulation. The validated numerical model provides a good basis for the optimization of processing parameters and geometries for a future investigation of a reduction of heat accumulation effects. Furthermore, it can be used to quickly provide preheating boundary conditions for detailed investigations by different model approaches at a finer scale for future studies.