bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of a Triple Buffered Peptone Broth for Detection of Salmonella in Broiler Feed
Authors: Cosby Douglas E. , Berrang Mark E. , Hinton Arthur
Published: February 2023

The pH of pre-enrichment media containing feed/ingredients can become acidic during incubation due to bacterial utilization of feed carbohydrates. This decrease in pH can result in cell injury or death, negatively impacting the detection of Salmonella. Our objective was to evaluate a new triple buffered peptone (TBP) against buffered peptone water (BPW) and lactose broth (LB) for the recovery of Salmonella from feed. Liquid cultures of nalidixic acid resistant strains of Salmonella (Enteritidis, Heidelberg, Kentucky or Typhimurium) were added to the pre-enrichment media alone, to pre-enrichment media containing feed or to artificially inoculated feed stored 1 or 7 d to evaluate the effect of the medium on the recovery of Salmonella. Three replicates per treatment were conducted. After incubation at 37 °C for 24 h, the pH of the medium was measured prior to plating onto brilliant green sulfa agar plates supplemented with 200 ppm nalidixic acid (BGSNA). Plates were incubated and evaluated for presence of typical Salmonella colonies. The experiment was replicated. TBP was observed to exhibit significantly better buffering capacity than BPW or LB. Additionally, TBP was able to recover Salmonella 100% of the time compared to BPW (97.9%) and LB (61.5%). TBP shows promise to maintain neutral pH during pre-enrichment which may allow for a more accurate detection of Salmonella in feed.
The Significance of Tablet Internal Structure on Disintegration and Dissolution of Immediate-Release Formulas: A Review
Authors: Jange Camila G. , Wassgren Carl R. , Ambrose Kingsly
Published: February 2023

The internal microstructure of a tablet, such as pore geometry and pore volume, impacts the tablet’s disintegration kinetics. Ideally, one could design the microstructure to control dissolution onset and therapeutical performance of immediate-release formulas; however, manufacturing tablets with a desired microstructure can be challenging due to the interplay between formulation and process parameters. Direct quantification of tablet microstructure can provide a framework for optimizing composition and process parameters based on a Quality-by-Design approach. This article reviews the importance of tablet microstructure design and liquid transport kinetics to help optimize the release and dissolution profiles of immediate-release products. Additionally, the formulation and process parameters influencing the tablet microstructure and liquid transport kinetics are discussed.
Synthesis and Applications of Bimetallic-Derived Catalysts on Semiconductor Materials for Photoelectrocatalytic Processes
Authors: Torralba Encarnación , Bastide Stéphane
Published: February 2023

Photoelectrocatalysis takes advantage of electrocatalytic and photocatalytic processes [...]
Tree Segmentation and Parameter Measurement from Point Clouds Using Deep and Handcrafted Features
Authors: Wang Feiyu , Bryson Mitch
Published: February 2023

Accurate measurement of the geometric parameters of trees is a vital part of forest inventory in forestry management. Aerial and terrestrial Light Detection and Ranging (LiDAR) sensors are currently used in forest inventory as an effective and efficient means of forest data collection. Many recent approaches to processing and interpreting this data make use of supervised machine learning algorithms such as Deep Neural Networks (DNNs) due to their advantages in accuracy, robustness and the ability to adapt to new data and environments. In this paper, we develop new approaches to deep-learning-based forest point cloud analysis that address key issues in real applications in forests. Firstly, we develop a point cloud segmentation framework that identifies tree stem points in individual trees and is designed to improve performance when labelled training data are limited. To improve point cloud representation learning, we propose a handcrafted point cloud feature for semantic segmentation which plays a complementary role with DNNs in semantics extraction. Our handcrafted feature can be integrated with DNNs to improve segmentation performance. Additionally, we combine this feature with a semi-supervised and cross-dataset training process to effectively leverage unlabelled point cloud data during training. Secondly, we develop a supervised machine learning framework based on Recurrent Neural Networks (RNNs) that directly estimates the geometric parameters of individual tree stems (via a stacked cylinder model) from point clouds in a data-driven process, without the need for a separate procedure for model-fitting on points. The use of a one-stage deep learning algorithm for this task makes the process easily adaptable to new environments and datasets. To evaluate our methods for both the segmentation and parameter estimation tasks, we use four real-world datasets of different tree species collected using aerial and terrestrial LiDAR. For the segmentation task, we extensively evaluate our method on the three different settings of supervised, semi-supervised, and cross-dataset learning, and the experimental results indicate that both our handcrafted point cloud feature and our semi-supervised and cross-dataset learning framework can significantly improve tree segmentation performance under all three settings. For the tree parameter estimation task, our DNN-based method performs comparably to well-established traditional methods and opens up new avenues for DNN-based tree parameter estimation.
Dynamic Habitat Indices and Climatic Characteristics Explain Species Richness Patterns on the Mongolian Plateau
Authors: Liu Yingbin , Yang Yaping , Yue Xiafang , Chen Xiaona , Liu Yangxiaoyue
Published: February 2023

Global climate change affects biodiversity patterns, especially in arid and semi-arid regions such as the Mongolian plateau, one of the most ecologically fragile regions in the world. Three dynamic habitat indices (DHIs) were related to the productivity hypothesis and calculated based on FAPAR, including cumulative productivity (DHIcum indicates the availability of resources such as food supply and habitat in a year, representing available energy), minimum productivity (DHImin indicates the limitations of food and habitat resources in a year, representing environmental stress), and seasonal productivity (DHIsea denotes the change in productivity in a year, representing environmental stability). In this paper, we investigated the distribution pattern of species richness on the Mongolian Plateau based on the productivity hypothesis. We constructed models of the richness of three species (mammals, birds, and amphibians) using DHIs and climate variables to explain patterns of species richness on the Mongolian Plateau. The results revealed that, on the Mongolian plateau, there is a relatively high correlation between DHIs and species richness, especially with DHIcum (R = 0.59 for mammals, R = 0.73 for birds, and R = 0.58 for amphibians). There was a significant non-linear relationship between DHIs and species richness, as the model predictive power was significantly enhanced with GAM and RF. The inclusion of climate variables significantly improved the explanatory power of various models for the mammal, bird, and amphibian species richness on the Mongolian Plateau, with the best results for RF (0.89, 0.94, and 0.91, respectively). The influence of climate variables on species richness patterns in the importance ranking was higher than that of DHIs. Climate also has an influence on species richness. Vegetation productivity and climatic factors are good determinants of species richness on the Mongolian Plateau and should be carefully considered in future studies.
A Proposed Ensemble Feature Selection Method for Estimating Forest Aboveground Biomass from Multiple Satellite Data
Authors: Zhang Yuzhen , Liu Jingjing , Li Wenhao , Liang Shunlin
Published: February 2023

Feature selection (FS) can increase the accuracy of forest aboveground biomass (AGB) prediction from multiple satellite data and identify important predictors, but the role of FS in AGB estimation has not received sufficient attention. Here, we aimed to quantify the degree to which FS can benefit forest AGB prediction. To this end, we extracted a series of features from Landsat, Phased Array L-band Synthetic Aperture Radar (PALSAR), and climatic and topographical information, and evaluated the performance of four state-of-the-art FS methods in selecting predictive features and improving the estimation accuracy with selected features. We then proposed an ensemble FS method that takes inro account the stability of an individual FS algorithm with respect to different training datasets used; the heterogeneity or diversity of different FS methods; the correlations between features and forest AGB; and the multicollinearity between the selected features. We further investigated the performance of the proposed stability-heterogeneity-correlation-based ensemble (SHCE) method for AGB estimation. The results showed that selected features by SHCE provided a more accurate prediction of forest AGB than existing state-of-the-art FS methods, with R2 = 0.66 ± 0.01, RMSE = 14.35 ± 0.12 Mg ha−1, MAE = 9.34 ± 0.09 Mg ha−1, and bias = 1.67 ± 0.11 Mg ha−1 at 90 m resolution. Boruta yielded comparable prediction accuracy of forest AGB, but could not identify the importance of features, which led to a slightly greater bias than the proposed SHCE method. SHCE not only ranked selected features by importance but provided feature subsets that enabled accurate AGB prediction. Moreover, SHCE provides a flexible framework to combine FS results, which will be crucial in many scenarios, particularly the wide-area mapping of land-surface parameters from various satellite datasets.
An Optimization Method of Production-Distribution in Multi-Value-Chain
Authors: Wang Shihao , Zhang Jianxiong , Ding Xuefeng , Hu Dasha , Wang Baojian , Guo Bing , Tang Jun , Du Ke , Tang Chao , Jiang Yuming
Published: February 2023

Value chain collaboration management is an effective means for enterprises to reduce costs and increase efficiency to enhance competitiveness. Vertical and horizontal collaboration have received much attention, but the current collaboration model combining the two is weak in terms of task assignment and node collaboration constraints in the whole production-distribution process. Therefore, in the enterprise dynamic alliance, this paper models the MVC (multi-value-chain) collaboration process for the optimization needs of the MVC collaboration network in production-distribution and other aspects. Then a MVC collaboration network optimization model is constructed with the lowest total production-distribution cost as the optimization objective and with the delivery cycle and task quantity as the constraints. For the high-dimensional characteristics of the decision space in the multi-task, multi-production end, multi-distribution end, and multi-level inventory production-distribution scenario, a genetic algorithm is used to solve the MVC collaboration network optimization model and solve the problem of difficult collaboration of MVC collaboration network nodes by adjusting the constraints among genes. In view of the multi-level characteristics of the production-distribution scenario, two chromosome coding methods are proposed: staged coding and integrated coding. Moreover, an algorithm ERGA (enhanced roulette genetic algorithm) is proposed with enhanced elite retention based on a SGA (simple genetic algorithm). The comparative experiment results of SGA, SEGA (strengthen elitist genetic algorithm), ERGA, and the analysis of the population evolution process show that ERGA is superior to SGA and SEGA in terms of time cost and optimization results through the reasonable combination of coding methods and selection operators. Furthermore, ERGA has higher generality and can be adapted to solve MVC collaboration network optimization models in different production-distribution environments.
Effect of Hydrothermal Conditions on Kenaf-Based Carbon Quantum Dots Properties and Photocatalytic Degradation
Authors: Saafie Nabilah , Sambudi Nonni , Wirzal Mohd , Sufian Suriati
Published: February 2023

The development of biomass-based CQD is highly attentive to enhancing photocatalytic performance, especially in secondary or ternary heterogeneous photocatalysts by allowing for smooth electron-hole separation and migration. In this study, kenaf-based carbon quantum dots (CQD) were prepared. The main objective of the current work was to investigate temperature, precursor mass and time in hydrothermal synthesis treatment to improve the CQD properties and methylene blue photocatalytic degradation. Optimization of kenaf-based CQD for inclusion in hydrothermal treatment was analyzed. The as-prepared CQDs were characterized in detail by Fourier transform infrared (FTIR) spectroscopy, using a Hitachi TEM System (HT7830, RuliTEM, Tokyo, Japan), by photoluminescence (PL), and by ultraviolet-visible (UV-Vis) spectroscopy. It was found that C200-0.5-24 exhibits a higher photocatalytic activity of the methylene blue dye and optimized hydrothermal conditions of 200 °C, 0.5 g and 24 h. Therefore, novel kenaf-based CQD was synthesized for the first time and was successfully optimized in the as-mentioned conditions. During the hydrothermal treatment, precursor mass controls the size and the distribution of CQD nanoparticles formed. The C200-0.5-24 showed a clearly defined and well-distributed CQD with an optimized nanoparticle size of 8.1 ± 2.2 nm. Indeed, the C200-0.5-24 shows the removal rate of 90% of MB being removed within 120 min.
Does Civic Engagement Support Relational and Mental Health of Urban Population?
Authors: Hrivnák Michal , Moritz Peter , Melichová Katarína , Bellérová Soňa
Published: February 2023

There is a general assumption that there is a relationship between civic engagement and mental health, but it has still received limited attention in empirical studies. This study provides estimates of the impact of civic engagement (measured in terms of political and community engagement) on the health of individuals in the case of a medium-sized urban settlement within the context of a post-socialist country. The impacts of civic engagement on mental and relational health are distinguished, which have received little attention in studies on the topic. Using primary data and utilising the tools of econometrics, we found positive effects of the population’s community engagement, including positive effects of volunteering, on relational health. Political participation of the population contributed to the reduction of depressive symptoms, but the relationship between community engagement and mental health was not found. A relatively high propensity towards participation in health and well-being projects, leading to improvements in the collective approach to public health and addressing unhealthy conditions in communities, was identified in the sample.
The Influence of the Rebound Hammer Test Location on the Estimation of Compressive Strength of a Historical Solid Clay Brick
Authors: Mengistu Girum Mindaye , Gyurkó Zoltán , Nemes Rita
Published: February 2023

This paper presents the study of a two-hundred-year-old, demolded solid clay brick using the rebound hammer test for the estimation of the compressive strength. During the test, the location and face type influence on the rebound values are monitored and recorded. In addition, the calculation of the average rebound value has been modified to encounter the influence of location and face types. Furthermore, the estimated compressive strength is compared with the normalized mean compressive strength to check the accuracy of the rebound hammer test if it is within the confidential limit of ±25%. The result shows that the location and surface types have influence on the rebound value, which in turn affected the compressive strength.
Designing Lightweight Stadium Roofing Structures Based on Advanced Analysis Methods
Authors: Tahmasebinia Faham , Chen Eason , Huang Andy , Li Jesse
Published: February 2023

The current structural engineering practical standards are unable to offer an universal structural design standard for long-spanning lightweight stadium roofing structures. As such, the design procedure of a particular stadium roof is not replicable to another. This research aims to present a novel design procedure for lightweight stadium roofing structures considering the Lakhwiya stadium the Optus Stadium and the CommBank Stadium as experimental cases. Using the finite element analysis (FEA) software Strand7, the cases will be modelled and analysed. Varying load cases and combinations such as ultimate strength (ULS) and serviceability limit states (SLS) based on the Australian Standard AS1170.0:2002 will be calculated and subsequently applied. Linear static analysis will then be undertaken where critical members will be identified within the model. Based on this, preliminary member sizing and design feasibility checks will be conducted in order to ensure structural stability and compliance to the Australian Steel Structure code AS4100:2020. A linear buckling analysis is also conducted based on the selected sizes from the initial stage to determine critical loads. Advanced analysis including non-linear buckling computation is comprehensively managed. Some of the crucial parameters such as maximum displacement, maximum/minimum principal stresses, critical buckling loads, as well as load factors are examined. The main novelty of this study is to determine a clear road map to design stadium roofing systems subjected to a combination of different types of the loading.
Pollution and Risk Evaluation of Toxic Metals and Metalloid in Water Resources of San Jose, Occidental Mindoro, Philippines
Authors: Senoro Delia B. , De Jesus Kevin Lawrence M. , Monjardin Cris Edward F.
Published: February 2023

Clean and safe drinking water is an integral part of daily living and is considered as a basic human need. Hence, this study investigated the suitability of the domestic water (DW) and groundwater (GW) samples with respect to the presence of metals and metalloid (MMs) in San Jose, Occidental Mindoro, Philippines. The MMs analyzed in the area of study for DW and GW were Arsenic (As), Barium (Ba), Copper (Cu), Chromium (Cr), Iron (Fe), Lead (Pb), Manganese (Mn), Nickel (Ni), and Zinc (Zn). The results revealed that Pb has the mean highest concentration for DW, while Fe is in GW resources in the area. Quality evaluation of DW and GW was performed using Metal Pollution Index (MPI), Nemerow’s Pollution Index (NPI), and Ecological Risk Index (ERI). The mean NPI value calculated for DW was 135 times greater than the upper limit of the unpolluted location category. The highest NPI observed was 1080 times higher than the upper limit of the unpolluted site category. That of the ERI observed in the area was 23.8 times higher than the upper limit for a “low” ERI category. Furthermore, the health risk assessment (HRA) of the GW and DW of the study area revealed non-carcinogenic health risks of the MMs analyzed in GW samples, and potential carcinogenic health risks from As, Cr, Pb, and Ni in DW. The use of machine learning geostatistical interpolation (MLGI) mapping to illustrate the PI and health risk (HR) in the area was an efficient and dependable evaluation tool for assessing and identifying probable MMs pollution hotspots. The data, tools, and the process could be utilized in carrying out water assessment, the evaluation leading to a comprehensive water management program in the area and neighboring regions of similar conditions.
Locality in the Promoted Sustainability Practices of Michelin-Starred Restaurants
Authors: Huang Yuying , Hall C. Michael
Published: February 2023

Sustainable practices are increasingly promoted in the restaurant industry. One significant aspect of sustainability in restaurants is the use of local supply chains, especially for food, which also serve as a means for restaurants to promote freshness of produce, sourcing, and quality. Considering the prevalence of locality in menu marketing, this study aims to explore the relationships between sustainability and locality at fine-dining restaurants. Michelin-starred restaurants are significant influencers in the restaurant industry, as well as food fashions overall, and may therefore serve to promote sustainability practices. This study examines the sustainability of 135 Michelin three-star restaurants by conducting website content analysis. By identifying restaurants’ sustainable practices during the processes of procurement, preparation, and presentation and analysing the official websites of 135 Michelin three-star restaurants, this study finds that although all sustainable practices are mentioned by less than half of the reviewed websites, most practices could be interpreted as being embedded in their locality, especially local food and restaurant history. This study suggests that promoting locality could therefore help sustain sustainability in the fine-dining restaurant industry. Although this study is limited to the website content of official websites for Michelin three-star restaurants, it provides potentially valuable insights on the promotion of sustainable restaurant practices.
Vector Autoregression Model-Based Forecasting of Reference Evapotranspiration in Malaysia
Authors: Hou Phon Sheng , Fadzil Lokman Mohd , Manickam Selvakumar , Al-Shareeda Mahmood A.
Published: February 2023

Evapotranspiration is one of the hydrological cycle’s most important elements in water management across economic sectors. Critical applications in the agriculture domain include irrigation practice improvement and efficiency, as well as water resource preservation. The main objective of this research is to forecast reference evapotranspiration using the vector autoregression (VAR) model and investigate the meteorological variables’ causal relationship with reference evapotranspiration using a statistical approach. The acquired 20-year, 1-year, and 2-month research climate datasets from Penang, Malaysia, were split into 80% training data and 20% validation data. Public weather data are used to train the initial VAR model. A Raspberry Pi IoT device connected to a DHT11 temperature sensor was outfitted at the designated experimental crop site. In situ data acquisition was done using DHT11 temperature sensors to measure the ambient temperature and humidity. The collected temperature and humidity data were used in conjunction with the vector autoregression (VAR) model to calculate the reference evapotranspiration forecast. The results demonstrated that the 20-year dataset showed better performance and consistent results in forecasting general reference evapotranspiration, derived using root mean square error (RMSE) and correlation coefficient (CORR) of 1.1663 and −0.0048, respectively. As for the 1-year dataset model, RMSE and CORR were recorded at 1.571 and −0.3932, respectively. However, the 2-month dataset model demonstrated both positive and negative performance due to seasonal effects in Penang. The RMSE ranged between 0.5297 to 2.3562 in 2020, 0.8022 to 1.8539 in 2019, and 0.8022 to 2.0921 in 2018. As for CORR, it ranged between −0.5803 to 0.2825 in 2020, −0.3817 to 0.2714 in 2019, and −0.3817 to 0.2714 in 2018. In conclusion, the model tested using 20-year, 1-year, and 2-month meteorological datasets for estimating reference evapotranspiration (ET0) based on smaller RMSEs demonstrates better performance at predicting the true values, as well as producing both positive and negative CORR performance due to seasonal variations in Penang.
Flexural Wave Bandgaps in a Prestressed Multisupported Timoshenko Beam with Periodic Inerter-Based Dynamic Vibration Absorbers
Authors: Han Wenwen , Wan Shui
Published: February 2023

Locally resonant (LR) metamaterial structures possess bandgaps in which wave propagation is significantly attenuated. In this paper, we discuss flexural wave bandgaps in an LR beam subjected to a global axial force and multiple vertical elastic supports. An array of inerter-based dynamic vibration absorbers (IDVAs) was periodically attached to the LR beam. The flexural wave band structure of this prestressed multisupported LR beam was first derived using the transfer matrix method (TMM) and then explicitly illustrated through a numerical example. Four bandgaps were identified: a bandgap located in the low-frequency zone, a Bragg band generated by Bragg scattering, and two LR bands generated by the local resonance of the IDVAs. The effects of the IDVA parameters, axial force, and vertical elastic support on the properties of the bandgaps were evaluated. In particular, the bandgaps merged accompanied by an exchange of their edge frequencies. The bandwidth of the merged bandgap was nearly equal to the sum of the bandwidths of the bandgaps involved, indicating a method for controlling broadband flexural vibration through the bandgap splicing mechanism.
Multi-Objective Optimization for Ranking Waste Biomass Materials Based on Performance and Emission Parameters in a Pyrolysis Process—An AHP–TOPSIS Approach
Authors: Howari Haidar , Parvez Mohd , Khan Osama , Alhodaib Aiyeshah , Mallah Abdulrahman , Yahya Zeinebou
Published: February 2023

The current era of energy production from agricultural by-products comprises numerous criteria such as societal, economical, and environmental concerns, which is thought to be difficult, considering the complexities involved. Making the optimum choice among the various classes of organic waste substances with different physio-chemical characteristics based on their appropriateness for pyrolysis is made possible by a ranking system. By using a feasible model, which combines several attributes of decision-making processes, it is possible to select the ideal biomass feedstock from a small number of possibilities based on relevant traits that have an impact on the pyrolysis. In this study, a multi-criteria decision-making (MCDM) technique model based on the weight calculated from the analytical hierarchy process (AHP) tool has been applied to obtain a ranking of different types of agro-waste-derived biomass feedstock. The technique of order preference by similarity to ideal solution (TOPSIS) is used to examine the possibilities of using/utilizing locally available biomass. From this point of view, multi-criteria are explained to obtain yield maximum energy. The suggested approaches are supported by the experimental findings and exhibit a good correlation with one another. Six biomass alternatives and eight evaluation criteria are included in this study. Sawdust is the highest-ranking agricultural waste product with a closeness coefficient score of 0.9 out of the six biomass components that were chosen, followed by apple bagasse with 0.8. The hybrid approach model that has been built can be evaluated and validated for the ranking method using the Euclidian distance-based approximation. This study offers a unique perspective on decision-making, particularly concerning thermo-chemical conversion.
To Be a Brazilian City Dweller, Sometimes We Must Learn to Say Enough!
Authors: Souza Franco L. , Fenoglio María S. , Angeoletto Fabio
Published: February 2023

In several Brazilian localities, a local-scale problem can be detected regarding an absence of citizens compromising that is negatively associated with a greater engagement in public policies that could reflect, in the end, a better understanding of the importance of ecosystem services for their lives. Whatever the governance initiatives, by considering the neighborhoods’ boundaries and their particularities, they should be accompanied by a strong informative commitment to encourage the local population to break away from their harmful attitudes that result in bizarre idiosyncrasies associated with human–nature connections. The conservation agenda, sustainable developmental goals, or other similar targets seem to be unconnected with social demands at a more local scale, while local stakeholders find it difficult to spread some specific and important ideas at a wider governmental scale. Without these connections, also fomented by weak or absent proactive academic initiatives and governance, most citizens will continue to live in cities that, instead of offering a better quality of life, will only bring environmental problems, such as smoke from burning forests and vacant lots, public areas filled with domestic garbage, polluted rivers, animals killed on the roads, and zoonosis. It is time to change the idiosyncrasies of these Brazilian cities, acting as if they were only part of the urban landscape and as if society has nothing to do with their actions. It is time to say enough!
Performance Analysis Method for Robotic Process Automation
Authors: Quille Rosa , Almeida Felipe , Borycz Joshua , Corrêa Pedro , Filgueiras Lucia , Machicao Jeaneth , Almeida Gustavo , Midorikawa Edson , Demuner Vanessa , Bedoya John , Vajgel Bruna
Published: February 2023

Recent studies show that decision making in Business Process Management (BPM) and incorporating sustainability in business is vital for service innovation within a company. Likewise, it is also possible to save time and money in an automated, intelligent and sustainable way. Robotic Process Automation (RPA) is one solution that can help businesses improve their BPM and sustainability practices through digital transformation. However, deciding which processes to automate with RPA technology can be complex. Consequently, this paper presents a model for selecting indicators to determine the profitability of shifting to RPA in selected business processes. The method used in this work is the Performance Analysis Method, which allows for predicting which processes could be replaced by RPA to save time and money in a service workflow. The Performance Analysis Method consists of collecting data on the speed and efficiency of a business process and then using that data to develop discrete event simulations to estimate the cost of automating parts of that process. A case study using this model is presented, using business process data from an international utility company as input to the discrete event simulation. The model used in this study predicts that this Electric Utility Company (EUC) will save a substantial amount of money if it implements RPA in its call center.
Strategic Human Resources Management for Creating Shared Value in Social Business Organizations
Authors: Ferdousi Farhana , Abedin Nuren
Published: February 2023

Social business organizations work to achieve social and business goals to create shared value (CSV). Strategic human resource management (SHRM) is one of the key components of optimizing shared value. The available literature needs more evidence on how these organizations manage their human resources. This study aims to bridge the gap by examining the HRM approaches of social business in creating shared value for the organization. This study analyzed four social business cases through semi-structured interviews, field visits, and a review of archival documents and web-based reports and resources. As shown in the results, we found that formal recruitment processes are followed where candidates’ prior knowledge and interests in the social business are tested rigorously. Talented, qualified, and suitable candidates are selected without any biases. On-the-job training processes are followed with a rigorous focus on orienting employees to social and business goals. Key performance indicators (KPIs) are sets for evaluating employees’ social and business performance. Market rates are followed in providing compensation. Family-oriented and participatory work environment exists, which keeps their turnover low. High-performance HRM is recommended to achieve future growth and shared value optimization.
Reducing Emitter Clogging in Drip Fertigation Systems by Magnetization Technology
Authors: Shi Kaili , Zhangzhong Lili , Han Furong , Zhang Shirui , Guo Rui , Yao Xueying
Published: February 2023

The issue of emitter clogging is a common phenomenon in drip fertigation systems, which causes uneven irrigation and fertilization. However, efficient and environmentally friendly methods are scarcely available for alleviating clogging. In the present study, we investigated the anti-fouling efficacy of three magnetic field strength levels (0, 0.4 T and 0.6 T) on emitter clogging in drip fertigation systems. Our results show that magnetized water treatment could effectively relieve emitter clogging and delay the occurrence time of clogging, which increased the average discharge variation rate (Dra) by 37.00–61.64% and decreased the dry weight (DW) by 53.00–69.29% compared with non-magnetized water treatments. X-rays were used to estimate the compositions of the main clogging substances, and the results show that phosphates were the dominant substances that induced emitter clogging. In addition, magnetized water treatment effectively reduced the contents of chemical and particulate fouling, as exhibited by a decrease in phosphates, silicate and quartz by 53.17–69.58%, 47.22–61.95% and 43.18–74.80%, respectively. In comparison, the higher strength of magnetized water treatment (0.6 T) was better in clogging control, which increased Dra and the Christiansen of uniformity (CU) by 24.64% and 43.96%, respectively, and the DW was reduced by 34.67% compared with that of 0.4 T. This study proves that magnetized water treatment is an effective, chemical-free treatment method with great potential for fouling control technology, and it is helpful for the further promotion of drip fertigation technology.
QCD Matter and Phase Transitions under Extreme Conditions
Authors: Huang Mei , Zhuang Pengfei
Published: February 2023

The interplay of chiral dynamics and gluodynamics plays an essential role in the nonperturbative QCD region, and the chiral phase transition and deconfinement phase transition are the main topics of QCD phase transitions under extreme conditions, e.g., finite temperature and/or baryon density, strong magnetic fields, and fast rotation. We briefly introduce our own recent progress from the theoretical side on QCD phase transitions and phase diagrams under extreme conditions related to the early universe, compact stars, and heavy-ion collisions.
Analysis of the Competition System Using Parameterized Fractional Differential Equations: Application to Real Data
Authors: DarAssi Mahmoud H. , Khan Muhammad Altaf , Fatmawati  , Alqarni Marei Saeed
Published: February 2023

Natural symmetries exist in several processes of chemistry, physics, and biology. Symmetries possess interesting dynamical characteristics that cannot be seen in non-symmetric systems. The present paper investigates the competition between two banking systems, rural and commercial, in Indonesia, in parameterized fractional order Caputo derivative. A novel numerical method is used to discretize the competition system using the real data of rural and commercial banks in Indonesia for the period 2004–2014. The new scheme is more suitable and reliable for data fitting results and has good accuracy. The integer model is formulated in Caputo derivative and their stability results are presented. With the available parameters, the data for the model is analyzed using various scenarios. We shall compare the result with the previous method used in the literature and show that the present method is better than the previous method in the literature. It is shown that fractional order α and the parameter ρ involved in the numerical scheme provide excellent fitting.
Repeatability and Reproducibility Uncertainty in Magnetic Resonance-Based Electric Properties Tomography of a Homogeneous Phantom
Authors: Arduino Alessandro , Pennecchi Francesca , Katscher Ulrich , Cox Maurice , Zilberti Luca
Published: February 2023

Uncertainty assessment is a fundamental step in quantitative magnetic resonance imaging because it makes comparable, in a strict metrological sense, the results of different scans, for example during a longitudinal study. Magnetic resonance-based electric properties tomography (EPT) is a quantitative imaging technique that retrieves, non-invasively, a map of the electric properties inside a human body. Although EPT has been used in some early clinical studies, a rigorous experimental assessment of the associated uncertainty has not yet been performed. This paper aims at evaluating the repeatability and reproducibility uncertainties in phase-based Helmholtz-EPT applied on homogeneous phantom data acquired with a clinical 3 T scanner. The law of propagation of uncertainty is used to evaluate the uncertainty in the estimated conductivity values starting from the uncertainty in the acquired scans, which is quantified through a robust James–Stein shrinkage estimator to deal with the dimensionality of the problem. Repeatable errors are detected in the estimated conductivity maps and are quantified for various values of the tunable parameters of the EPT implementation. The spatial dispersion of the estimated electric conductivity maps is found to be a good approximation of the reproducibility uncertainty, evaluated by changing the position of the phantom after each scan. The results underpin the use of the average conductivity (calculated by weighting the local conductivity values by their uncertainty and taking into account the spatial correlation) as an estimate of the conductivity of the homogeneous phantom.
Estrogenic Effect Mechanism and Influencing Factors for Transformation Product Dimer Formed in Preservative Parabens Photolysis
Authors: Niu Xiaolin , Chen Guanhui , Chen Yi , Luo Na , Wang Mei , Hu Xinyi , Gao Yanpeng , Ji Yuemeng , An Taicheng
Published: February 2023

The environmental transformation and health effects of endocrine disruptors (EDCs) need urgent attention, particularly the formation of transformation products with higher toxicity than parent EDCs. In this paper, an important transformation product dimer (short for ethyl 4-hydroxy-3-(2-((4-hydroxybenzoyl) oxy) ethyl) benzoate) with estrogenic activity was investigated and detected in the photolysis of preservative ethyl-paraben (EPB) dissolved in actual water. The environmental factors, such as the higher initial concentration of EPB, the stronger optical power and the lower pH could stimulate the formation of the dimer. Simultaneously, the interaction of multiple environmental factors was significant, especially the initial concentration and pH using the response surface methodology. Furthermore, the relationship between the environmental factors and the formation of the product dimer was further explained and the empirical model equation was built for predicting the amount of dimer in actual water. Quantum chemical and toxicological calculations showed the estrogenic effect mechanism of the product dimer and it was revealed further that the hydrogen bonds of the dimer and ERα proteins (ARG-394, Glu-353, His-524, GYY-521) were formed, with a lowest binding energy of −8.38 Kcal/mol during molecular docking. In addition, the health effect risk of the product dimer was higher than the parent compound in the blood, cardiovascular system, gastrointestinal system, kidney and liver. In short, the present study was of great significance for the transformation product in pollution control and health effects in the photolysis of EDCs.
Cosmological Constant from Boundary Condition and Its Implications beyond the Standard Model
Authors: Stenflo Jan O.
Published: February 2023

Standard cosmology has long been plagued by a number of persistent problems. The origin of the apparent acceleration of the cosmic expansion remains enigmatic. The cosmological constant has been reintroduced as a free parameter with a value in energy density units that “happens” to be of the same order as the present matter energy density. There is an internal inconsistency with regards to the Hubble constant, the so-called H0 tension. The derived value of H0 depends on the type of data that is used. With supernovae as standard candles, one gets a H0 that is 4–5 σ larger than the value that one gets from CMB (Cosmic Microwave Background) data for the early universe. Here we show that these problems are related and can be solved if the cosmological constant represents a covariant integration constant that arises from a spatial boundary condition, instead of being a new type of hypothetical physical field, “dark energy”, as assumed by standard cosmology. The boundary condition only applies to the bounded 3D subspace that represents the observable universe, the hypersurface of the past light cone.
SARS-CoV-2-Induced TSLP Is Associated with Duration of Hospital Stay in COVID-19 Patients
Authors: Gerla Luke , Moitra Subhabrata , Pink Desmond , Govindasamy Natasha , Duchesne Marc , Reklow Eileen , Hillaby Angela , May Amy , Lewis John D. , Melenka Lyle , Hobman Tom C. , Mayers Irvin , Lacy Paige
Published: February 2023

Thymic stromal lymphopoietin (TSLP) is an epithelium-derived pro-inflammatory cytokine involved in lung inflammatory responses. Previous studies show conflicting observations in blood TSLP in COVID-19, while none report SARS-CoV-2 inducing TSLP expression in bronchial epithelial cells. Our objective in this study was to determine whether TSLP levels increase in COVID-19 patients and if SARS-CoV-2 induces TSLP expression in bronchial epithelial cells. Plasma cytokine levels were measured in patients hospitalized with confirmed COVID-19 and age- and sex-matched healthy controls. Demographic and clinical information from COVID-19 patients was collected. We determined associations between plasma TSLP and clinical parameters using Poisson regression. Cultured human nasal (HNEpC) and bronchial epithelial cells (NHBEs), Caco-2 cells, and patient-derived bronchial epithelial cells (HBECs) obtained from elective bronchoscopy were infected in vitro with SARS-CoV-2, and secretion as well as intracellular expression of TSLP was detected by immunofluorescence. Increased TSLP levels were detected in the plasma of hospitalized COVID-19 patients (603.4 ± 75.4 vs 997.6 ± 241.4 fg/mL, mean ± SEM), the levels of which correlated with duration of stay in hospital (β: 0.11; 95% confidence interval (CI): 0.01–0.21). In cultured NHBE and HBECs but not HNEpCs or Caco-2 cells, TSLP levels were significantly elevated after 24 h post-infection with SARS-CoV-2 (p < 0.001) in a dose-dependent manner. Plasma TSLP in COVID-19 patients significantly correlated with duration of hospitalization, while SARS-CoV-2 induced TSLP secretion from bronchial epithelial cells in vitro. Based on our findings, TSLP may be considered an important therapeutic target for COVID-19 treatment.
“What If It Was Your Dog?” Resource Shortages and Decision-Making in Veterinary Medicine—A Vignette Study with German Veterinary Students
Authors: Persson Kirsten , Gerdts Wiebke-Rebekka , Hartnack Sonja , Kunzmann Peter
Published: February 2023

The here presented vignette study was part of a survey on ethical judgement skills among advanced veterinary students at the University of Veterinary Medicine Hannover, Foundation. The vignette describes a fictitious dilemma in veterinary practice due to medication supply shortages. First, the students should make an ethically justified decision: who of the two patients in the waiting room gets the last dosage of a medication. Important factors were the animal patients’ characteristics (age, state of health, life expectancy), the patient owners’ wellbeing, and context-related criteria. Second, the students were asked for decisional changes if one of the patients was their own dog. They reacted in four different ways: (1) for a professional, this should not make a difference; (2) most likely being “egoistic” and preferring their own dog; (3) giving the medication to the other dog; and (4) avoiding a decision. Finally, the students judged a list of possible solutions to the dilemma on a 9-point scale. They preferred patient-related criteria to patient-owner-related criteria in this task. In the overall results, it became obvious that no “gold standard” or guidelines for situations of medication shortages exist, yet, which presents an important subject for future research and veterinary ethics teaching.
Deep Transfer Learning Models for Industrial Fault Diagnosis Using Vibration and Acoustic Sensors Data: A Review
Authors: Bhuiyan Md Roman , Uddin Jia
Published: February 2023

In order to evaluate final quality, nondestructive testing techniques for finding bearing flaws have grown in favor. The precision of image processing-based vision-based technology has greatly improved for defect identification, inspection, and classification. Deep Transfer Learning (DTL), a kind of machine learning, combines the superiority of Transfer Learning (TL) for knowledge transfer with the benefits of Deep Learning (DL) for feature representation. As a result, the discipline of Intelligent Fault Diagnosis has extensively developed and researched DTL approaches. They can improve the robustness, reliability, and usefulness of DL-based fault diagnosis techniques (IFD). IFD has been the subject of several thorough and excellent studies, although most of them have appraised important research from an algorithmic standpoint, neglecting real-world applications. DTL-based IFD strategies have also not yet undergone a full evaluation. It is necessary and imperative to go through the relevant DTL-based IFD publications in light of this. Readers will be able to grasp the most cutting-edge concepts and develop practical solutions to any IFD challenges they may encounter by doing this. The theory behind DTL is briefly discussed before describing how transfer learning algorithms may be included into deep learning models. This research study looks at a number of vision-based methods for defect detection and identification utilizing vibration acoustic sensor data. The goal of this review is to assess where vision inspection system research is right now. In this respect, image processing as well as deep learning, machine learning, transfer learning, few-shot learning, and light-weight approach and its selection were explored. This review addresses the creation of defect classifiers and vision-based fault detection systems.
Impacts of Best Management Practices on Runoff, Soil Loss, and Sediment Yield in the Megech Watershed, Ethiopia
Authors: Admas Mulugeta , Melesse Assefa M. , Abate Brook , Tegegne Getachew
Published: February 2023

This study evaluated the best management practices on how to manage soil losses from catchment and reduce sediment load into a dam reservoir. This study aimed to evaluate the relationship of runoff, soil loss, and sediment yield with best management practice (BMP) scenarios in the GeoWEPP environment for the selected three micro-watersheds (hot spot areas) in the Megech watershed, upper Blue Nile Basin. The impacts of four agricultural BMP scenarios, including forest five years old, corn, soybean; wheat, alfalfa (4 yr) no till; corn, soybean, wheat, alfalfa (4 yr) conservation till; and winter wheat mulch till, on soil loss, runoff, and sediment yield were quantified. The results revealed that soil loss ranges between 41.45–66.11 t/ha/year and sediment yield rates ranges between 36.5–54.8 t/ha/year with the baseline situation (conventional tillage condition) were found to be higher than the tolerable soil loss (10 t/ha/year) in the region. Implementing BMPs on the crop land of the micro-watersheds has positive impacts on all variables’ runoff, soil loss, and sediment yield reductions. Among the implemented BMPs, forests with a five-year perennial (agroforestry) option showed the highest rate of reduction for all runoff, soil loss, and sediment yield, but no cost benefit analysis was included in this study to choose among the BMPs. This study also identified that agricultural BMPs play a great role in reducing runoff, soil loss, and sediment yield in the Megech watershed to minimize on- and off-site impacts. In general, it is important to consider how cost benefit analysis will change throughout project’s implementation among the selected BMP scenarios at the watershed level in the future.