bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantitative Gait Analysis of Patients with Severe Symptomatic Spinal Stenosis Utilizing the Gait Profile Score: An Observational Clinical Study
Authors: Lodin Jan , Jelínek Marek , Sameš Martin , Vachata Petr
Published: February 2022

Lumbar spine stenosis (LSS) typically manifests with neurogenic claudication, altering patients’ gait. The use of optoelectronic systems has allowed clinicians to perform 3D quantitative gait analysis to quantify and understand these alterations. Although several authors have presented analysis of spatiotemporal gait parameters, data concerning kinematic parameters is lacking. Fifteen patients with LSS were matched with 15 healthy controls. Quantitative gait analysis utilizing optoelectronic techniques was performed for each pair of subjects in a specialized laboratory. Statistical comparison of patients and controls was performed to determine differences in spatiotemporal parameters and the Gait Profile Score (GPS). Statistically significant differences were found between patient and control groups for all spatiotemporal parameters. Patients had significantly different overall GPS (p = 0.004) and had limited internal/external pelvic rotation (p < 0.001) and cranial/caudal movement (p = 0.034), limited hip extension (p = 0.012) and abduction/adduction (p = 0.012) and limited ankle plantar flexion (p < 0.001). In conclusion, patients with LSS have significantly altered gait patterns in three regions (pelvis, hip and ankle) compared to healthy controls. Analysis of kinematic graphs has given insight into gait pathophysiology of patients with LSS and the use of GPS will allow us to quantify surgical results in the future.
Human Being Detection from UWB NLOS Signals: Accuracy and Generality of Advanced Machine Learning Models
Authors: Moro Gianluca , Di Luca Federico , Dardari Davide , Frisoni Giacomo
Published: February 2022

This paper studies the problem of detecting human beings in non-line-of-sight (NLOS) conditions using an ultra-wideband radar. We perform an extensive measurement campaign in realistic environments, considering different body orientations, the obstacles’ materials, and radar–obstacle distances. We examine two main scenarios according to the radar position: (i) placed on top of a mobile cart; (ii) handheld at different heights. We empirically analyze and compare several input representations and machine learning (ML) methods—supervised and unsupervised, symbolic and non-symbolic—according to both their accuracy in detecting NLOS human beings and their adaptability to unseen cases. Our study proves the effectiveness and flexibility of modern ML techniques, avoiding environment-specific configurations and benefiting from knowledge transference. Unlike traditional TLC approaches, ML allows for generalization, overcoming limits due to unknown or only partially known observation models and insufficient labeled data, which usually occur in emergencies or in the presence of time/cost constraints.
A Fairness of Data Combination in Wireless Packet Scheduling
Authors: Bhandari Sovit , Ranjan Navin , Kim Yeong-Chan , Khan Pervez , Kim Hoon
Published: February 2022

With the proliferation of artificial intelligence (AI) technology, the function of AI in a sixth generation (6G) environment is likely to come into play on a large scale. Moreover, in recent years, with the rapid advancement in AI technology, the ethical issues of AI have become a hot topic. In this paper, the ethical concern of AI in wireless networks is studied from the perspective of fairness in data. To make the dataset fairer, novel dataset categorization and dataset combination schemes are proposed. For the dataset categorization scheme, a deep-learning-based dataset categorization (DLDC) model is proposed. Based on the results of the DLDC model, the input dataset is categorized based on the group index. The datasets based on the group index are combined using various combination schemes. Through simulations, the results of each dataset combination method and their performance are compared, and the advantages and disadvantages of fairness and performance according to the dataset configuration are analyzed.
How Much Margin Is Left for Degrading Agricultural Soils? The Coming Soil Crises
Authors: Gebremedhin Maheteme , Coyne Mark S. , Sistani Karamat R.
Published: February 2022

Agricultural soils are in peril. Multiple lines of observational and empirical evidence suggest that we are losing the world’s fertile soils at an alarming rate, worsening the on-going global food crisis. It is increasingly clear that the risk of soil crises driven by erratic precipitation, warming air, and farming mismanagement is coming sooner rather than later. At this critical time, society cannot avoid looking for ways to curb soil crises. We argue that now is the right time for science-based mitigation strategies and new insights to protect soils. We offer four research priority areas that society needs to address. Arresting and reversing the ongoing soil degradation are tantamount to safeguarding humanity and the environment. To the extent that we continue to treat soil crises as a problem for farmers only—not as a global challenge—we only escalate the scale to which the problem will grow in time and complexity.
Game Elements towards More Sustainable Learning in Object-Oriented Programming Course
Authors: Jusas Vacius , Barisas Dominykas , Jančiukas Mindaugas
Published: February 2022

Gamification of education is considered to have the capacity to increase students’ engagement in a learning process; to drive learning and skills acquisition; and creates changes for more sustainable behavior. Sustainable behavior is especially important in studying the initial courses at a university. Object-oriented programming (OOP) is such a course in the studies for a programmer’s diploma. We applied four elements of gamification: experience points, interactive content, local team, and global team. A local team is dedicated specifically to the OOP course, but all other game elements can be applied to any course. The course was implemented in a Moodle platform, where two additional plugins for experience points and interactive content were employed. Research using the implemented gamified course in the teaching process was carried out during the fall semester in 2020 and results compared to two previous non-gamified course years. A statistically insignificant increase in the mean grade was observed. Nevertheless, the gamified course resulted in a 7% decrease in the number of students who had to retake the OOP course compared to the year 2019. The gamified course also resulted in a drastic decrease in the number of students requiring a second attempt to pass the course’s exam. The obtained results demonstrate that the gamification of OOP course resulted in more sustainable behavior of the students. Based on the results of students’ questionnaires, we highly recommend using global team game element as it showed a positive effect on students’ engagement when the number of participating students is large.
Use of Triangulation in Comparing the Blockchain Knowledge Structure between China and South Korea: Scientometric Network, Topic Modeling, and Prediction Technique
Authors: Zhu Yu-Peng , Park Han-Woo
Published: February 2022

Blockchain, as a new innovative technology, has become a popular topic in many fields in recent years. In this study, triangulation was used to investigate the development of knowledge structures. First, scientometric network analysis was employed to identify the cooperation of knowledge networks. It was found that the structure of blockchain knowledge networks in China is relatively more complex and diverse than in South Korea. Since increased teamwork in blockchain is conducive to the creation of high-quality knowledge products, the Chinese government appears to strongly promote diversified cooperation on blockchain technology through centralized policies. Second, machine-learning topic modeling was used to analyze the content exchanged via a collaborative network. As a result, it was found that both countries lacked the societal and commercial aspects of blockchain technology. Finally, we developed a prediction technique based on the Ernie model to automatically categorize the nature of blockchain research.
Environmental Performance through Environmental Resources Conservation Efforts: Does Corporate Social Responsibility Authenticity Act as Mediator?
Authors: Nassani Abdelmohsen A. , Yousaf Zahid , Radulescu Magdalena , Haffar Mohamed
Published: February 2022

This study shows the impact of environmental resources conservation efforts (ERCE) and corporate social responsibility (CSR) authenticity on environmental performance (EP) in the hospitality and tourism industry. A mediating role of CSR authenticity between the ERCE and environmental performance link is also checked. A quantitative research design has been employed, and data were collected through a questionnaire. Results show that both ERCE and CSR authenticity positively influence environmental performance. Results also show that CSR authenticity act as a mediator, which enhances the positive impact of ERCE on environmental performance. Environmental performance is a broader concept, and its achievement is majorly dependent on the attraction of stakeholders. This study points out that stakeholders should be more concerned with environmental management’s initiatives in terms of resource conservation efforts and organization involvement in CSR activities for achieving the environmental performance goal. ERCE is a pre-condition for an efficient involvement in CSR-related activities to achieve environmental performance.
Empirical Evidence of the Livelihood Vulnerability to Climate Change Impacts: A Case of Potato-Based Mountain Farming Systems in Bhutan
Authors: Rai Pradeep , Bajgai Yadunath , Rabgyal Jimba , Katwal Tirtha Bdr , Delmond Anthony Ryan
Published: February 2022

Potato (Solanum tuberosum) is an indispensable commodity, mainly cultivated by high-altitude mountain households, that sustains and supports the livelihood of an overwhelming 51% of the Bhutanese population. The popularity of potato cultivation among Bhutanese farmers can be attributed to the crop’s adaptability to a wide range of agroclimatic conditions such as a rainfed crop, high productivity, an assured market, and a reliable source of income for the farming families. We hypothesize that the changing climate would make the livelihood associated with potato cultivation in Bhutan more vulnerable. We tested this hypothesis to identify the sources of vulnerability of smallholder farming households using the Livelihood Vulnerability Index (LVI) and LVI-IPCC (Intergovernmental Panel on Climate Change) approaches in six potato growing districts of Bhutan: Bumthang, Chukha, Gasa, Mongar, Tashigang, and Wangdue. Primary data were generated through a semi structured sample survey of 240 households on the seven major livelihood components of sociodemographic profiles, livelihood strategies, social networks, health, food, water, natural disasters, and climate variability. The results showed that the LVI (range 0.302 to 0.375) and LVI-IPCC (range −0.005 to 0.030) differed significantly (p < 0.001) across the districts. The districts of Tashigang and Mongar were less vulnerable than the other four districts by the LVI approach, whereas Bumthang was also revealed to be less vulnerable using the LVI-IPCC approach. The degree of vulnerability in a district differed according to their level of exposure and adaptive capacity to the climate change impacts of the potato farming household. The results are expected to serve as empirical evidence for designing a future course of actions to mitigate the negative impacts.
Exploring Machine Learning Models in Predicting Irrigation Groundwater Quality Indices for Effective Decision Making in Medjerda River Basin, Tunisia
Authors: Trabelsi Fatma , Bel Hadj Ali Salsebil
Published: February 2022

Over the last years, the global application of machine learning (ML) models in groundwater quality studies has proved to be a robust alternative tool to produce highly accurate results at a low cost. This research aims to evaluate the ability of machine learning (ML) models to predict the quality of groundwater for irrigation purposes in the downstream Medjerda river basin (DMB) in Tunisia. The random forest (RF), support vector regression (SVR), artificial neural networks (ANN), and adaptive boosting (AdaBoost) models were tested to predict the irrigation quality water parameters (IWQ): total dissolved solids (TDS), potential salinity (PS), sodium adsorption ratio (SAR), exchangeable sodium percentage (ESP), and magnesium adsorption ratio (MAR) through low-cost, in situ physicochemical parameters (T, pH, EC) as input variables. In view of this, seventy-two (72) representative groundwater samples have been collected and analysed for major cations and anions during pre-and post-monsoon seasons of 3 years (2019–2021) to compute IWQ parameters. The performance of the ML models was evaluated according to Pearson’s correlation coefficient (r), the root means square error (RMSE), and the relative bias (RBIAS). The model sensitivity analysis was evaluated to identify input parameters that considerably impact the model predictions using the one-factor-at-time (OFAT) method of the Monte Carlo (MC) approach. The results show that the AdaBoost model is the most appropriate model for predicting all parameters (r was ranged between 0.88 and 0.89), while the random forest model is suitable for predicting only four parameters: TDS, PS, SAR, and ESP (r was with 0.65 to 0.87). Added to that, this study found out that the ANN and SVR models perform well in predicting three parameters (TDS, PS, SAR) and two parameters (PS, SAR), respectively, with the most optimal value of generalization ability (GA) close to unity (between 1 and 0.98). Moreover, the results of the uncertainty analysis confirmed the prominent superiority and robustness of the ML models to produce excellent predictions with only a few physicochemical parameters as inputs. The developed ML models are relevant for predicting cost-effective irrigation water quality indices and can be applied as a DSS tool to improve water management in the Medjerda basin.
Assessing Riverbank Erosion and Livelihood Resilience Using Traditional Approaches in Northern Bangladesh
Authors: Mamun Abdullah Al , Islam Abu Reza Md. Towfiqul , Alam Edris , Chandra Pal Subodh , Alam G. M. Monirul
Published: February 2022

Bangladesh is a riverine country that is the most climate-change-vulnerable country in the world. Riverbank erosion adversely affects people and their livelihoods by damaging their homesteads, agricultural land, and causing economic, social, and psychological distress over time. Under these criteria, erosion control measures/approaches are vital for protecting erosion and crating livelihoods resilient. This study intends to assess riverbank erosion and livelihood resilience using traditional erosion control approaches by observing and surveying 118 riverine households in Rowmari, northern Bangladesh. The results show that riverbank erosion enormously damages household lands, socio-economic conditions, livelihoods, and resources, resulting in increased vulnerability. About 54.2% of the households utilize traditional approaches known as bundlings and 33.1% use both bundlings + bank vegetation that helps them resist erosion. Traditional erosion management approaches enhance resilience by safeguarding agricultural land, crop fields, production, and homesteads rapidly and sustainably, and by reducing damage to infrastructure, roads, embankments and transportation in the region. A probit model was also employed to explore factors that have influenced traditional erosion approaches. The factors such as age, education, family size, erosion experience, occupation, earning members, and farm size were meaningfully related to taking traditional erosion approaches. It is expected that the outcomes of the present research will help guide governmental agencies and policymakers and contribute to the construction of a sustainable riverbank erosion action framework in Bangladesh and other regions with similar problems.
Adaptation Strategies to a Changing Resource Base: Case of the Gillnet Nile Perch Fishery on Lake Victoria in Uganda
Authors: Mpomwenda Veronica , Tómasson Tumi , Pétursson Jón Geir , Taabu-Munyaho Anthony , Nakiyende Herbert , Kristófersson Daði Mar
Published: February 2022

Sustainable management of fisheries has proven to be a daunting exercise for Lake Victoria. Exploitation patterns in the fishery are driven by fishers who adopt different strategies as a response to changing economic, management, socio-economic, and resource conditions. Fisheries managers, however, seldom consider these changes in management policies. The aim of the study, therefore, was to evaluate the adaptation strategies of the Nile perch gillnet fishers on Lake Victoria in Uganda using 8-year catch and effort data collected in the period from 2005 to 2015. Trends of the selected effort and catch variables in the study period identified two adaptive fishing strategies by gillnet fishers on the lake. The first group, the paddled fishermen whose gillnet use varied in the first half of the study, diverted to harvesting juvenile Nile perch by using smaller, mesh sizes, monofilament nets and gillnets of less depth in the second half of the study. Motorized fishers, on the other hand, maintained their mesh size, using multifilament gillnets, however, they increased the depth of their nets in the second half of the study period to maintain their targeted fish size. Fishers on Lake Victoria adapted strategies to cope with their constraints and opportunities based on the Nile perch population structure and their economic needs. It is important for fishery managers to consider that the fishers are an integral part of the fisheries ecosystems, and considering their behavior in management decisions will aid in devising adaptive policies for sustainable resource use and sustainable livelihood development of the fishers’ communities.
Travel-Business Stagnation and SME Business Turbulence in the Tourism Sector in the Era of the COVID-19 Pandemic
Authors: Surya Batara , Hernita Hernita , Salim Agus , Suriani Seri , Perwira Iwan , Yulia Yulia , Ruslan Muhlis , Yunus Kafrawi
Published: February 2022

The COVID-19 pandemic, apart from having an impact on public health, has also caused the stagnation of travel-bureau businesses and the management of small and medium enterprises (SMEs) in the tourism sector. This study aims to analyze the COVID-19 pandemic as a determinant of travel-business stagnation and turbulence in small and medium enterprises (SMEs), the influence of human resources, business development, and product marketing on the productivity of the travel and SME business, the direct and indirect effects of business innovation, economic digitization, and the use of technology on business stability and economic-business sustainability. This study uses an explanatory sequential qualitative–quantitative approach. Data were obtained through observation, in-depth interviews, surveys, and documentation. This study is focused on assessing the efforts made by travel-agency-business actors and SMEs in responding and adapting to changes in the business environment, both internally and externally. Human resources, business development, and product marketing together affect the productivity of travel agents and SMEs with a coefficient of determination of 95.84%. Furthermore, business innovation, economic digitization, and the use of technology simultaneously affect business stability with a coefficient of determination of 63.8%, and business stability affects the sustainability of travel and SMEs with a coefficient of determination of 67.6%. This study recommends a strategy for travel-agency-business sustainability and the stability of SMEs’ economic-business management towards increasing economic growth in the North Toraja Regency, South Sulawesi, Indonesia.
Modeling and Optimization of Biochar Injection into Blast Furnace to Mitigate the Fossil CO2 Emission
Authors: Mousa Elsayed , Sjöblom Kurt
Published: February 2022

Most modern blast furnaces (BFs) operate with Pulverized Coal Injection (PCI), but renewable and carbon neutral biochar could be applied to reduce the fossil CO2 emission in the short term. In the present study, heat and mass balance-based model (MASMOD) is applied to evaluate the potential of biochar in partial and full replacement of injected pulverized coal (PC) in the ironmaking BF. The impact of biochar injection on the raceway adiabatic flame temperature (RAFT) and top gas temperature (TGT) is evaluated. Three grades of biochar, produced from the pyrolysis of sawdust, were evaluated in this study. The total carbon content was 79.2%, 93.4% and 89.2% in biochar 1, 2 and 3, respectively, while it was 81.6% in the reference PC. For each type of biochar, 6 cases were designed at different injection levels from 30 kg/tHM up to 143 kg/tHM, which represent 100% replacement of PC in the applied case, while the top charged coke is fixed in all cases as reference. The oxygen enrichment, RAFT, and TGT are fixed for certain cases, and have been calculated by MASMOD in other cases to identify the optimum level of biochar injection. The MASMOD calculation showed that as the injection rate of biochar 1 and biochar 2 increased, the RAFT increased by ~190 °C, while TGT decreased by ~45 °C at 100% replacement of PC with biochar. By optimizing the moisture content of biochar and the oxygen enrichment in the blast, it is possible to reach 100% replacement of PC without much affecting the RAFT and TGT. Biochar 3 was able to replace 100% of PC without deteriorating the RAFT or TGT.
The Effect of WO3-Doped Soda Lime Silica SLS Waste Glass to Develop Lead-Free Glass as a Shielding Material against Radiation
Authors: Khazaalah Thair Hussein , Shahrim Mustafa Iskandar , Al-Ghamdi Hanan , Abdul Rahman Azhar , Sayyed M. I. , Almuqrin Aljawhara H. , Mohd Zaid Mohd Hafiz , Hisam Rosdiyana , Abdul Malik Muhammad Fadhirul Izwan , Seth Ezra Nabasu , Mohd Shariff Hanisha
Published: February 2022

The current study aims to enhance the efficiency of lead-free glass as a shielding material against radiation, solve the problem of the dark brown of bismuth glass, and reduce the accumulation of waste glass disposed in landfills by using soda-lime-silica SLS glass waste. The melt-quenching method was utilized to fabricate (WO3)x [(Bi2O3)0.2 (ZnO)0.3 (B2O3)0.2 (SLS)0.3]1−x at 1200 °C, where x = (0, 0.01, 0.02, 0.03, 0.04, and 0.05 mol). Soda lime silica SLS glass waste, which is mostly composed of 74.1 % SiO2, was used to obtain SiO2. Radiation Attenuation parameters were investigated using narrow-beam geometry and X-ray fluorescence (XRF). Furthermore, the parameters related to radiation shielding were calculated. The results showed that when WO3 concentration was increased, the half-value layer was reduced, whereas the μ increased. It could be concluded that WBiBZn-SLS glass is a good shielding material against radiation, nontoxic, and transparent to visible light.
Using a Product Life Cycle Cost Model to Solve Supplier Selection Problems in a Sustainable, Resilient Supply Chain
Authors: Tao Yu-Jwo , Lin Yi-Shyuan , Lee Hsuan-Shih , Gan Guo-Ya , Tu Chang-Shu
Published: February 2022

Supplier selection constitutes a crucial component of manufacturing procurement. We developed a product life cycle cost (PLCC) model to support Taiwanese light-emitting diode (LED) manufacturers in capacity planning for sustainable and resilient supply chain (SC) management. For firms, supply chain PLCC (SCPLCC) is a key consideration, but relevant evidence is scarce. We applied two types of goal programming, namely multiobjective linear programming and revised multichoice goal programming (RMCGP), to develop a PLCC-based model that minimizes net costs, rejections, and late deliveries. Moreover, we constructed a decision-making tool for application to a case of SC sustainable procurement management in a high-tech Taiwanese LED company. Managers can resolve relevant problems by employing the two approaches of the SCPLCC model with various parameters. The implementation of RMCGP with weighted linear goal programming sensitivity analysis produced sufficient findings, according to a study of five models for practical implications. The primary findings of the current model assist business decision-makers in minimizing PLCC, reducing PLCC cost, minimizing net cost, number of rejections, number of late deliveries, achieving PLCC goals, and selecting the best supplier in the context of sustainable SC development.
Corporate Social Responsibility and Hotel Employees’ Organizational Citizenship Behavior: The Roles of Organizational Pride and Meaningfulness of Work
Authors: Youn Hyewon , Kim Jong-Hyeong
Published: February 2022

The corporate social responsibility (CSR) literature documents inconsistent results regarding the relationship between CSR and employees’ organizational citizenship behavior (OCB). Additionally, few empirical studies examine the mediating factors explaining how employees’ CSR perception affects their attitudes and behaviors. Thus, we propose an extended M-R model linking hotels’ CSR activities, employees’ perceived meaningfulness of work and organizational pride, and organizational citizenship behavior. We recruited 363 Chinese hotel employees in Guangzhou for our sample and employed partial least squares structural equation modeling for our analysis. The results showed that CSR practices benefiting both internal (i.e., employees) and external stakeholders (i.e., community and customers) positively influenced employees’ organizational pride. However, only CSR activities benefiting customers significantly affected employees’ perceived meaningfulness of work. CSR influenced employees’ OCB through meaningfulness of work and organizational pride. The findings provide hotel management insights regarding the development of CSR initiatives to enhance employees’ organizational attitudes and work performance. The study limitations are that the survey methodology has weaknesses and that we do not consider the influence of corporate culture on OCB.
Estimating per Capita Primary Energy Consumption Using a Novel Fractional Gray Bernoulli Model
Authors: Wang Huiping , Wang Yi
Published: February 2022

On the basis of the available gray models, a new fractional gray Bernoulli model (GFGBM (1,1,tα)) is proposed to predict the per capita primary energy consumption (PPEC) of major economies in the world. First, this paper introduces the modeling mechanism and characteristics of the GFGBM (1,1,tα). The new model can be converted to other gray models through parameter changes, so the new model has strong adaptability. Second, the predictive performance of the GFGBM (1,1,tα) is assessed by the four groups of PPEC. The optimal parameters of the model are solved by the moth flame optimization and gray wolf optimization algorithms, and the prediction results of the models are evaluated by two error metrics. The results show that the GFGBM (1,1,tα) is more feasible and effective than the other tested gray models. Third, the GFGBM (1,1,tα) is applied to forecast the PPEC of India, the world, the Organization for Economic Cooperation and Development (OECD) countries, and non-OECD countries over the next 5 years. The forecasting results indicate that the PPEC of the four economies will increase by 5.36 GJ, 42.09 GJ, 5.75 GJ, and 29.22 GJ, respectively, an increase of 51.53%, 55.61%, 3.22%, and 53.41%, respectively.
An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety
Authors: Tao Wenlong , Aghaabbasi Mahdi , Ali Mujahid , Almaliki Abdulrazak H. , Zainol Rosilawati , Almaliki Abdulrhman A. , Hussein Enas E.
Published: February 2022

More than 8000 pedestrians were killed due to road crashes in Australia over the last 30 years. Pedestrians are assumed to be the most vulnerable users of roads. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. It is critical to know the causes of pedestrian injuries in order to enhance the safety of these vulnerable road users. To achieve this, traditional statistical models are used frequently. However, they have been criticized for their inflexibility in handling outliers and missing or noisy data, and their strict pre-assumptions. This study applied an advanced machine learning algorithm, a Bayesian neural network, which has the characters of both Bayesian theory and neural networks. Several structures of this model were built, and the best structure was selected, which included three hidden neuron layers—sixteen hidden nodes in the first layer and eight hidden nodes in the second and third layers. The performance of this model was compared with the performances of some other machine learning techniques, including standard Bayesian networks, a standard neural network, and a random forest model. The Bayesian neural network model outperformed the other models. In addition, a study on the importance of the features showed that the individuals’ characteristics, time, and circumstantial factors were essential. They greatly increased model performance if the model used them. This research lays the groundwork for using machine learning approaches to alleviate pedestrian deaths caused by road accidents.
Mangrove Ecosystem Services, Associated Threats and Implications for Wellbeing in the Mono Transboundary Biosphere Reserve (Togo-Benin), West-Africa
Authors: Gnansounou Setondé Constant , Salako Kolawolé Valère , Sagoe Alberta Ama , Mattah Precious Agbeko Dzorgbe , Aheto Denis Worlanyo , Glèlè Kakaï Romain
Published: February 2022

Mangroves are important coastal ecosystems, which deliver diverse and crucial services to humans. This study explored the diversity of mangrove ecosystem services, their associated threats as well as their contribution to livelihoods and wellbeing of coastal communities in the Mono Transboundary Biosphere Reserve (MTBR) located between Benin and Togo. Data were collected using the exploratory sequential mixed method. The approach included field reconnaissance, focus group discussions (n = 14), in-depth interviews (n = 17), household survey (n = 274) and direct observations. A total of 21 services and 7 associated threats were recorded in the entire reserve. Provisioning services were the most important service for mangroves in the reserve followed by supporting services, regulating services and cultural services. Change in water salinity, mangrove overharvesting and illegal, unreported and unregulated fishing were the three major threats to mangrove ecosystem services in the reserve. Most of the respondents indicated that the current flow of provisioning services, regulating services and cultural services does not sustain their wellbeing and livelihoods. However, the perception varied significantly across respondents’ gender, ethnical groups, educational background and country. Our study showed some similarities between the two countries but also highlighted important differences which can assist the sustainable management of mangroves in the MTBR.
Spontaneous Symmetry Breaking: The Case of Crazy Clock and Beyond
Authors: Pagnacco Maja C. , Maksimović Jelena P. , Daković Marko , Bokic Bojana , Mouchet Sébastien R. , Verbiest Thierry , Caudano Yves , Kolaric Branko
Published: February 2022

In this work, we describe the crazy-clock phenomenon involving the state I (low iodide and iodine concentration) to state II (high iodide and iodine concentration with new iodine phase) transition after a Briggs–Rauscher (BR) oscillatory process. While the BR crazy-clock phenomenon is known, this is the first time that crazy-clock behavior is linked and explained with the symmetry-breaking phenomenon, highlighting the entire process in a novel way. The presented phenomenon has been thoroughly investigated by running more than 60 experiments, and evaluated by using statistical cluster K-means analysis. The mixing rate, as well as the magnetic bar shape and dimensions, have a strong influence on the transition appearance. Although the transition for both mixing and no-mixing conditions are taking place completely randomly, by using statistical cluster analysis we obtain different numbers of clusters (showing the time-domains where the transition is more likely to occur). In the case of stirring, clusters are more compact and separated, revealed new hidden details regarding the chemical dynamics of nonlinear processes. The significance of the presented results is beyond oscillatory reaction kinetics since the described example belongs to the small class of chemical systems that shows intrinsic randomness in their response and it might be considered as a real example of a classical liquid random number generator.
A Novel Method for Friction Coefficient Calculation in Metal Sheet Forming of Axis-Symmetric Deep Drawing Parts
Authors: Xia Jiansheng , Zhao Jun , Dou Shasha , Shen Xing
Published: February 2022

Friction is one of the important factors in sheet metal forming. It greatly affects dynamic behaviors of metal sheets and stress and strain distributions in the metal sheets. In this study, deformation characteristics, stress–strain distribution, and change law of symmetrical parts in the process of deep drawing are analyzed using a new theoretical model based on the plastic flow law and partitioning the forming area. In the model, the least-square method is used to linearize the friction coefficient in nonlinear problems and reverse the calculation of friction coefficients to interpret the friction coefficient. To evaluate the model, the friction coefficient in sheet metal drawing of axis-symmetric deep drawing parts under various friction conditions was measured using a self-developed measuring system. The comparison between the experimental results and the calculation using the model shows a good agreement. The results show that the drawing force increases with the increase in punch depth; the friction coefficient decreases with the rise in punch depth. The friction coefficient obtained by fitting is relatively stable, and the average error is less than 3%. Using the friction coefficient model in finite element simulation analysis, it shows that the thickness and blank shape errors are less than 5%. The novel method studied in this paper shows great significance in support for theoretical research, numerical simulation research, and sheet metal stamping performance evaluation.
New Applications of Fractional Integral for Introducing Subclasses of Analytic Functions
Authors: Alb Lupaş Alina
Published: February 2022

The fractional integral is prolific in giving rise to interesting outcomes when associated with different operators. For the study presented in this paper, the fractional integral is associated with the convolution product of multiplier transformation and the Ruscheweyh derivative. Using the operator obtained as a result of this association and inspired by previously published results obtained with similarly introduced operators, the class of analytic functions IR(μ,λ,β,γ,α,l,m,n) is defined and investigated concerning various characteristics such as distortion bounds, extreme points and radii of close-to-convexity, starlikeness and convexity for functions belonging to this class.
The Role of Art Expertise and Symmetry on Facial Aesthetic Preferences
Authors: Monteiro Luis Carlos Pereira , Nascimento Victória Elmira Ferreira do , Carvalho da Silva Amanda , Miranda Ana Catarina , Souza Givago Silva , Ripardo Rachel Coelho
Published: February 2022

Humans, like other species, have a preference for symmetrical visual stimuli, a preference that is influenced by factors such as age, sex, and artistic training. In particular, artistic training seems to decrease the rejection of asymmetry in abstract stimuli. However, it is not known whether the same trend would be observed in relation to concrete stimuli such as human faces. In this article, we investigated the role of expertise in visual arts, music, and dance, in the perceived beauty and attractiveness of human faces with different asymmetries. With this objective, the beauty and attractiveness of 100 photographs of faces with different degrees of asymmetry were evaluated by 116 participants with different levels of art expertise. Expertise in visual arts and dance was associated with the extent to which facial asymmetry influenced the beauty ratings assigned to the faces. The greater the art expertise in visual arts and dance, the more indifferent to facial asymmetry the participant was to evaluate beauty. The same effect was not found for music and neither for attractiveness ratings. These findings are important to help understand how face aesthetic evaluation is modified by artistic training and the difference between beauty and attractiveness evaluations.
Molecular Detection of Cryptosporidium cuniculus in Rabbits (Oryctolagus cuniculus) from Tenerife, Canary Islands, Spain
Authors: Baz-González Edgar , Martín-Carrillo Natalia , García-Livia Katherine , Foronda Pilar
Published: February 2022

Cryptosporidium cuniculus is a zoonotic parasite responsible for cryptosporidiosis cases and outbreaks in both humans and rabbits. Since there are no molecular Cryptosporidium spp. infection data in rabbits (Oryctolagus cuniculus) from Spain, our aim was to gather information about this parasite in wild European rabbits from Tenerife, Canary Islands (Spain). A total of 100 faecal samples were collected from rabbits from eight municipalities of Tenerife. Microscopic analysis showed that 4.0% of the samples presented structures compatible with Cryptosporidium oocyst. A nested polymerase chain reaction (PCR) targeting 18S ribosomal RNA (rRNA) gene fragments was carried out, and sequencing confirmed the identity of C. cuniculus in one sample (1.0%). The sample was successfully subtyped using nested PCR analysis of the 60-kDa glycoprotein (gp60) gene as the subtype VbA26R3. This study confirms the presence of C. cuniculus in wild rabbits from Tenerife, providing new information on the occurrence of this zoonotic parasite. Further studies are required to better understand the epidemiology of Cryptosporidium spp. in wild rabbits in Spain and their possible public health repercussions.
Multiplexed Genome Editing for Efficient Phenotypic Screening in Zebrafish
Authors: Guo Shuyu , Gao Ge , Zhang Cuizhen , Peng Gang
Published: February 2022

Zebrafish are widely used to investigate candidate genes for human diseases. While the emergence of CRISPR-Cas9 technology has revolutionized gene editing, the use of individual guide RNAs limits the efficiency and application of this technology in functional genetics research. Multiplexed genome editing significantly enhances the efficiency and scope of gene editing. Herein, we describe an efficient multiplexed genome editing strategy to generate zebrafish mutants. Following behavioural tests and histological examination, we identified one new candidate gene (tmem183a) for hearing loss. This study provides a robust genetic platform to quickly obtain zebrafish mutants and to identify candidate genes by phenotypic readouts.
Assessment of Groundwater Contamination Risk in Oilfield Drilling Sites Based on Groundwater Vulnerability, Pollution Source Hazard, and Groundwater Value Function in Yitong County
Authors: Fang Zhang , Liu Zhiguo , Zhao Siyuan , Ma Yanlin , Li Xia , Gao Han
Published: February 2022

Oilfield drilling sites are the potential dispersive pollution source of groundwater, especially to shallow groundwater. The pollution risk assessment in these areas is an important reference for effective groundwater management and protection. The vulnerability assessment alone is not sufficient for groundwater contamination risk assessment. In this study, we developed a comprehensive groundwater pollution risk assessment method for oilfield drilling sites that combine groundwater vulnerability, pollution source hazard, and groundwater value function to produce a more comprehensive result. Consider the oilfield drilling area in Yitong County of Jilin Province, China, as an example. Thematic maps of the three aspects (groundwater vulnerability, pollution source hazard, and groundwater value function) were generated in ArcGIS environment to assess the contamination risk of groundwater in quaternary pore unconfined aquifer. The results show that 9.92% of the study area is characterized as being at high risk. These areas are mainly distributed around the center position of the oil drilling site, floodplains, and the reservoir. The moderate risk area accounts for 21.04% of the total area. It is distributed in the first-level terrace, mainly because of the high function value of groundwater. The remaining 69.04% of the study area is characterized as none and mild risk, mainly distributed in the valleys and terraces. This integrated groundwater contamination risk assessment method is suited for comparative assessment of multiple-point sources of contamination at a regional scale. Finally, the groundwater contamination risk grade distributed in this area provides a reference for effective protection and sustainable supply of groundwater in the oilfield drilling area.
Experimental and Numerical Study to Investigate the Impact of Changing the Boundary Water Levels on Saltwater Intrusion in Coastal Aquifers
Authors: Abd-Elhamid Hany F. , Abdel-Aal Gamal M. , Fahmy Maha , Sherif Mohsen , Zeleňáková Martina , Abd-Elaty Ismail
Published: February 2022

Experimental and numerical models can be used to investigate saltwater intrusion (SWI) in coastal aquifers. Sea level rise (SLR) and decline of freshwater heads due to climate change are the two key variables that may affect saltwater intrusion. This study aims to give a better understanding of the impact of increasing seawater levels and decreasing freshwater heads due to climate change and increasing abstraction rates due to overpopulation using experimental and numerical models on SWI. The experimental model was conducted using a flow tank and the SEAWAT code was used for the numerical simulation. Different scenarios were examined to assess the effect of seawater rise and landside groundwater level decline. The experimental and numerical studies were conducted on three scenarios: increasing seawater head by 25%, 50% and 75% from the difference between seawater and freshwater heads, decreasing freshwater head by 75%, 50% and 25% from the difference between seawater and freshwater heads, and a combination of these two scenarios. Good agreement was attained between experimental and numerical results. The results showed that increasing the seawater level and decreasing freshwater head increased saltwater intrusion, but the combination of these two scenarios had a severe effect on saltwater intrusion. The numerical model was then applied to a real case study, the Biscayne aquifer, Florida, USA. The results indicated that the Biscayne aquifer is highly vulnerable to SWI under the possible consequences of climate change. A 25 cm seawater rise and 28% reduction in the freshwater flux would cause a loss of 0.833 million m3 of freshwater storage per each kilometer width of the Biscayne aquifer. This study provides a better understanding and a quantitative assessment for the impacts of changing water levels’ boundaries on intrusion of seawater in coastal aquifers.
Regional Flood Frequency Analysis for Sustainable Water Resources Management of Genale–Dawa River Basin, Ethiopia
Authors: Mengistu Tarekegn Dejen , Feyissa Tolera Abdisa , Chung Il-Moon , Chang Sun Woo , Yesuf Mamuye Busier , Alemayehu Esayas
Published: February 2022

Regional information on stream discharge is needed in order to improve flood estimates based on the limited data availability. Regional flood estimation is fundamental for designing hydraulic structures and managing flood plains and water resource projects. It is essential for estimating flood risks during recurrent periods due to suitable distributions. Regional flood frequency analysis is crucial for evaluating design flows in ungauged basins, and can complement existing time series in gauged sites and transfer them to ungauged catchments. Hence, this study aims to perform a regional flood frequency analysis of the Genale–Dawa River Basin of Ethiopia using the index flood and L-moments approach for sustainable water resource management. Three homogeneous hydrological regions were defined and delineated based on homogeneity tests from data of 16 stream-gauged sites, named Region-A, Region-B, and Region-C. The discordancy index of regional data for L-moment statistics was identified using MATLAB. All regions showed promising results of L-moment statistics with discordance measures (discordance index less than 3) and homogeneity tests (combined coefficient of variation (CC) less than 0.3). L-moment ratio diagrams were used to select best fit probability distributions for areas. Generalized extreme value, log-Pearson type III, and generalized Pareto distributions were identified as suitable distributions for Region-A, Region-B, and Region-C, respectively, for accurately modeling flood flow in the basin. Regional flood frequency curves were constructed, and peak flood was predicted for different return periods. Statistical analysis of the gauged sites revealed an acceptable method of regionalization of the basin. This study confirms that the robustness of the regional L-moments algorithm depends on particular criteria used to measure the performance of estimators. The identified regions should be tested with other physical catchment features to enhance flood quantile estimates at gauged and ungauged sites. Henceforth, this study’s findings can be further extended into flood hazard, risk, and inundation mapping of identified regions of the study area. Furthermore, this study’s approach can be used as a reference for similar investigations of other river basins.
Phytoextraction and Antioxidant Defense of Mangrove Seedling (Kandelia obovata) to Inorganic Arsenate Exposure
Authors: Wu Guirong , Mei Kang , He Caimei , Wang Sujuan , Jiang Liling
Published: February 2022

Increasing arsenic (As) pollution is posing potential endangerment to mangrove wetland ecosystems. Mangrove phytoextraction, translocation, and responses to As exposure must be urgently addressed. In this study, the growth and physiological response of Kandelia obovata seedlings were examined after addition of 25−200 mg kg−1 As under sediment culture conditions. Results showed that the seedling morphological variations were not significant below 100 mg kg−1 compared to the control group, indicating superoxide dismutase, peroxidase, and catalase synergetic interaction to resist the As exposure. High As concentrations (150–200 mg kg−1) inhibited the seedling growth accompanied by a significant increase in malondialdehyde content and decrease in activities of antioxidant enzymes. Toxicity symptoms and mortality appeared in 200 mg kg−1 As, presumably because the plant reached the limit of As tolerance. Besides, As accumulated mainly in roots, accounting for 87.04–97.85% of the total As, and the bioaccumulation factor (BCF) was >100%. However, the BCF and translocation factor (TF) in stems and leaves were below unity, illustrating a weak capacity of transferring As to aerial parts of the seedlings. Overall, K. obovata is a potential remediated species in polluted coastal wetlands due to high phytoextraction capacity and high tolerance to As exposure.