bluuebunny/crossref_metadata_2025 huggingface dataset:
Recovery of Ammonium from Biomass-Drying Condensate Via Ion Exchange and Its Valorization as a Fertilizer
Authors: Song Jianzhi , Heinonen Jari , Sainio Tuomo
Published: March 2023

In this study, an industrial biomass-drying wastewater condensate containing > 3200 mg/L NH4+ and >8900 mg/L CH3COO− was treated in ion-exchange columns for the recovery of NH4+. Two commercial resins (CS12GC and CS16GC) were studied on laboratory and pilot scales. CS16GC outperformed CS12GC by achieving better separation at the condensate temperature (60 °C), which was energy-efficient regarding NH4+ removal. K3PO4 was used for regeneration to produce a liquid compound fertilizer containing nutrient elements (N, K, and P) as a byproduct. The N/K ratio in the byproduct was found to be adjustable by varying the operating parameters. Regeneration with 2 mol/L K3PO4 gave a higher regeneration efficiency (97.67% at 3 BV and ~100% at 4 BV). The stability tests performed on a laboratory scale showed that the cyclic runs of the column separation process were steady and repeatable. Based on the outcomes of the laboratory-scale tests, the pilot-scale tests applied a loading volume of 7 BV. The pilot column purified the feed and achieved the target NH4+ level in the treated effluent within the seven tested cycles, revealing that the industrial application of the cation ion-exchange resin CS16GC is worth further study.
Special Issue “Building Three-Dimensional Integrated Circuits and Microsystems”
Authors: Zhu Zhiyuan , Zhang Sixiang , Ma Shenglin , Liu Ziyu
Published: March 2023

With the further innovation of chip technology, semiconductor integrated circuits have made irreplaceable contributions to the development of microelectronic systems [...]
Postulant Education within the Jogye Order of Korean Buddhism: A Critical Examination of Its Past, Its Present, and the Issues Facing Its Future
Authors: Park Cheonghwan , Kim Kyungrae
Published: March 2023

Over the last three decades of evolution, the Jogye Order’s postulant education system has made considerable progress in standardizing, centralizing, and modernizing Buddhist education for aspiring monastics. As celebrated by the order’s 2022 publication The 30-year History of Buddhist Monastic Postulant Education, the order’s program has successfully seen over 9800 ordained novices graduate since its launch in 1991. However, there is a broad consensus within Korea’s Buddhist community that the religion is in crisis and, within the order in particular, that its future is in peril. Unless it is reversed, the trend portends the very real possibility of the order’s demise by the end of the century, if not sooner. The order recently vowed to reverse the downward trend in monastic recruitment and raise the annual number of ordained novices to 150 by 2025 through a multifaceted plan involving greater youth outreach efforts, an increased social media presence, and online Buddhist educational materials, along with an expansion of the order’s international missionary efforts. Given that postulant recruitment is critical to the order’s survival, this paper examines the past, present, and future of the Jogye Order’s postulant education system in light of the current membership crisis, as well as the order’s recent publication of The 30-year History of Buddhist Monastic Postulant Education.
Spatio-Temporal Heterogeneity and Cumulative Ecological Impacts of Coastal Reclamation in Coastal Waters
Authors: Lu Jingfang , Lv Xianqing , Shi Honghua
Published: March 2023

The coastal reclamation, as one of the most extreme transformations of the ocean space by humans, still lacks scientific quantitative evaluating methods to a large extent, compared with the evolution of land use patterns. A cumulative ecological impacts of reclamation (RCEI) was established in our study based on ecological influence characteristics of different reclamation types, and the attenuation effect of reclamation on adjacent areas. It was characterized by spatio-temporal features in decades. Here, we estimated that the cumulative reclamation area in the Bohai Sea from 1985 to 2018 was 5839.5 km2. Under the influence of human activity, proportions of the industrial and urban boundary, marine construction boundaries (e.g., ports, wharves, and bridges), and protective dams were increased significantly, which led to a sharp increase of the RCEI. In addition, spatio-temporal changes of reclamation were affected by the combination of population growth, economic development, urbanization, industrialization, and marine industry development in coastal cities. These results provided an important historical reference for tracking future development of the Bohai Sea by humans and provided basic data support for the development and protection of the ocean.
Development and Application of Predictive Models to Distinguish Seepage Slicks from Oil Spills on Sea Surfaces Employing SAR Sensors and Artificial Intelligence: Geometric Patterns Recognition under a Transfer Learning Approach
Authors: Genovez Patrícia Carneiro , Ponte Francisco Fábio de Araújo , Matias Ítalo de Oliveira , Torres Sarah Barrón , Beisl Carlos Henrique , Mano Manlio Fernandes , Silva Gil Márcio Avelino , Miranda Fernando Pellon de
Published: March 2023

The development and application of predictive models to distinguish seepage slicks from oil spills are challenging, since Synthetic Aperture Radars (SAR) detect these events as dark spots on the sea surface. Traditional Machine Learning (ML) has been used to discriminate the Oil Slick Source (OSS) as natural or anthropic assuming that the samples employed to train and test the models in the source domain (DS) follow the same statistical distribution of unknown samples to be predicted in the target domain (DT). When such assumptions are not held, Transfer Learning (TL) allows the extraction of knowledge from validated models and the prediction of new samples, thus improving performances even in scenarios never seen before. A database with 26 geometric features extracted from 6279 validated oil slicks was used to develop predictive models in the Gulf of Mexico (GoM) and its Mexican portion (GMex). Innovatively, these well-trained models were applied to predict the OSS of unknown events in the GoM, the American (GAm) portion of the GoM, and in the Brazilian continental margin (BR). When the DS and DT domains are similar, the TL and generalization are null, being equivalent to the usual ML. However, when domains are different but statically related, TL outdoes ML (58.91%), attaining 87% of global accuracy when using compatible SAR sensors in the DS and DT domains. Conversely, incompatible SAR sensors produce domains statistically divergent, causing negative transfers and generalizations. From an operational standpoint, the evidenced generalization capacity of these models to recognize geometric patterns across different geographic regions using TL may allow saving time and budget, avoiding the collection of validated and annotated new training samples, as well as the models re-training from scratch. When looking for new exploratory frontiers, automatic prediction is a value-added product that strengthens the knowledge-driven classifications and the decision-making processes. Moreover, the prompt identification of an oil spill can speed up the response actions to clean up and protect sensitive areas against oil pollution.
Evaluation and Comparison of Semantic Segmentation Networks for Rice Identification Based on Sentinel-2 Imagery
Authors: Xu Huiyao , Song Jia , Zhu Yunqiang
Published: March 2023

Efficient and accurate rice identification based on high spatial and temporal resolution remote sensing imagery is essential for achieving precision agriculture and ensuring food security. Semantic segmentation networks in deep learning are an effective solution for crop identification, and they are mainly based on two architectures: the commonly used convolutional neural network (CNN) architecture and the novel Vision Transformer architecture. Research on crop identification from remote sensing imagery using Vision Transformer has only emerged in recent times, mostly in sub-meter resolution or even higher resolution imagery. Sub-meter resolution images are not suitable for large scale crop identification as they are difficult to obtain. Therefore, studying and analyzing the differences between Vision Transformer and CNN in crop identification in the meter resolution images can validate the generalizability of Vision Transformer and provide new ideas for model selection in crop identification research at large scale. This paper compares the performance of two representative CNN networks (U-Net and DeepLab v3) and a novel Vision Transformer network (Swin Transformer) on rice identification in Sentinel-2 of 10 m resolution. The results show that the three networks have different characteristics: (1) Swin Transformer has the highest rice identification accuracy and good farmland boundary segmentation ability. Although Swin Transformer has the largest number of model parameters, the training time is shorter than DeepLab v3, indicating that Swin Transformer has good computational efficiency. (2) DeepLab v3 also has good accuracy in rice identification. However, the boundaries of the rice fields identified by DeepLab v3 tend to shift towards the upper left corner. (3) U-Net takes the shortest time for both training and prediction and is able to segment the farmland boundaries accurately for correctly identified rice fields. However, U-Net’s accuracy of rice identification is lowest, and rice is easily confused with soybean, corn, sweet potato and cotton in the prediction. The results reveal that the Vision Transformer network has great potential for identifying crops at the country or even global scale.
Social Work Students’ Perception of Education Quality, Commitment, and Competence: Comparison of Indonesia and Taiwan
Authors: Rusyidi Binahayati , Chen Yi-Yi , Shih Yao-Chi , McCarthy Mary , Tseng Yu-Hao
Published: March 2023

Competency-based education is designed to produce quality graduates and competent practitioners in social work. However, comparative studies to identify how social work education adapts to demands in practice from employers in coordination with the development of the profession across nations are rare. This study examines the influence of education quality and profession commitment on undergraduate social work students’ perceptions of their professional competence in Indonesia and Taiwan. A sample of 872 senior-year students from private and public universities was surveyed using a structured questionnaire. The analysis found that in the two countries, both education quality and professional commitment were positively associated with students’ perceptions of competence. Additionally, students’ commitment was a relatively important predictor in Indonesia but not in Taiwan. Implications for educators and profession development are discussed for societies where social work relies on the state for licensing and funding of practice.
Emotional Attachment in Social E-Commerce: The Role of Social Capital and Peer Influence
Authors: Yan Jianwen , Zhang Siwei , Zhang Siqi
Published: March 2023

As competition in the social e-commerce industry intensifies, building high-quality relationships with users to increase customer loyalty and gain sustainable competitive advantage is important for platforms. Based on the perspective of social capital, this paper constructs a relationship model of “social capital-peer influence-emotional attachment” based on Red Booklet and Poizon users and explores the influence and mechanism of social capital on emotional attachment in the context of social e-commerce. Social capital has a significant positive effect on peer influence and emotional attachment, while peer influence has a significant positive effect on emotional attachment and partially mediates the relationship between social capital and emotional attachment. This study provides practical insights from the perspective of “social capital” for enterprises to improve the users’ emotional attachment to the platform and further develop themselves in the social e-commerce environment.
Preference Model in the Context of Mobility as a Service: A Pilot Case Study
Authors: Franco Antonella , Vitetta Antonino
Published: March 2023

In this paper, a pilot study of a pre-test preference model in the context of mobility as a service (MaaS) is defined by following the steps required for transport system engineering: survey, specification, calibration, and validation. The availability of a MaaS preference model is crucial to support decision takers and decision makers before starting planning activities for new, sustainable transport services. In this paper, a pre-test model is proposed for evaluating user preferences. The pre-test model was specified with a Logit random utility model and the parameters were estimated using the maximum likelihood method. To define the preference model, a pilot survey was conducted in the Gioia Tauro area, an extra-urban area in southern Italy. For the pre-test model, a pilot sample of users was considered. In the area, a high percentage of users traveled by an individual transport system; this high percentage was also present in the survey, with 76% traveling by private car. Short- and long-distance scenarios were proposed to users. In the calibrated model, it emerged that bundles were more attractive for long-distance journeys and decreased with the cost of the package. The additional cost in the present scenario influenced the preference for bundle cost. Considering the parking cost in the present scenario (scenario 2), the MaaS preference probability started at higher probability values but increased less quickly. The pre-test model was defined starting from a pilot sample and represents the basis for a larger MaaS preference model built starting from a larger survey and a sample with a greater number of calibrated parameters.
Ecological Sensitivity of Urban Agglomeration in the Guanzhong Plain, China
Authors: Wei Xingtao , Eboy Oliver Valentine , Xu Lu , Yu Di
Published: March 2023

In the past two decades, China’s urbanization has advanced rapidly. In 2018, Xi’an was successfully selected as a national central city, and the Guanzhong Plain urban agglomeration (GZPUA) is emerging rapidly due to Xi’an. This study focuses on the current ecological status of the region and how to strike a balance between economic development and ecological protection. This study uses the ecological vulnerability of the Guanzhong urban agglomeration as a starting point to investigate the changes in its spatial and temporal distribution of ecological vulnerability and the primary driving factors, as well as to investigate the interaction between the changes in ecological vulnerability and urban agglomeration development in the GZPUA region. Using the “sensitivity–elasticity–pressure (SEP)” assessment framework model, this paper selects the spatial distribution data of natural, social, and economic sources in 2000 and 2020 based on the ecological environment characteristics of GZPUA. By using spatial principal component analysis, this paper quantitatively evaluates the ecological vulnerability changes of GZPUA in two periods, 2000 and 2020, with 1000 m × 1000 m raster as the evaluation unit, classifies the ecological vulnerability of the area into levels, and conducts a sub-regional in-depth study from different administrative regions. This research helps to comprehend the change in ecological environment quality in the GZPUA and provides a basis for ecological environment management decisions in the region. The results showed that (1) the ecological vulnerability of the Guanzhong Plain urban agglomeration as a whole is moderate, with the highest ecological vulnerability index (EVI) value of 0.89 and the lowest EVI value of 0.087 in 2000, and the highest EVI value of 0.93 and the lowest EVI value of 0.082 in 2020. The percentage of areas with the highest ecological vulnerability (moderate or severe) was 5.07% in 2000 and 15.11% in 2020. (2) The variation scope of the integrated EVI in the study region is 1.78–4.96 (2000) and 1.81–4.99 (2020), among which the EVI values in Xi’an, Bei Lin, Lian Hu, and Xin Cheng are the highest and the EVI values in Tai Bai, Zhou Zhi, and Feng Xian are the lowest. (3) In the central region of GZPUA, the spatial variation of CEVI is distributed in a circle that is highly congruent with the region’s economic and population development characteristics, whereas the spatial variation of CEVI in the southern mountainous region of the study area is primarily constrained by the topography and natural conditions. This region has low CEVI because of the high mountains and thick forests, which is a crucial ecological barrier for the GZPUA. (4) The EVI, ecological sensitivity index (ESI), ecological elasticity index (EEI), and ecological pressure index (EPI) have a strong relationship with land use. Among them, farmland and built-up land showed highly significant correlations with the EVI, ESI, EEI, and EPI (p < 0.01).
EEG-Based Performance-Driven Adaptive Automated Hazard Alerting System in Security Surveillance Support
Authors: Zhou Xiaoshan , Liao Pin-Chao
Published: March 2023

Automated vision-based hazard detection algorithms are being rapidly developed to provide hazard alerts for construction workers. However, these alerting systems often apply a fixed low-beta alerting threshold, which can cause excessive false alarms, followed by distractions and human distrust in automation. In this study, we propose a novel adaptive automated hazard alerting system capable of adjusting alert threshold levels based on environmental scenarios and workers’ hazard recognition performance evaluated using a wearable electroencephalogram (EEG) sensor system. We designed a hazard recognition experiment consisting of multiple hazardous scenarios and acquired behavioral data and EEG signals from 76 construction workers. We used the linear ballistic accumulator model to decompose hazard recognition into several psychological subcomponents and compared them among different scenarios. Subsequently, our proposed strategy includes clustering of participants’ hazard recognition performance levels based on latent profile analysis, wavelet transform of EEG signals, transfer learning for signal classification, and continual learning to improve the robustness of the model in different scenarios. The results show that the proposed method represents a feasible worker-centered adaptive hazard alerting approach. The anticipated system can be leveraged in a real-world wearable headset application that aims to promote proactive hazard intervention and facilitate human trust in automated hazard alerting technologies.
Firefly Optimization Heuristics for Sustainable Estimation in Power System Harmonics
Authors: Malik Naveed Ahmed , Chaudhary Naveed Ishtiaq , Raja Muhammad Asif Zahoor
Published: March 2023

The sustainable power development requires the study of power quality while taking into account of electrical equipment is an important aspect because it highly compromises the overall efficiency including quality, reliability and continuity of power flow. The aim for smooth power flow is only accomplished if compatibility is met between all the instruments connected to the system. The odd harmonics both on amplitude and phase domain must be known in order to exactly cop up with their adverse effects on overall working of the system. In this regard, parameter estimation is performed in detail for diverse generation size (gs) and particle size (ps), besides for altered signal to noise ratio. Firefly optimization technique under different scenarios for both phase and amplitude parameters accurately estimated the power signal harmonics and proved its robustness under different noise levels. The MSE values achieved by FFO are 6.54 × 10−3, 1.04 × 10−5 and 1.35 × 10−6 for 20 dB, 50 dB and 80 dB respectively for gs = 200 in case study 1. While the respective results in case study 2 are 7.33 × 10−3, 6.67 × 10−6 and 6.59 × 10−9 for gs = 1000. Whereas no significant effect in performance is seen with the change in ps values.
Machine Learning-Based Flexural Capacity Prediction of Corroded RC Beams with an Efficient and User-Friendly Tool
Authors: Abushanab Abdelrahman , Wakjira Tadesse Gemeda , Alnahhal Wael
Published: March 2023

Steel corrosion poses a serious threat to the structural performance of reinforced concrete (RC) structures. Thus, this study evaluates the flexural capacity of RC beams through machine learning (ML)-based techniques with six parameters used as input features: beam width, beam effective depth, concrete compressive strength, reinforcement ratio, reinforcement yield strength, and corrosion level. Four single and ensemble ML models are evaluated; namely, decision tree, support vector machine, adaptive boosting, and gradient boosting. Hyperparameters of each model were optimized using grid search and K-fold cross-validation with root mean squared error used as the performance index. The predictive performance of each model was assessed using four statistical performance metrics. The analysis results demonstrated that the decision tree model exhibited overfitting and limited generalization ability. The adaptive boosting model also had a slight overfitting issue. In addition, the support vector machine reported comparable accuracy to that of adaptive boosting. Conversely, the proposed gradient boosting ensemble model achieved the best performance with strong generalization ability, as indicated by its lowest mean absolute error of 2.78 kN.m, mean absolute percent error of 13.40%, and root mean squared error of 3.56 kN.m, and the highest coefficient of determination of 97.30% on the test dataset. The optimized gradient boosting model has been deployed into a graphical user interface, allowing for practical implementation of the model and enabling fast, efficient, and intelligent prediction of the flexural capacity of corroded RC beams.
Information Gap Decision Theory-Based Risk-Averse Scheduling of a Combined Heat and Power Hybrid Energy System
Authors: Shi Lumin , Tian Man-Wen , Alizadeh As’ad , Mohammadzadeh Ardashir , Nojavan Sayyad
Published: March 2023

This research investigates the optimal management of electric and heat energies in a hybrid energy system (HES). In the studied HES, a pair of photovoltaic and battery storage devices is used to supply the electricity demand, and a boiler system to supply the heat demand directly. In addition, a modified cycle power plant acted as a combined heat and power (CHP) unit to increase the generation capacity and supply reliability. The HES is also able to connect to the electric grid to exchange power according to real-time energy prices. The uncertainty of renewable generation, demand levels, and energy prices challenge the decision-making process. To deal with the uncertainty of these overlapping parameters, a comprehensive information-gap decision theory (IGDT) approach is proposed in this paper that, despite other works, considers the uncertainties in an integrated framework and derives risk-averse and risk seeker strategies in different steps. The problem is modeled as mixed-integer linear programming and solved using the GAMS optimization package. Concerning simulation results, from the viewpoint of a risk-seeking decision maker, the increment of the uncertainty degree by 10.906% results in a reduced operating cost of 8.6%. From the viewpoint of a risk-averse decision maker, the increment of the uncertainty degree by 10.208% results in 8.6% more operating cost.
Analysis of the Spatial and Temporal Variability and Factors Influencing the Ecological Resilience in the Urban Agglomeration on the Northern Slope of Tianshan Mountain
Authors: Tong Yanjun , Lei Jun , Zhang Shubao , Zhang Xiaolei , Rong Tianyu , Fan Liqin , Duan Zuliang
Published: March 2023

Based on land-use change data, this paper constructed an ecological resilience evaluation model from the three dimensions of resistance, adaptability, and regeneration capacity. The spatial and temporal evolution characteristics of the ecological resilience of urban agglomeration on the northern slope of Tianshan Mountain (UANST) from 1990 to 2020 were studied. The key factors affecting the spatial distribution of ecological resilience were detected. The results showed that (1) from 1990 to 2020, the mean ecological resilience values of the UANST were 0.3371, 0.3326, 0.3330, and 0.3240, showing an overall decreasing trend. The regions with low and medium values of ecological resilience contributed the most to these values. (2) The spatial distribution of the ecological resilience of the UANST was uneven, showing a “sandwich”-type distribution with low values in the south and north of the study area and high values in the middle of the study area. During the study period, the ecological resilience in the north part of the study area declined overall, while the ecological resilience in the south part of the study area increased continuously. (3) The results of the Geodetector model showed that natural and human factors jointly influenced the spatial distribution of the ecological resilience of the UANST, with natural factors dominating and temperature changes being the most sensitive. Finally, the impact of intense human activities on the ecological resilience of the UANST is increasing.
Probability-Based City-Scale Risk Assessment of Passengers Trapped in Elevators under Earthquakes
Authors: Gu Donglian , Wang Yixing , Lu Xinzheng , Xu Zhen
Published: March 2023

An accurate prediction of the number of passengers trapped in elevators under earthquakes in urban areas is essential for promoting earthquake emergencies. A probability-based city-scale method for assessing the earthquake-induced risk of passenger entrapment in elevators was proposed, in which city-scale time history analysis was performed to simulate the seismic response of building clusters, and the Monte Carlo simulation was conducted to consider the uncertainty of multiple factors, including the mechanical properties of buildings and elevators, the elevator story position, and the spatiotemporal nature of elevator traffics. A case study of the Tsinghua University campus was performed to demonstrate the practicability of the method. The results show that the number of trapped passengers when an earthquake occurs during the off-peak hours of elevator traffic is approximately a quarter of that when the earthquake occurs at 8:00. The maximum number of trapped passengers under the maximum considered earthquake reaches 195, approximately five times that under the design basis earthquake. This study fills a gap in the research on city-scale earthquake-induced passenger entrapment risk. The proposed method can be used to perform both scenario- and intensity-based assessments, thereby having the potential to facilitate virtual rescuer drills and earthquake emergency plans.
Linking Green Human Resource Practices and Sustainable Performance: The Mediating Role of Job Satisfaction and Green Motivation
Authors: Abdelhamied Hany Hosny , Elbaz Ahmed Mohamed , Al-Romeedy Bassam Samir , Amer Tamer Mohamed
Published: March 2023

The purpose of this study was to investigate the influence of job satisfaction and green motivation as mediators of green human resource practices and sustainable performance in the hotel industry. In order to collect information in a quantifiable manner, a questionnaire was issued to the employees of Egypt’s five- and four-star hotels. Perspectives of 333 employees working at 18 five- and four-star hotels out of forty-four hotels located in Greater Cairo were analyzed using the partial least squares structural equation model (PLS-SEM). The results supported the model’s validity and demonstrated that green human resource practices had a beneficial effect on job satisfaction. In addition, the results suggested that job satisfaction and “green motivation” significantly contributed to sustainable performance. In addition, the results demonstrated that work satisfaction acted as a partial and full mediator between the variables of sustainable human resource practices and sustainable performance. The ramifications of theory and management are examined.
Geochemical Characteristics and Factors of Transfer and Accumulation of Rare Earth Elements in Rock-Soil-Tea of the Mengku Tea Region in Yunnan Province, China
Authors: Xie Mengli , Chang He , Zhou Xiaohua , Zhu Jieyong , Chen Zhong , Yang Tianfu , Long Kun , Song Yinxian
Published: March 2023

Rare earth elements (REEs) in tea are usually determined by the soil, but their transfer characteristics and influencing factors have been rarely studied and reported. In order to determine the transfer and accumulation characteristics of REEs, rock, soil, and tea samples were collected in the Mengku tea region. Levels of 15 REEs (La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Y) in samples were determined by inductively coupled plasma atomic emission spectrometry (ICP-MS). The results showed that light rare earth elements (LREEs) were easily enriched in rock, soil, and tea. The average total concentration of REEs in investigated rocks was 199 mg·kg−1, which was lower than the element abundance of China’s continental crust but higher than the standard value of element abundance in the upper continental crust (UCC). The average total REE concentration in the investigated soil was 225 mg·kg−1, which was lower than the background value of soil in Yunnan Province. The chondrite-normalized levels of Ce and Eu in rocks and Eu in soil showed negative anomalies, while Ce levels in soil showed slightly positive anomalies. The total amount of REEs in tea was relatively low and the chondrite-normalized values of Eu in tea showed significantly positive anomalies. Eu anomalies in tea were closely correlated with soil pH (pH < 5, negative anomalies; pH > 5, positive anomalies). K, Na, Ca, and Mg were the main factors affecting the transfer of REEs. LREEs were mainly enriched in tea through Na+ channels. Mg and K affected the differentiation and enrichment of heavy rare earth elements (HREEs) in tea. Calcium showed a significantly positive correlation with Eu in tea. The human health risk assessment showed that the risk of drinking tea to the human body was far below the reference value. Ce, Y, and La in tea were the main elements that affected non-carcinogenic risk and carcinogenic risk.
Technology Gap Efficiency of Small-Scale Rice Processors in Anambra State, Nigeria
Authors: Obianefo Chukwujekwu A. , Ezeano Ike C. , Isibor Chinwe A. , Ahaneku Chinwendu E.
Published: March 2023

This paper aims to examine the technology gap efficiency of small-scale rice processors in Anambra State, Nigeria. The research was conducted through a survey of 100 small-scale rice processors in Anambra State. Data were collected with structured questionnaires and analyzed using descriptive, stochastic frontier analysis, stochastic meta-frontier model, and inferential statistical techniques. The study revealed that important variables to rice processing present in the state are paddy, firewood, water, and huller. Concerning the economic-specific factors, the cost of grading and other processing assets contributes to inefficiency. The results equally showed that the technology gap efficiency of small-scale rice processors in the industry is tied to or tangential to the frontier output, meaning that the processors in Anambra State are making use of the best technology available. The average efficiency index for the processors in the industry was 0.506, implying that their output is below potential by 49.4%. The results also revealed the technology gap efficiency for the participating and non-participating processors as 0.924 and 0.983, respectively, meaning that the participants need to close an 8.0% gap, and the non-participants need to close a 2.0% gap. These gaps are caused by the high cost of processing equipment, high cost of input, and inadequate infrastructure, among others contributing factors. The paper concluded that the average meta-technical efficiency (0.498) of small-scale rice processors in Anambra State is low and needs to be improved through the provision of adequate technology, training, and infrastructure to bring the current industrial production capacity to 100.0%.
The Climate Financialization Trap: Claiming for Public Action
Authors: Viganò Federica
Published: March 2023

This opinion paper aims to offer a critical assessment on the prevalence of solutions to the climate change problem that rely on climate finance. The paper briefly recalls the key milestones in the evolution of climate finance and highlights what is referred to as a “climate finance trap,” offering some crucial insights that might help remedy potential systemic distortions and that vehemently demand public action in the domain of climate policy.
Environmental Perceptions and Sustainable Consumption Behavior: The Disparity among South Africans
Authors: Kirsten Frederich , Eligius Biyase Mduduzi
Published: March 2023

The aim of this study is to assess the impact of sociodemographic factors on the environmental perceptions and sustainable consumption behavior in South Africa, a country with the highest record of inequality in the world. Few studies have examined the ways in which people in low-income countries perceive social and environmental problems. By using the International Social Survey Programme Environment III dataset for 2010, this study assessed the impact of sociodemographic factors on the environmental perceptions and sustainable consumption behavior of South Africans. The results show that environmental concern rates are highest among those with low socioeconomic status and African people. Since these individuals constitute the majority of the most vulnerable population in society, it supports the exposure to degradation hypothesis in a South African context. In contrast, sustainable consumption behavior rate is highest among those with high socioeconomic status, suggesting a strong post-materialist effect on pro-environmental consumption. From a policy perspective, environmental policymakers in South Africa could take note of the strong environmental concerns among those more vulnerable to daily environmental degradation and provide further incentives and support their transition to sustainable consumption behavior changes that would assist in environmental protection.
Influence of Pastoral Settlements Gradient on Vegetation Dynamics and Nutritional Characteristics in Arid Rangelands
Authors: Jamil Akash , Zubair Muhammad , Endress Bryan A.
Published: March 2023

An expansion of pastoral settlements in arid rangelands can increase pressure on fragile ecosystems. High stocking rates and inappropriate rangeland management can cause large, difficult-to-reverse changes in the composition and structure of rangeland ecosystems. This study aims to understand changes in vegetation composition (cover, density, biomass, richness, diversity) and nutritional characteristics of range vegetation along a gradient of increasing pastoral population in arid rangelands of Pakistan. Three sites were selected for sampling within three areas classified by their distance from settlement: Near, Away, Far (9 sites in total) belt transects (10 × 150 m). A total of 90 transects (30 each in classified sites) of size 10 × 150 m were placed at a distance of 100 m apart from each distance group. Results identified 28 species from nine plant families. We found a significant difference in vegetation characteristics along the gradient. Vegetation diversity increased along the settlement gradient, highest being in the far sites. Density, plant cover and biomass were greatest at the sites near pastoral communities and decreased as the distance from the settlements increased. Palatable species were characterized by low density and diversity near herder’s villages, but values increased as the distance from the villages increased. An increase in shrubs was observed near pastoral settlements, resulting in higher plant cover, biomass and density in these sites. This study identified anthropogenic impacts on rangeland structure and composition and found large shifts near communities. The frequent monitoring of vegetation resources is important, and the development of sustainable conservative strategies are recommended to ensure harmonious coexistence of pastoral populations in arid rangelands.
Learner-Centered Teaching Catalyzed by Teacher Learning Communities: The Mediating Role of Teacher Self-Efficacy and Collaborative Professional Learning
Authors: Pan Hui-Ling Wendy
Published: March 2023

Although there is evidence suggesting that teacher learning communities can improve instructional practices, the underlying mechanism is not well-understood. This study aimed to investigate the mediating role of self-efficacy and collaborative professional learning in the relationship between teachers’ experiences in learning communities and learner-centered teaching practices. The study surveyed 226 teachers from 28 schools in Taiwan and used structural equation modeling to analyze the data. The findings indicate that participation in teacher learning communities did not directly predict student-centered teaching practices, but, rather, its effect on learner-centered teaching was fully mediated by teacher self-efficacy. Additionally, a mediation path was found from teacher learning communities to teacher self-efficacy to collaborative professional learning and, ultimately, to learner-centered teaching. The study highlights the importance of developing teacher self-efficacy to achieve the desired outcomes of learner-centered teaching programs.
Equalization Measurement and Optimization of the Public Cultural Facilities Distribution in Tianjin Central Area
Authors: Zhang Sheng , Xu Shimin , Wan Da , Liu Hui , Zhao Lin , Guo Lian , Ren Juan
Published: March 2023

In the context of urban stock renewal, the spatial arrangement of public cultural facilities (PCFs) should follow the principles of equity and efficiency to ensure that residents have equitable access to and quality of public cultural services. The aim of this article is to study the spatial distribution of PCFs and the coupling of supply and demand of cultural resources in Tianjin’s central area. By building a supply-demand coupling coordination model and other methods, the equalization of the spatial distribution of PCFs is measured from various perspectives, and the results suggest that more than half of the sub-districts are in a situation of supply and demand imbalance. To fulfill the purpose of meeting residents’ actual needs, balancing supply and demand for cultural resources, and coordinating the increase in stock, these sub-districts’ facilities enter the step of optimization. Depending on the circumstances, the quality and scale of these facilities are optimized, or new facility points are added based on the maximized coverage model. The optimization is shown to be beneficial in terms of updating design and coverage quantity using two real-world cases. Finally, the coverage of facilities in the study area is maximized, facility utilization is made more efficient, and residents’ needs for public cultural services are satisfied.
Prospect Research on the Diversity of Extracellular Mineralization Process Induced by Mineralizing Microorganisms and Its Use as a Treatment for Soil Pollutants
Authors: Guo Baoyou , Liu Baolei , Chen Jun , Jing Chuan , Zhong Ming , Shan Qi
Published: March 2023

Microbial-induced mineralization is a process in which metal ions in the environment are processed by microorganisms, forming deposits of crystals with cementing and void-filling functions. Cementing crystals can fix metal ions, reduce permeability, improve soil strength, and play a positive role in soil remediation and pollution control. This paper first introduces the principle of microbial-induced mineralization and analyzes its mechanism of action in the treatment of soil organic and inorganic pollutants. Then, the mineralization principle of different types of mineralized bacteria in soil (fungal metabolism involving organic acid complexation and metabolic urease catalysis, sulfur oxidation by sulfur-oxidizing bacteria, dissimilatory sulfate reduction by sulfate-reducing bacteria, ammonification by ammoniating bacteria, reverse digestion by denitrifying bacteria, urease catalysis by urease-producing bacteria, acetic acid fermentation by methanogenic bacteria, and H2/CO2 reduction) is elaborated, the influencing factors in the treatment of soil pollutants by mineralization technology in practical application are analyzed, and the current status of mineralization treatment for different types of pollutants is summarized. Finally, the future prospects of soil pollutant treatment are outlined to promote research into microbial-induced mineralization technology for the treatment of soil pollutants.
A Critique on Public–Private–People Partnerships: From a Definitional Inconsistency to the Partnering Dilemma in Today’s Housing Conjunction
Authors: Miranda-Poggys Alex G. , Morena Marzia
Published: March 2023

This paper showcases the first exhaustive literature review on the public–private–people partnership (4P), as a concept on its own, its backstory, its proposed definitions, and multiple interpretations throughout the last fifteen years. Beyond mapping the discussion on what does the extra ‘P’—the people—component of these partnerships really mean, and how it is understood by scholars and the traditional public and private partners alike, this work also aims to be the starting point for new research avenues, such as corporate–community cooperation, and urban regeneration through different land use and ownership schemes. Hence, the discussion spreads onto analyzing how the concept is approached from different professional fields—for its inherent interdisciplinarity, despite the perceived scarcity of related literature available—but also onto the ambivalence stemming from the nature of the ‘people’ component itself, its functions, and the relationships—formal or informal—that should be established. Finally, the authors go on to critically place the concept within the current urban and real estate trends in both, the Global North and South, while identifying further gaps in the current literature, for example: given today’s critical housing supply and affordability situations, how to get the public and private sectors interested in partnering with people in a more direct way? What needs to be done? What needs to be further researched?
Insect-Derived Chitin and Chitosan: A Still Unexploited Resource for the Edible Insect Sector
Authors: Rehman Kashif ur , Hollah Clemens , Wiesotzki Karin , Heinz Volker , Aganovic Kemal , Rehman Rashid ur , Petrusan Janos-Istvan , Zheng Longyu , Zhang Jibin , Sohail Summar , Mansoor Muhammad Khalid , Rumbos Christos I. , Athanassiou Christos , Cai Minmin
Published: March 2023

Chitin and chitosan are biopolymers that are frequently found in nature and have a broad range of applications in the food, biomedical and industrial sectors, due to their high biological activity. The primary source of chitin and chitosan is shellfish, however, shortages in the supply chain, seasonality issues in their availability, as well as ecological degradation are only a few of the problems with the main chitin resources. Due to the broad spectrum of applications for which chitin can be used, the demand for chitin and its derivatives is increasing. Therefore, the market is looking for widely available, greener alternatives to the main commercial chitin sources. Insects appear as a suitable candidate to fill this gap. During insect rearing and processing, a number of side streams are generated, e.g., exuviae of larvae and pupae, dead adults, etc. which are currently mostly discarded as waste. However, these side streams could constitute a novel and long-term supply of chitin for industrial applications. Recent research has demonstrated the suitability of several edible insect species for the production of chitin and chitosan, wherein the exoskeleton of the black soldier fly and field cricket are rich in chitin, making them a good source for chitin and chitosan extraction and purification among other farmed insect candidates. Moreover, several potential uses have been identified for insect-derived chitin and chitosan. Thus, this review aims to present recent advances in the production of chitin and chitosan from edible insects, specifically on their extraction and purification, as well as on their applications for agriculture, food and nutrition, biomedicine and bioplastic production.
A Study on the Evacuation of an Extra-Long Highway Tunnel Fire—A Case Study of Chengkai Tunnel
Authors: Wang Kai , Hu Jingwei , Chen Ruiding , Wang Jianhua
Published: March 2023

The smoke from tunnel fires spreads over long distances and is difficult to vent. Smoke accumulation leads to high temperatures, low visibility, and high concentrations of toxic gases, which greatly hinders the evacuation of people inside the tunnel. In this paper, a representative extra-long highway tunnel—Chengkai Tunnel—is selected as the engineering background, and a tunnel model is built using FDS and Pathfinder software to simulate the fire scenario and evacuation scenario under different longitudinal wind speeds. The concept of safe evacuation reliability is proposed to describe the relationship between the ASET (available safe egress time) and the RSET (required safe egress time). The simulation results show that with the increase in longitudinal wind speed, the ASET upstream of fire source increases first and then remains unchanged, while ASET downstream of fire source increases first and then decreases. The ASET upstream of the fire source is affected by visibility, while the ASET downstream of the fire source is affected by visibility when the wind speed is low, and is affected by temperature as the wind speed increases. The bottleneck effect is an important reason for the long evacuation time of people. The blockage time is a power function of the evacuation movement time, and increasing the width of the cross passage can improve the evacuation efficiency of the tunnel. The increase in the number of evacuees will reduce the reliability of the safe evacuation of personnel. Among all simulated scenarios, a longitudinal wind speed of 2.5 m/s has the highest safe evacuation reliability, with 0.79, 0.92, and 0.99 for scenarios R1, R2, and R3, respectively. Excessive wind speed reduces the safe evacuation reliability downstream of the fire source.
Prevention and Detection of Electricity Theft of Distribution Network
Authors: Ali Sajad , Yongzhi Min , Ali Wajid
Published: March 2023

Electricity theft is a costly problem. This paper will be focused on Pakistan and the problem of electricity theft. We will discuss its impacts and how best to fix them through the use of technology. For this purpose, we developed a smart meter, focusing on grid modernization through economic smart meter development. This paper focuses on a study carried out with the help of PESCO. It is one of the most inefficient distribution providers. The study has evaluated commercial, industrial, rural, and urban areas, covering a total area of 15 km2. The area includes several power sinks. Previous research has been used to compare the results of this case study; this included studies of other Third World countries, such as Pakistan and South Africa. The design of, clever, innovative, intelligent meters used in this study was better than the basic digital meters and had many features compatible with the E.U., and U.S.A.’s western power market and energy infrastructure. The study also discusses the potential use of neural network-trained models and IoT (internet of things) integration with cloud computing. This can provide an alternate means of data analysis, accurate prediction, and greater user accessibility. The case study is the first ever done using smart meters on such a large scale, and the compiled data has provided insight into energy consumers and their usage. The statistics can be used to isolate the most probable cause of theft and the area or location of occurrence.