bluuebunny/crossref_metadata_2025 huggingface dataset:
Infertility Treatment and Hypertension in Pregnancy: The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study
Authors: Ishikuro Mami , Obara Taku , Murakami Keiko , Ueno Fumihiko , Noda Aoi , Onuma Tomomi , Matsuzaki Fumiko , Kikuya Masahiro , Watanabe Zen , Shiga Naomi , Tachibana Masahito , Iwama Noriyuki , Hamada Hirotaka , Saito Masatoshi , Sugawara Junichi , Metoki Hirohito , Yaegashi Nobuo , Kuriyama Shinichi
Published: April 2023

Infertility treatment is a possible factor in hypertensive disorders of pregnancy (HDP). Identifying the characteristics of pregnant women who have undergone infertility treatment and have a potential risk for HDP is valuable for its prevention and treatment. Using data from 12,456 pregnant Japanese women from the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study, the association between infertility treatment and HDP was analyzed. A multiple logistic regression model showed an association between infertility treatment and HDP (odds ratio, 1.34; 95% confidence interval, 1.05–1.72). In vitro fertilization/intracytoplasmic sperm injection were also associated with HDP. Moreover, these associations were observed even among women who were not overweight and did not smoke. The application of infertility treatment should be carefully considered, even among women with low modifiable risk factors.
Centroid Extraction of Laser Spots Captured by Infrared Detectors Combining Laser Footprint Images and Detector Observation Data
Authors: Yang Xiaomeng , Xie Junfeng , Liu Ren , Mo Fan , Zeng Junze
Published: April 2023

On-orbit geometric calibration of satellite-borne laser based on infrared detectors is the key tool to ensure the elevation measurement accuracy, and the accuracy of on-orbit geometric calibration is directly determined by laser spots captured by detectors. Mathematical methods, such as gray-scale barycenter, are widely applied for centroid extraction of spots captured by infrared detectors and completely depend on the energy values at points measured by detectors, which have low precision and are greatly affected by the consistency of the detectors and other factors at present. Based on the above question, considering the consistency between the real laser footprint shape and spot captured by detectors, a centroid extraction method of laser spots captured by infrared detectors combining laser footprint images and detector observation data is proposed for making up this defect to some extent. First, the self-adaptive “two-step method” is used to denoise footprint images hierarchically to obtain the real shape of footprints for constraining the spots captured by detectors, and then the centroids of spots are extracted by using the energy-weighted barycenter method based on regional blocks. In the experiment, Gaofen-7 (GF-7) satellite is taken as the research object, and the proposed method, as well as the other six methods, are used for the centroid extraction of laser spots captured by detectors, the calculation of calibration parameters based on the single-beam and dual-beam laser calibration models, the positioning of laser footprints, and cross verification. According to the results, the plane accuracy of centroid extraction using the proposed method is as follows: 0.34 grids for Beam 1 and 0.33 grids for Beam 2. In addition, on flat terrain, the elevation accuracy of Beam 1 and Beam 2 in 2021 is 5.2 cm and 5.0 cm, respectively, 0.6 cm and 4.2 cm higher than those in the most accurate one among other methods; the elevation accuracy in 2020 is 23.3 cm and 7.1 cm, respectively, 7.7 cm and 2.7 cm higher than those in the most accurate one among other methods. On slopes and gentle slopes, the method proposed is also superior to other methods. Since the change of pointing angle caused by satellite jitter, atmosphere, etc., between different years, the accuracy drops when laser footprints of 2020 are located using the parameters of 2021. In summary, under different terrains and years, the results fully demonstrate the effectiveness and accuracy of the proposed method, which has more significant advantages than other traditional methods.
Convolutional Neural Network Shows Greater Spatial and Temporal Stability in Multi-Annual Land Cover Mapping Than Pixel-Based Methods
Authors: Boston Tony , Van Dijk Albert , Thackway Richard
Published: April 2023

Satellite imagery is the only feasible approach to annual monitoring and reporting on land cover change. Unfortunately, conventional pixel-based classification methods based on spectral response only (e.g., using random forests algorithms) have shown a lack of spatial and temporal stability due, for instance, to variability between individual pixels and changes in vegetation condition, respectively. Machine learning methods that consider spatial patterns in addition to reflectance can address some of these issues. In this study, a convolutional neural network (CNN) model, U-Net, was trained for a 500 km × 500 km region in southeast Australia using annual Landsat geomedian data for the relatively dry and wet years of 2018 and 2020, respectively. The label data for model training was an eight-class classification inferred from a static land-use map, enhanced using forest-extent mapping. Here, we wished to analyse the benefits of CNN-based land cover mapping and reporting over 34 years (1987–2020). We used the trained model to generate annual land cover maps for a 100 km × 100 km tile near the Australian Capital Territory. We developed innovative diagnostic methods to assess spatial and temporal stability, analysed how the CNN method differs from pixel-based mapping and compared it with two reference land cover products available for some years. Our U-Net CNN results showed better spatial and temporal stability with, respectively, overall accuracy of 89% verses 82% for reference pixel-based mapping, and 76% of pixels unchanged over 33 years. This gave a clearer insight into where and when land cover change occurred compared to reference mapping, where only 30% of pixels were conserved. Remaining issues include edge effects associated with the CNN method and a limited ability to distinguish some land cover types (e.g., broadacre crops vs. pasture). We conclude that the CNN model was better for understanding broad-scale land cover change, use in environmental accounting and natural resource management, whereas pixel-based approaches sometimes more accurately represented small-scale changes in land cover.
Accelerated Glacier Area Loss in the Zhetysu (Dzhungar) Alatau Range (Tien Shan) for the Period of 1956–2016
Authors: Nurakynov Serik , Kaldybayev Azamat , Zulpykharov Kanat , Sydyk Nurmakhambet , Merekeyev Aibek , Chepashev Daniker , Nyssanbayeva Aiman , Issanova Gulnura , Fang Gonghuan
Published: April 2023

An updated glacier inventory is important for understanding the current glacier dynamics in the conditions of actual accelerating glacier retreat observed around the world. Here, we present a detailed analysis of the glaciation areas of the Zhetysu Alatau Range (Tien Shan) for 1956–2016 using well-established semiautomatic methods based on the band ratios. The total glacier area decreased by 49 ± 2.8% or by 399 ± 11.2 km2 from 813.6 ± 22.8 km2 to 414.6 ± 11.6 km2 during 1956–2016, while the number of glaciers increased from 985 to 813. Similar rates of area change characterized the periods 1956–2001, 2001–2012, 2012–2016, and 2001–2016: −296.2 ± 8.3 (−0.8% a−1), −63.7± 1.8 (−1.1% a−1), −39.1 ±1.1 (−2.2% a−1) and −102.8 ± 2.9 (−1.3% a−1) km2, respectively. The mean glacier size decreased from 0.57 km2 in 2001 to 0.51 km2 in 2016. Most glaciation areas of the Zhetysu Alatau faced north (north, northwest, and northeast), covered 390.35 ± 11 km2, and were located in altitudes between 3000 and 4000 m.a.s.l. With shrinkage rates of about −0.8% and −1.3% a−1 for the periods of 1956–2001 and 2001–2016, our results show that study area has the highest shrinkage rate compared to other glacierized areas of Central Asian mountains, including Altai, Pamir, and even the inner ranges of Tien Shan. It was found that a significant increase in temperature (0.12 °C/10 years) plays a main role in the state of glaciers.
Recalibrating Features and Regression for Oriented Object Detection
Authors: Chen Weining , Miao Shicheng , Wang Guangxing , Cheng Gong
Published: April 2023

The objects in remote sensing images are normally densely packed, arbitrarily oriented, and surrounded by complex backgrounds. Great efforts have been devoted to developing oriented object detection models to accommodate such data characteristics. We argue that an effective detection model hinges on three aspects: feature enhancement, feature decoupling for classification and localization, and an appropriate bounding box regression scheme. In this article, we instantiate the three aspects on top of the classical Faster R-CNN, with three novel components proposed. First, we propose a weighted fusion and refinement (WFR) module, which adaptively weighs multi-level features and leverages the attention mechanism to refine the fused features. Second, we decouple the RoI (region of interest) features for the subsequent classification and localization via a lightweight affine transformation-based feature decoupling (ATFD) module. Third, we propose a post-classification regression (PCR) module for generating the desired quadrilateral bounding boxes. Specifically, PCR predicts the precise vertex location on each side of a predicted horizontal box, by simply learning the following: (i) classify the discretized regression range of the vertex, and (ii) revise the vertex location with an offset. We conduct extensive experiments on the DOTA, DIOR-R, and HRSC2016 datasets to evaluate our method.
Mapping Tree Species Using CNN from Bi-Seasonal High-Resolution Drone Optic and LiDAR Data
Authors: Lee Eu-Ru , Baek Won-Kyung , Jung Hyung-Sup
Published: April 2023

As the importance of forests has increased, continuously monitoring and managing information on forest ecology has become essential. The composition and distribution of tree species in forests are essential indicators of forest ecosystems. Several studies have been conducted to classify tree species using remote sensing data and machine learning algorithms because of the constraints of the traditional approach for classifying tree species in forests. In the machine learning approach, classification accuracy varies based on the characteristics and quantity of the study area data used. Thus, applying various classification models to achieve the most accurate classification results is necessary. In the literature, patch-based deep learning (DL) algorithms that use feature maps have shown superior classification results than point-based techniques. DL techniques substantially affect the performance of input data but gathering highly explanatory data is difficult in the study area. In this study, we analyzed (1) the accuracy of tree classification by convolutional neural networks (CNNs)-based DL models with various structures of CNN feature extraction areas using a high-resolution LiDAR-derived digital surface model (DSM) acquired from a drone platform and (2) the impact of tree classification by creating input data via various geometric augmentation methods. For performance comparison, the drone optic and LiDAR data were separated into two groups according to the application of data augmentation, and the classification performance was compared using three CNN-based models for each group. The results demonstrated that Groups 1 and CNN-1, CNN-2, and CNN-3 were 0.74, 0.79, and 0.82 and 0.79, 0.80, and 0.84, respectively, and the best mode was CNN-3 in Group 2. The results imply that (1) when classifying tree species in the forest using high-resolution bi-seasonal drone optical images and LiDAR data, a model in which the number of filters of various sizes and filters gradually decreased demonstrated a superior classification performance of 0.95 for a single tree and 0.75 for two or more mixed species; (2) classification performance is enhanced during model learning by augmenting training data, especially for two or more mixed tree species.
Multi-Domain Feature Alignment for Face Anti-Spoofing
Authors: Zhang Shizhe , Nie Wenhui
Published: April 2023

Face anti-spoofing is critical for enhancing the robustness of face recognition systems against presentation attacks. Existing methods predominantly rely on binary classification tasks. Recently, methods based on domain generalization have yielded promising results. However, due to distribution discrepancies between various domains, the differences in the feature space related to the domain considerably hinder the generalization of features from unfamiliar domains. In this work, we propose a multi-domain feature alignment framework (MADG) that addresses poor generalization when multiple source domains are distributed in the scattered feature space. Specifically, an adversarial learning process is designed to narrow the differences between domains, achieving the effect of aligning the features of multiple sources, thus resulting in multi-domain alignment. Moreover, to further improve the effectiveness of our proposed framework, we incorporate multi-directional triplet loss to achieve a higher degree of separation in the feature space between fake and real faces. To evaluate the performance of our method, we conducted extensive experiments on several public datasets. The results demonstrate that our proposed approach outperforms current state-of-the-art methods, thereby validating its effectiveness in face anti-spoofing.
Octopus: A Novel Approach for Health Data Masking and Retrieving Using Physical Unclonable Functions and Machine Learning
Authors: Satra Sagar , Sadhu Pintu Kumar , Yanambaka Venkata P. , Abdelgawad Ahmed
Published: April 2023

Health equipment are used to keep track of significant health indicators, automate health interventions, and analyze health indicators. People have begun using mobile applications to track health characteristics and medical demands because devices are now linked to high-speed internet and mobile phones. Such a combination of smart devices, the internet, and mobile applications expands the usage of remote health monitoring through the Internet of Medical Things (IoMT). The accessibility and unpredictable aspects of IoMT create massive security and confidentiality threats in IoMT systems. In this paper, Octopus and Physically Unclonable Functions (PUFs) are used to provide privacy to the healthcare device by masking the data, and machine learning (ML) techniques are used to retrieve the health data back and reduce security breaches on networks. This technique has exhibited 99.45% accuracy, which proves that this technique could be used to secure health data with masking.
Flexible Magnetic Sensors
Authors: Pan Lili , Xie Yali , Yang Huali , Li Mengchao , Bao Xilai , Shang Jie , Li Run-Wei
Published: April 2023

With the merits of high sensitivity, high stability, high flexibility, low cost, and simple manufacturing, flexible magnetic field sensors have potential applications in various fields such as geomagnetosensitive E-Skins, magnetoelectric compass, and non-contact interactive platforms. Based on the principles of various magnetic field sensors, this paper introduces the research progress of flexible magnetic field sensors, including the preparation, performance, related applications, etc. In addition, the prospects of flexible magnetic field sensors and their challenges are presented.
Experimental Investigation of Tip Wear of AFM Monocrystalline Silicon Probes
Authors: Huang Song , Tian Yanling , Wang Tao
Published: April 2023

AFM has a wide range of applications in nanostructure scanning imaging and fabrication. The wear of AFM probes has a significant impact on the accuracy of nanostructure measurement and fabrication, which is particularly significant in the process of nanomachining. Therefore, this paper focuses on the study of the wear state of monocrystalline silicon probes during nanomachination, in order to achieve rapid detection and accurate control of the probe wear state. In this paper, the wear tip radius, the wear volume, and the probe wear rate are used as the evaluation indexes of the probe wear state. The tip radius of the worn probe is detected by the nanoindentation Hertz model characterization method. The influence of single machining parameters, such as scratching distance, normal load, scratching speed, and initial tip radius, on probe wear is explored using the single factor experiment method, and the probe wear process is clearly divided according to the probe wear degree and the machining quality of the groove. Through response surface analysis, the comprehensive effect of various machining parameters on probe wear is determined, and the theoretical models of the probe wear state are established.
Fuzzy Control Application to an Irrigation System of Hydroponic Crops under Greenhouse: Case Cultivation of Strawberries (Fragaria Vesca)
Authors: Maya Olalla Edgar , Lopez Flores Andres , Zambrano Marcelo , Domínguez Limaico Mauricio , Diaz Iza Henry , Vasquez Ayala Carlos
Published: April 2023

Hydroponics refers to a modern set of agricultural techniques that do not require the use of natural soil for plant germination and development. These types of crops use artificial irrigation systems that, together with fuzzy control methods, allow plants to be provided with the exact amount of nutrients for optimal growth. The diffuse control begins with the sensorization of the agricultural variables that intervene in the hydroponic ecosystem, such as the environmental temperature, electrical conductivity of the nutrient solution and the temperature, humidity, and pH of the substrate. Based on this knowledge, these variables can be controlled to be within the ranges required for optimal plant growth, reducing the risk of a negative impact on the crop. This research takes, as a case study, the application of fuzzy control methods to hydroponic strawberry crops (Fragaria vesca). It is shown that, under this scheme, a greater foliage of the plants and a larger size of the fruits are obtained in comparison with natural cultivation systems in which irrigation and fertilization are carried out by default, without considering the alterations in the aforementioned variables. It is concluded that the combination of modern agricultural techniques such as hydroponics and diffuse control allow us to improve the quality of the crops and the optimization of the required resources.
Paper Mill Biosolids and Forest-Derived Liming Materials Applied on Cropland: Residual Effects on Soil Properties and Metal Availability
Authors: Gagnon Bernard , Ziadi Noura
Published: April 2023

Combined paper mill biosolids (PB) and forest-derived liming by-products improve soil properties, but their residual effects following several years of application have hardly been investigated. A 13-year (2009–2021) field study was initiated at Yamachiche, QC, Canada, to assess the residual effects of PB and liming materials on the properties of a loamy soil. The PB was applied during nine consecutive years (2000–2008) at 0, 30, 60, and 90 Mg wet·ha−1, whereas the 30 Mg PB·ha−1 rate also received one of three liming materials (calcitic lime, lime mud, wood ash) at 3 Mg wet·ha−1. No amendment was applied during residual years. Past liming materials continued to increase soil pH but their effect decreased over time; meanwhile, past PB applications caused a low increase in residual soil NO3-N. Soil total C, which represented 40% of added organic C when PB applications ceased, stabilized to 15% after six years. Soil Mehlich-3-extractable contents declined over the thirteen residual years to be not significant for P, K, and Cu, while they reached half the values of the application years for Zn and Cd. Conversely, Mehlich-3 Ca was little affected by time. Therefore, land PB and liming material applications benefited soil properties several years after their cessation.
A Review of the Current State of Microplastic Pollution in South Asian Countries
Authors: Sin Lee Tin , Balakrishnan Vineshaa , Bee Soo-Tueen , Bee Soo-Ling
Published: April 2023

Microplastic contamination has become a concerning topic of study in recent decades. This review discusses the development of microplastic pollution based on a selection of South Asian countries consisting of Bangladesh, Iran, Philippines, Thailand, India, Indonesia, and Vietnam. The condition of microplastic pollution related to the abundance of microplastic found in various environments as well as the presence of microplastics in food and the air, is covered in this review. Several reports found that drinking water sourced from taps was found to have about 83% of microplastic particles in the year 2017 based on results from 14 nations, and in the year 2018, 260 bodies of water for human consumption in 11 countries were found to have about 93% of microplastic particles. Micro debris pollution in seas and oceans worldwide is predicted to be at an amount of 236,000 metric tons based on a statistical report. A mean value of 30 micro debris per liter of glacier water was recovered from the top of Mount Everest, whereas about 2200 small particles per liter were discovered in the deep waters of the Mariana Trench. The main environments that are severely microplastic-contaminated are water-based places such as rivers, estuaries, and beaches. The presence of microplastics in food items, such as tea bags, sugar, shrimp paste, and salt packets, has been reported. In terms of impacts on the environment, microplastic contamination includes the ingestion of microplastics by aquatic creatures in water environments. The impacts on terrestrial environments relate to microplastics sinking into the soil, leading to the alteration of the physicochemical parameters of soil. Meanwhile, the impacts on the atmospheric environment include the settling of microplastics on the external bodies of animals and humans.
Associating Metrics of Hunting Effort with Hunting Rate: A Case Study with the Wild Boar Sus scrofa
Authors: Varuzza Paolo , Lombardini Marco , Toscano Valerio , Argenio Felice , D’Alessio Nicola , Caputo Vincenzo , Veneziano Vincenzo , Fioretti Alessandro
Published: April 2023

Wild boar Sus scrofa populations have increased dramatically in recent decades throughout Europe. While hunting is widely used in management activities; it rarely has an important role in regulating and reducing wild boar populations. Therefore, increasing the efficiency of hunting is a compelling issue. In this study, we used a three-year dataset (2016–2018) on a wild boar population living in Campania (southern Italy) as a case study to explore how the hunting effort made in collective drive hunts affected the hunting rate, estimated as the number of individuals culled per day. We fitted a Linear Mixed Model, in which we included the number of wild boars culled per drive hunt as the dependent variable, and the number of beaters, shooters and dogs and the month during which hunting occurred as the predictors. A mean of 1.81 wild boars were culled per drive hunt. The number of culled animals per hunt increased with the increasing number of hunting dogs and with the progression of the hunting season (i.e., from October to December), whereas the number of beaters and shooters had no effect. Overall, we observed a low hunting rate. We suggest that adjusting the hunting calendar and reorganising wild boar collective hunts, e.g., through an appropriate management of the number and training of hunting dogs, are essential to increase the hunting rate. Our results can be useful for wildlife managers to enhance hunting contribution in counteracting the negative impact of wild boar.
A Decision Support Model for Cost-Effective Choice of Temperature-Controlled Transport of Fresh Food
Authors: Maiyar Lohithaksha M. , Ramanathan Ramakrishnan , Roy Indira , Ramanathan Usha
Published: April 2023

The application of a plethora of wireless technologies to support real-time food quality monitoring during transportation has significantly improved the performance of fresh food delivery systems. However, deployment of these technologies increases the capital and operational costs of food delivery and, hence, not all food delivery operations need to employ them. This paper looks at the trade-off of the costs involved in utilizing these technologies with the nature of food delivered, the length of transportation, and the perceived costs of food wasted using a linear programming model. The problem is formulated over a bi-echelon network with the possibility of transporting the fresh produce through dry vans, vans with temperature control but without monitoring capability, and vans with temperature control and monitoring capability. Results indicate that under situations of infinite vehicle resource availability, the optimal choice of the van type is independent of the demand levels; however, the optimal choice changes for different travel distances and the value of penalty costs (of allowing food to go waste). For example, technologies that maintain and monitor the temperature of storage conditions will be useful for food items that quickly become waste, especially when transported over longer distances and when the penalty costs are higher.
Atlantic Forest Ecosystems: Are There Significant Differences When Compared at the Same Stage of Regeneration?
Authors: Bressane Adriano , Pinto João Pedro da Cunha , da Silva Julia Villalta , Silva Mirela Beatriz , Siminski Alexandre , Medeiros Líliam César de Castro , Negri Rogério Galante
Published: April 2023

For the monitoring and assessment of Atlantic Forest ecosystems, key indicators of the regeneration stage are considered. However, the current classification of these stages by experts does not consider the possible differences among such ecosystems. In order to test the hypothesis of significant differences, Atlantic Forest ecosystems in southern Brazil were compared at the same stage (initial, medium, and advanced stages of regeneration). An extensive database of the floristic forest inventory of Santa Catarina state, southern Brazil, with 460 sampling units, was used, addressing the seasonal deciduous forest (SDF), dense ombrophilous forest (DOF), and mixed ombrophilous forest (MOF). The regeneration stage of each sample unit was previously classified by experts using five key indicators (basal area per hectare—BA/ha; number of individuals per hectare—N/ha; number of species—S; Shannon biodiversity index—H’; and diameter at breast height—DBH). The Kruskal–Wallis method and pairwise multiple comparisons based on Dunn’s procedure were applied, considering two-way tests with 5% significance, and 95% power. The statistical tests confirmed the research hypothesis, namely, that the key indicators had significant differences in the later regeneration stages of Atlantic Forest ecosystems. For instance, S in DOF was statistically higher (p < 0.001) than in MOF and SDF (44 ± 9); N in SDF was significantly lower (394 ± 248 at mid-stage) compared to MOF (475 ± 233) and DOF (545 ± 173); and H’ showed increasing differences towards more advanced stages. Considering that the stage of forest regeneration is the main criterion for decision-making between suppression and conservation of forest ecosystems, the results achieved should support the review of current procedures applied to the classification of successional stages. Given the demonstrated differences, in the next steps of research, we will evaluate and propose specific standards for each Atlantic Forest ecosystem, i.e., intervals of discriminating values for the key indicators applicable to this biome.
Heritability and Selection Using GGE Biplots and the Sustainability Index (SI) of Maize Mutants under Different Cropping Systems in Upland
Authors: Maulana Haris , Maxiselly Yudithia , Yuwariah Yuyun , Ruswandi Dedi
Published: April 2023

A good maize plant breeding program must consider the effect of genotype-by-environment interactions (GEIs) and the correlation of important traits. The purpose of this study was to evaluate genetic variation, identify and investigate the implications of GEIs on breeding maize mutants in order to determine the ideal genotypes (stable and high yield), identify the best intercropping system for maize mutants, and identify the heritability and relationships of the traits tested that are important in cultivation. This research was carried out in five different intercropping systems in Upland West Java, Indonesia. A randomized block design with two replications was used in the field experiment. The measurement results revealed that the genetic diversity of maize mutants is broad, with six axes having eigenvalues ranging from 8.76 to 1.07 and a cumulative value of 76.64%. The neighbor-joining tree also showed a wide range of variation, yielding five distinct groups. The GEIs had a significant effect on the 14 traits tested, particularly yield. The environment had a significant impact on the variation of all the traits tested. The ideal cropping system for selecting the ideal maize mutant was Environment 4 (maize + rice). Superior maize mutants were successfully selected based on the GGE biplot. Thus, the sustainability index (SI) was used to successfully select maize mutants that were superior by 20.00%. These two methods selected only 15.00% of the maize mutants. One trait has a low heritability value, five traits have a moderate heritability value, and 17 traits have a high heritability value. The yields correlated positively and significantly with nine yield traits. Future maize-breeding programs can be based on data on genetic diversity, heritability, and the relationship of the traits tested. The best intercropping system can be used as an alternative for increasing maize cultivation income. Selected maize mutants can be proposed as new superior genotype candidates.
Heat Utilization Characteristics of Two Sensible Heat Storage Vegetable Oils for Domestic Applications
Authors: Abedigamba Oyirwoth P. , Mndeme Sayuni F. , Mawire Ashmore , Rukaaya Musa
Published: April 2023

The heat utilization characteristics of two different sensible heat storage vegetable oils for domestic applications are evaluated. Heat utilization is the heat delivered to the cooking fluid/load. The two sensible heat storage vegetable oils are sunflower oil, and Roki oil (a blend of palm oil and sunflower oil). The heat utilization characteristics of the two heat storage materials are experimentally determined in water heating experiments using 1.0, 1.5, 2.0, and 2.5 kg water loads. The stored heat in Roki oil and sunflower oil is used to heat up the water during cool down/heat utilization tests. The reason for the research is that new insights into the utilization of a locally available vegetable oil (Roki oil) in Uganda, as a sensible thermal energy storage material, is determined. Additionally, a new storage pot is designed, tested, and compared with Roki oil and sunflower oil as sensible thermal energy storage materials. The highlights of the work are that average heat utilization values are dependent on the water heating load. The average heat utilization efficiency increases with the increase in the water heating load; Roki oil shows higher heat utilization and higher average heat utilization efficiency values as compared to sunflower oil; the average heat utilization and average heat utilization efficiency values increase with the increase in the water load for both Sunflower oil and Roki oil. The results suggest that Roki oil is a potential heat storage material for domestic applications since it shows better heat utilization characteristics than sunflower oil during heat utilization.
A Literature Review on System Dynamics Modeling for Sustainable Management of Water Supply and Demand
Authors: Naeem Khawar , Zghibi Adel , Elomri Adel , Mazzoni Annamaria , Triki Chefi
Published: April 2023

Water supply and demand management (WSDM) is essential for developing sustainable cities and societies. WSDM is only effective when tackled from the perspective of a holistic system understanding that considers social, environmental, hydrological, and economic (SEHEc) sub-systems. System dynamics modeling (SDM) is recommended by water resource researchers as it models the biophysical and socio-economic systems simultaneously. This study presents a comprehensive literature review of SDM applications in sustainable WSDM. The reviewed articles were methodologically analyzed considering SEHEc sub-systems and the type of modeling approach used. This study revealed that problem conceptualization using the causal loop diagram (CLD) was performed in only 58% of the studies. Moreover, 70% of the reviewed articles used the stock flow diagram (SFD) to perform a quantitative system analysis. Furthermore, stakeholder engagement plays a significant role in understanding the core issues and divergent views and needs of users, but it was incorporated by only 36% of the studies. Although climate change significantly affects water management strategies, only 51% of the reviewed articles considered it. Although the scenario analysis is supported by simulation models, they further require the optimization models to yield optimal key parameter values. One noticeable finding is that only 12% of the articles used quantitative models to complement SDM for the decision-making process. The models included agent-based modeling (ABM), Bayesian networking (BN), analytical hierarchy approach (AHP), and simulation optimization multi-objective optimization (MOO). The solution approaches included the genetic algorithm (GA), particle swarm optimization (PSO), and the non-dominated sorting genetic algorithm (NSGA-II). The key findings for the sustainable development of water resources included the per capita water reduction, water conservation through public awareness campaigns, the use of treated wastewater, the adoption of efficient irrigation practices including drip irrigation, the cultivation of low-water-consuming crops in water-stressed regions, and regulations to control the overexploitation of groundwater. In conclusion, it is established that SDM is an effective tool for devising strategies that enable sustainable water supply and demand management.
The Embedded Agroecology of Coffee Agroforestry: A Contextualized Review of Smallholder Farmers’ Adoption and Resistance
Authors: Wienhold Karl , Goulao Luis F.
Published: April 2023

Contemporary ecology and agronomy point to the many benefits of agroforestry crop systems for the provision of ecosystem services by regenerating native ecologies, and in many contexts, socio-economic benefits for coffee farmers, especially the contribution of nitrogen-fixing trees’ litter to soil nutrition and water retention. However, the implementation of agroforestry in coffee cultivation is thus far incomplete and uptake has been uneven. In this paper, we examine the adoption and non-adoption of agroforestry coffee growing techniques as a reflection of the historical, social and cultural embeddedness of smallholder coffee cultivation. It is structured as a narrative literature review contextualized with the results of surveys of smallholder coffee farmers in Colombia, Malawi and Uganda regarding their perceptions of agroforestry coffee in their respective contexts. Findings suggest that coffee farmers’ perceptions of agroforestry and the decision to implement or remove it are influenced by factors included in the notion of embeddedness, involving social relations, historical memory and formal and informal institutions, as well as practical capabilities. Intention and action are sometimes discordant due to the complex interactions of these institutional factors, and they often conflict with outside interveners’ expectations based on epistemological differences. The study illuminates some of the main sources, manifestations and dimensions of the social embeddedness of agricultural practices which mediate the perception of current practices, the sacrifice implied by potential changes, the credibility of theories linking action with outcome and the desirability of expected outcomes.
Technology Challenges and Aids: The Sustainable Development of Professional Interpreters in Listening Comprehension Effectiveness and Interpreting Performance
Authors: Huang Yuwei , Shi Weinan , Wen Jinglin
Published: April 2023

Technology plays a double-edged role in the interpreting job market. The development of technology may threaten the jobs of interpreters, as well as provide aids for them. The sustainable development of interpreters depends on determining the balance between the challenges and aids of modern technology so as to take advantage of it to enhance the performance of interpreting to fend off the challenge of technology itself. This paper launches these empirical studies by taking a tech product (a speech recognition device) as the study object, considering the lack of empirical studies about technology-assisted interpreting, as well as to explore the way and the extent to which the technology can facilitate interpreting performance in such a competitive world. Listening comprehension is generally regarded as the most difficult element in interpreting because understanding what we hear requires a colossal amount of cognitive effort, which will inevitably jeopardize the overall performance of interpreting. Therefore, how and to what extent the application of technology is capable of improving this performance is worth our attention. To measure the interpreting performance via the domain of language accuracy, message fidelity, etc., the empirical research conducted in the paper includes 30 student interpreters and 20 professional interpreters to explore the extent to which speech recognition assistance can increase the effectiveness of interpreting, as well as to shed some light on the professional sustainability and future training of interpreters.
Land Use Misclassification Results in Water Use, Economic Value, and GHG Emission Discrepancies in California’s High-Intensity Agriculture Region
Authors: Espinoza Vicky , Booth Lorenzo Ade , Viers Joshua H.
Published: April 2023

California’s San Joaquin Valley is both drought-prone and water-scarce but relies on high-intensity agriculture as its primary economy. Climate change adaptation strategies for high-intensity agriculture require reliable and highly resolved land use classification data to accurately account for changes in crop water demand, greenhouse gas (GHG) emissions, and farmgate revenue. Understanding direct and indirect economic impacts from potential changes to high-intensity agriculture to reduce groundwater overdrafts, such as reductions in the cultivated area or switching to less water-intensive crops, is unachievable if land use data are too coarse and inconsistent or misclassified. This study quantified the revenue, crop water requirement, and GHG emission discrepancies resulting from land use misclassification in the United States’ most complex agricultural region, California’s San Joaquin Valley. By comparing three commonly used land use classification datasets—CropScape, Land IQ, and Kern County Agriculture—this study found that CropScape led to considerable revenue and crop water requirement discrepancies compared to other sources. Crop misclassification across all datasets resulted in an underestimation of GHG emissions. The total revenue discrepancies of misclassified crops by area for the 2016 dataset comparisons result in underestimations by CropScape of around USD 3 billion and overestimation by LIQ and Kern Ag of USD 72 million. Reducing crop misclassification discrepancies is essential for crafting climate resilience strategies, especially for California, which generates USD 50 billion in annual agricultural revenue, faces increasing water scarcity, and aims to reach carbon neutrality by 2045. Additional investments are needed to produce spatial land use data that are highly resolved and locally validated, especially in high-intensity agriculture regions dominated by specialty crops with unique characteristics not well suited to national mapping efforts.
Manganese Adsorption onto Permanganate-Modified Bamboo Biochars from Groundwater
Authors: Wilamas Arachaporn , Vinitnantharat Soydoa , Pinisakul Anawat
Published: April 2023

Potassium permanganate-modified bamboo biochar (MBB) was used to adsorb manganese from simulated groundwater and its performance was compared to that of unmodified bamboo biochar (BB), activated carbon, and manganese greensand. The adsorption kinetics, adsorption isotherms, and manganese fractions were investigated. The Langmuir model was the best fit for manganese adsorption by MBB and BB at the maximum adsorption capacities of 21.277 and 0.803 mg g−1, respectively. The heat of adsorption from the Temkin model indicated that manganese adsorption occurs via an ion exchange process for MBB and a physical adsorption process for BB. The sequential extraction results revealed that manganese was strongly bound to the iron/manganese oxide fraction, in accordance with the chemical adsorption established in pseudo-second order kinetic data records.
Predicting Workforce Engagement towards Digital Transformation through a Multi-Analytical Approach
Authors: Hizam Sheikh Muhamad , Akter Habiba , Sentosa Ilham , Ahmed Waqas , Masrek Mohamad Noorman , Ali Jawad
Published: April 2023

The shift towards sustainable and digital practices in organizations is transforming employees’ mindsets and work performance. The digital transformation in academia is leading to meaningful changes in the behaviors and responsibilities of non-academic employees within organizations toward sustainable and responsible practices. By expounding insights into these views through social exchange theory (SET), this study aims to examine the key predictors of employee engagement (EE); namely, knowledge sharing (KS), employee mobility (EM), training and development (TD), and psychological empowerment (PE) in a digital workplace scenario. A quantitative survey based on convenience sampling was conducted to validate the research framework through partial least squares structural equation modelling (PLS-SEM). Accordingly, 205 responses were collected from the non-academic staff of universities in Klang Valley, Malaysia. Data analysis results showed that all hypotheses were significantly accepted. The impact of the model variables on employee engagement in digital transformation was found to be 75%, with employee mobility and knowledge sharing being the most prominent factors. Multigroup analysis (MGA) and importance-performance map analysis (IPMA) were additional analytical tools applied to reinforce the survey findings further and provide more comprehensive insights into employee engagement across different departments within the organization. The findings also showed the robustness of social exchange theory in digital business practices. This research offers novel and innovative perspectives on the impact of various factors (KS, EM, TD, PE) on employee engagement during digital transformation and how they mold employee behavior toward driving productive and responsible outcomes.
Sustainable Biodiesel Production from a New Oleaginous Fungus, Aspergillus carneus Strain OQ275240: Biomass and Lipid Production Optimization Using Box–Behnken Design
Authors: Ibrahim Amany G. , Baazeem Alaa , Al-Zaban Mayasar I. , Fawzy Mustafa A. , Hassan Sedky H. A. , Koutb Mostafa
Published: April 2023

Due to their low cost and ability to synthesize lipids for sustainable biodiesel production, oleaginous fungus has recently gained more prominence than other microorganisms. The new oleaginous fungus Aspergillus carneus OQ275240’s dry biomass, lipid content, and lipid yield were all optimized in this work, using the response surface methodology-based Box–Behnken design. Analysis of variance (ANOVA) was also used to examine the experimental data, and multiple regression analysis was used to fit the data to a second-order polynomial equation. Three independent variables, such as the concentration of yeast, glucose, and phosphorus, were examined for their mutual impacts. Maximum dry biomass (0.024 g/50 mL), lipid content (36.20%), and lipid yield (8.70 mg/50 mL) were achieved at optimal concentrations of 2.68 g/L of yeast, 20.82 g/L of glucose, and 0.10 g/L of phosphorus, respectively, showing that the actual data and predictions of the models were in good agreement. A. carneus OQ275240 has a favorable fatty acid profile that can be used to successfully create biodiesel, as shown by the presence of palmitic acid (C16:0), stearic acid (C18:0), and oleic acid (C18:1) in its fatty acid methyl esters (FAMEs) profile. Furthermore, the qualities of the biodiesel were investigated, and it was found that they fell within the parameters established by the international specifications EN 14214 (Europe) and ASTM D6751-08 (United States). These findings point to the newly evaluated filamentous fungal strain as a potential feedstock for the production of high-quality biodiesel.
Operationalizing Digitainability: Encouraging Mindfulness to Harness the Power of Digitalization for Sustainable Development
Authors: Gupta Shivam , Campos Zeballos Jazmin , del Río Castro Gema , Tomičić Ana , Andrés Morales Sergio , Mahfouz Maya , Osemwegie Isimemen , Phemia Comlan Sessi Vicky , Schmitz Marina , Mahmoud Nady , Inyaregh Mnena
Published: April 2023

Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues to enable and encourage practices with numerous unsustainable impacts affecting our environment, ingraining inequality, and degrading quality of life. There is an urgent need to identify such multifaceted impacts holistically. Impact assessment of digital interventions (DIs) leading to digitalization is essential specifically for Sustainable Development Goals (SDGs). Action is required to understand the pursuit of short-term gains toward achieving long-term value-driven sustainable development. We need to understand the impact of DIs on various actors and in diverse contexts. A holistic understanding of the impact will help us align the visions of sustainable development and identify potential measures to mitigate negative short and long-term impacts. The recently developed digitainability assessment framework (DAF) unveils the impact of DIs with an in-depth context-aware assessment and offers an evidence-based impact profile of SDGs at the indicator level. This paper demonstrates how DAF can be instrumental in guiding participatory action for the implementation of digitainability practices. This paper summarizes the insights developed during the Digitainable Spring School 2022 (DSS) on “Sustainability with Digitalization and Artificial Intelligence,” one of whose goals was to operationalize the DAF as a tool in the participatory action process with collaboration and active involvement of diverse professionals in the field of digitalization and sustainability. The DAF guides a holistic context-aware process formulation for a given DI. An evidence-based evaluation within the DAF protocol benchmarks a specific DI’s impact against the SDG indicators framework. The participating experts worked together to identify a DI and gather and analyze evidence by operationalizing the DAF. The four DIs identified in the process are as follows: smart home technology (SHT) for energy efficiency, the blockchain for food security, artificial intelligence (AI) for land use and cover change (LUCC), and Big Data for international law. Each of the four expert groups addresses different DIs for digitainability assessment using different techniques to gather and analyze data related to the criteria and indicators. The knowledge presented here could increase understanding of the challenges and opportunities related to digitainability and provide a structure for developing and implementing robust digitainability practices with data-driven insights.