bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation on the Properties and on the Photo-Oxidation Behaviour of Polypropylene/Fumed Silica Nanocomposites
Authors: Titone Vincenzo , Mistretta Maria Chiara , Botta Luigi , Mantia Francesco Paolo La
Published: August 2021

This work investigates the effects of very small amounts of fumed silica on the morphology and on the rheological and mechanical behaviour of polypropylene nanocomposites and on their photo-oxidation behaviour. Polypropylene nanocomposites were prepared using a twin-screw corotating extruder with 0, 1 and 2 wt/wt% of SiO2. Morphological, mechanical, thermomechanical and rheological properties were examined. It was found that the viscosity of the matrix is reduced by the presence of the silica nanoparticles, suggesting a poor adhesion between the two phases and probably some lubricating effect. On the contrary, the mechanical and, in particular, the thermomechanical properties of the matrix are considerably improved by the presence of the silica. In particular, elastic modulus and tensile strength increases remarkably, and this effect becomes more and more remarkable with an increasing temperature. As for the photo-oxidation behaviour, the presence of silica improves the photostability of the polypropylene matrix. This effect has been attributed to both the barrier to the oxygen and to the absorbance of the UV radiation from the silica nanoparticles. Finally, no significant effect of the silica nanoparticles has been put in evidence on the crystallisation behaviour of the polypropylene. As for the effect of the silica content, the difference in the properties of the two nanocomposites is relatively small and all the measured properties depend much less than linearly with its amount. This has been correlated with the reaggregation of the nanoparticles that, having a larger size, decrease the contact area between the matrix and the filler.
Preparation and Characterization of Salt-Mediated Injectable Thermosensitive Chitosan/Pectin Hydrogels for Cell Embedding and Culturing
Authors: Morello Giulia , Polini Alessandro , Scalera Francesca , Rizzo Riccardo , Gigli Giuseppe , Gervaso Francesca
Published: August 2021

In recent years, growing attention has been directed to the development of 3D in vitro tissue models for the study of the physiopathological mechanisms behind organ functioning and diseases. Hydrogels, acting as 3D supporting architectures, allow cells to organize spatially more closely to what they physiologically experience in vivo. In this scenario, natural polymer hybrid hydrogels display marked biocompatibility and versatility, representing valid biomaterials for 3D in vitro studies. Here, thermosensitive injectable hydrogels constituted by chitosan and pectin were designed. We exploited the feature of chitosan to thermally undergo sol–gel transition upon the addition of salts, forming a compound that incorporates pectin into a semi-interpenetrating polymer network (semi-IPN). Three salt solutions were tested, namely, beta-glycerophosphate (βGP), phosphate buffer (PB) and sodium hydrogen carbonate (SHC). The hydrogel formulations (i) were injectable at room temperature, (ii) gelled at 37 °C and (iii) presented a physiological pH, suitable for cell encapsulation. Hydrogels were stable in culture conditions, were able to retain a high water amount and displayed an open and highly interconnected porosity and suitable mechanical properties, with Young’s modulus values in the range of soft biological tissues. The developed chitosan/pectin system can be successfully used as a 3D in vitro platform for studying tissue physiopathology.
Electrochemical Analysis of Free Glycerol in Biodiesel Using Reduced Graphene Oxide and Gold/Palladium Core-Shell Nanoparticles Modified Glassy Carbon Electrode
Authors: Paiva Victor Magno , Assis Kelly Leite dos Santos Castro , Archanjo Braulio Soares , Ferreira Daniela Ramos , Senna Carlos Alberto , Ribeiro Emerson Schwingel , Achete Carlos Alberto , D'Elia Eliane
Published: August 2021

Glycerol is a major byproduct obtained in the production of biodiesel, an important renewable fuel. The presence of free glycerol in fuel can have structural and performance consequences with respect to the engine, making fuel quality control important. The standard method to analyze glycerol in biodiesel is gas chromatography, a time-consuming and expensive technique. In this context, an electrode based on glassy carbon electrodes (GCEs) modified with reduced graphene oxide and core-shell gold@palladium nanoparticles was developed for the determination of glycerol in biodiesel. The free glycerol analysis was performed in the aqueous phase obtained by liquid–liquid extraction from a biodiesel sample. Cyclic voltammetry was chosen as the method for glycerol electrochemical analysis to regenerate active sites and promote greater sensor stability. The modified Au@Pd/rGO/GCE electrode showed an excellent performance, obtaining a linear range of 18.2 to 109 µmol L−1 with a correlation coefficient of 0.9895, limits of detection and quantification of 5.33 and 17.6 µmol L−1, respectively, high stability during 1000 cycles, and recovery values of 86% and 87% in the quantification of glycerol in biodiesel samples. The proposed method proved to be a great alternative for the analysis of glycerol in biodiesel, being a fast, sensitive, and low-cost technique due to its high stability and the use of small quantities of reagents.
A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems
Authors: Meilanitasari Prita , Shin Seung-Jun
Published: August 2021

This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summarized as follows: less consideration of uncertainty factors causes a gap between the reality and the derived schedules; the use of stationary dispatching rules is limited to reflect the dynamics and flexibility; production-level scheduling is restricted to increase responsiveness owing to product-level uncertainty; and optimization is more focused, while prediction is used mostly for verification and validation, although prediction-then-optimization is the standard stream in data analytics. In future research, the degree of uncertainty should be quantified and modeled explicitly; both holistic and granular algorithms should be considered; product sequences should be incorporated; and sequence learning should be applied to implement the prediction-then-optimization stream. This would enable us to derive data-learned prediction and optimization models that output accurate and precise schedules; foresee individual product locations; and respond rapidly to dynamic and frequent changes in JS-FMSs.
Divine vs. Human Law: The Quarrel between the Anti- and Pro-Constitutionalist Jurists in Iranian Constitutional Revolution of 1906
Authors: Mirabian Tabar Mehdi
Published: August 2021

This study investigated the quarrel between the pro-and anti-constitutionalist jurists following the establishment of the first National Consultative Assembly (Majlis) in Iran and the drafting of the first constitution in 1906. A group of shi῾ite jurists launched an attack on Majlis, in addition to the ideas of human legislation, freedom, and equality, by considering the Islamic Sharī῾a law to be a set of perfect and impeccable laws. In response to these oppositions, the pro-constitutional jurists argued in favor of the constitutional movement. In this paper, it is argued that the quarrel could be considered as evidence for the perennial tension between the divine and human law in Islam. It appears that examining this conflict may shed light on incidents shaping the history of contemporary Iran.
Abram the One from Beyond-the-River, and King Chedorlaomer of Elam (Genesis 14): Persia and the Formation of Judaean Ethnic Identity in a Late Patriarchal Narrative
Authors: Granerød Gard
Published: August 2021

The perception of Persia in Judaean/Jewish texts from antiquity contributed to the construction of a Judaean/Jewish identity. Genesis 14 gives an example of this; in it, Abra(ha)m wages war with a coalition headed by King Chedorlaomer of Elam. The article argues that Genesis 14 is one of the latest additions to the patriarchal narratives (Genesis 12–36), composed in the Persian or early Hellenistic period. It was conceived and used as an ethnic identity-forming story. The characters in the narrative represented groups and nations in the neighbourhood of the province of Judah. Abra(ha)m was perceived as the ancestor of the Judaeans and the inhabitants of the province Beyond-the-River. The King of Elam represented the Persian Empire. The article uses redaction criticism to argue that Genesis 14 is among the latest additions to the patriarchal narrative in the late Persian or Hellenistic period. Moreover, it uses a combination of philological and historical methods to argue that the description of Abra(ha)m as hāʿibrî (traditionally translated “the Hebrew,” Gen 14: 13) characterises him as a person from the region Eber-nāri (Beyond-the-River). The article uses similar methods to argue that the names of people and places in Genesis 14 referred to political entities in and around Judah. Eventually, the article uses Anthony D. Smith’s theory of ethnic community and elements from postcolonial theory as “reading lenses” and a framework for analysing Genesis 14. Reading this way underscores that Genesis 14 originated and worked as an ethnic identity-forming story.
Further Reflections on Zhi Qian’s Foshuo Pusa Benye Jing: Some Terminological Questions
Authors: Han Jaehee , Hwang Soonil , Lee Hyebin , Braarvig Jens
Published: August 2021

Zhi Qian (支謙, fl. ca. 220–257 CE), a prolific Yuezhi-Chinese translator of Indian Buddhist scriptures into Chinese, is widely known for his broad range of styles and terminology. For several decades, his translation activities and his legacy in the history of Chinese Buddhist literature have been a rich field of research, particularly within the context of the transmission of Buddhism from India to China. In the present article, as a follow-up study to “Buddhism for Chinese readers: Zhi Qian’s Literary Refinements in the Foshuo pusa benye jing,” recently published by the authors in this journal, we offer additional reflections on distinctive features of Zhi Qian’s language. We focus on four unusual and interesting renderings in the Foshuo pusa benye jing (佛說菩薩本業經, T. 281), namely (1) santu 三塗; (2) shezui 捨罪; (3) kong 空, wu xiang 無想 and bu yuan 不願; and (4) sishi buhu 四時不護. Through an analysis of these words and phrases, we discuss Zhi Qian’s translation techniques and lexical idiosyncrasies, highlighting their significance in our understanding of the dynamics of language contact and change in the early period of the Chinese Buddhist tradition. Thus, the paper investigates some key Buddhist terms as coined by the early translators on the basis of the classical Chinese and illustrates the semantic changes of the Chinese language taking place in the period as well as influence of Buddhist regimes of knowledge.
Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning
Authors: Parekh Jash R. , Poortinga Ate , Bhandari Biplov , Mayer Timothy , Saah David , Chishtie Farrukh
Published: August 2021

The large scale quantification of impervious surfaces provides valuable information for urban planning and socioeconomic development. Remote sensing and GIS techniques provide spatial and temporal information of land surfaces and are widely used for modeling impervious surfaces. Traditionally, these surfaces are predicted by computing statistical indices derived from different bands available in remotely sensed data, such as the Landsat and Sentinel series. More recently, researchers have explored classification and regression techniques to model impervious surfaces. However, these modeling efforts are limited due to lack of labeled data for training and evaluation. This in turn requires significant effort for manual labeling of data and visual interpretation of results. In this paper, we train deep learning neural networks using TensorFlow to predict impervious surfaces from Landsat 8 images. We used OpenStreetMap (OSM), a crowd-sourced map of the world with manually interpreted impervious surfaces such as roads and buildings, to programmatically generate large amounts of training and evaluation data, thus overcoming the need for manual labeling. We conducted extensive experimentation to compare the performance of different deep learning neural network architectures, optimization methods, and the set of features used to train the networks. The four model configurations labeled U-Net_SGD_Bands, U-Net_Adam_Bands, U-Net_Adam_Bands+SI, and VGG-19_Adam_Bands+SI resulted in a root mean squared error (RMSE) of 0.1582, 0.1358, 0.1375, and 0.1582 and an accuracy of 90.87%, 92.28%, 92.46%, and 90.11%, respectively, on the test set. The U-Net_Adam_Bands+SI Model, similar to the others mentioned above, is a deep learning neural network that combines Landsat 8 bands with statistical indices. This model performs the best among all four on statistical accuracy and produces qualitatively sharper and brighter predictions of impervious surfaces as compared to the other models.
Unpaired Remote Sensing Image Super-Resolution with Multi-Stage Aggregation Networks
Authors: Zhang Lize , Lu Wen , Huang Yuanfei , Sun Xiaopeng , Zhang Hongyi
Published: August 2021

Mainstream image super-resolution (SR) methods are generally based on paired training samples. As the high-resolution (HR) remote sensing images are difficult to collect with a limited imaging device, most of the existing remote sensing super-resolution methods try to down-sample the collected original images to generate an auxiliary low-resolution (LR) image and form a paired pseudo HR-LR dataset for training. However, the distribution of the generated LR images is generally inconsistent with the real images due to the limitation of remote sensing imaging devices. In this paper, we propose a perceptually unpaired super-resolution method by constructing a multi-stage aggregation network (MSAN). The optimization of the network depends on consistency losses. In particular, the first phase is to preserve the contents of the super-resolved results, by constraining the content consistency between the down-scaled SR results and the low-quality low-resolution inputs. The second stage minimizes perceptual feature loss between the current result and LR input to constrain perceptual-content consistency. The final phase employs the generative adversarial network (GAN) to adding photo-realistic textures by constraining perceptual-distribution consistency. Numerous experiments on synthetic remote sensing datasets and real remote sensing images show that our method obtains more plausible results than other SR methods quantitatively and qualitatively. The PSNR of our network is 0.06dB higher than the SOTA method—HAN on the UC Merced test set with complex degradation.
Domain Adaptive Ship Detection in Optical Remote Sensing Images
Authors: Li Linhao , Zhou Zhiqiang , Wang Bo , Miao Lingjuan , An Zhe , Xiao Xiaowu
Published: August 2021

With the successful application of the convolutional neural network (CNN), significant progress has been made by CNN-based ship detection methods. However, they often face considerable difficulties when applied to a new domain where the imaging condition changes significantly. Although training with the two domains together can solve this problem to some extent, the large domain shift will lead to sub-optimal feature representations, and thus weaken the generalization ability on both domains. In this paper, a domain adaptive ship detection method is proposed to better detect ships between different domains. Specifically, the proposed method minimizes the domain discrepancies via both image-level adaption and instance-level adaption. In image-level adaption, we use multiple receptive field integration and channel domain attention to enhance the feature’s resistance to scale and environmental changes, respectively. Moreover, a novel boundary regression module is proposed in instance-level adaption to correct the localization deviation of the ship proposals caused by the domain shift. Compared with conventional regression approaches, the proposed boundary regression module is able to make more accurate predictions via the effective extreme point features. The two adaption components are implemented by learning the corresponding domain classifiers respectively in an adversarial training way, thereby obtaining a robust model suitable for both of the two domains. Experiments on both supervised and unsupervised domain adaption scenarios are conducted to verify the effectiveness of the proposed method.
Unsupervised Change Detection Using Fuzzy Topology-Based Majority Voting
Authors: Shao Pan , Shi Wenzhong , Liu Zhewei , Dong Ting
Published: August 2021

Remote sensing change detection (CD) plays an important role in Earth observation. In this paper, we propose a novel fusion approach for unsupervised CD of multispectral remote sensing images, by introducing majority voting (MV) into fuzzy topological space (FTMV). The proposed FTMV approach consists of three principal stages: (1) the CD results of different difference images produced by the fuzzy C-means algorithm are combined using a modified MV, and an initial fusion CD map is obtained; (2) by using fuzzy topology theory, the initial fusion CD map is automatically partitioned into two parts: a weakly conflicting part and strongly conflicting part; (3) the weakly conflicting pixels that possess little or no conflict are assigned to the current class, while the pixel patterns with strong conflicts often misclassified are relabeled using the supported connectivity of fuzzy topology. FTMV can integrate the merits of different CD results and largely solve the conflicting problem during fusion. Experimental results on three real remote sensing images confirm the effectiveness and efficiency of the proposed method.
Surface Urban Heat Islands Dynamics in Response to LULC and Vegetation across South Asia (2000–2019)
Authors: Hassan Talha , Zhang Jiahua , Prodhan Foyez Ahmed , Pangali Sharma Til Prasad , Bashir Barjeece
Published: August 2021

Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (°C). The SUHII linear trend is increasing in Delhi, Karachi, Kathmandu, and Thimphu, while decreasing in Colombo, Dhaka, and Kabul from 2000–2019. UTFVI has shown the poor ecological conditions in all urban buffers due to high LST and urban infrastructures. In addition, a strong negative correlation between LST and NDVI can be seen in a range of −0.1 to −0.6.
Carbon Stocks, Species Diversity and Their Spatial Relationships in the Yucatán Peninsula, Mexico
Authors: Hernández-Stefanoni José Luis , Castillo-Santiago Miguel Ángel , Andres-Mauricio Juan , Portillo-Quintero Carlos A. , Tun-Dzul Fernando , Dupuy Juan Manuel
Published: August 2021

Integrating information about the spatial distribution of carbon stocks and species diversity in tropical forests over large areas is fundamental for climate change mitigation and biodiversity conservation. In this study, spatial models showing the distribution of carbon stocks and the number of species were produced in order to identify areas that maximize carbon storage and biodiversity in the tropical forests of the Yucatan Peninsula, Mexico. We mapped carbon density and species richness of trees using L-band radar backscatter data as well as radar texture metrics, climatic and field data with the random forest regression algorithm. We reduced sources of errors in plot data of the national forest inventory by using correction factors to account for carbon stocks of small trees (<7.5 cm DBH) and for the temporal difference between field data collection and imagery acquisition. We created bivariate maps to assess the spatial relationship between carbon stocks and diversity. Model validation of the regional maps obtained herein using an independent data set of plots resulted in a coefficient of determination (R2) of 0.28 and 0.31 and a relative mean square error of 38.5% and 33.0% for aboveground biomass and species richness, respectively, at pixel level. Estimates of carbon density were influenced mostly by radar backscatter and climatic data, while those of species richness were influenced mostly by radar texture and climatic variables. Correlation between carbon density and species richness was positive in 79.3% of the peninsula, while bivariate maps showed that 39.6% of the area in the peninsula had high carbon stocks and species richness. Our results highlight the importance of combining carbon and diversity maps to identify areas that are critical—both for maintaining carbon stocks and for conserving biodiversity.
Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach
Authors: Amazirh Abdelhakim , Bouras El Houssaine , Olivera-Guerra Luis Enrique , Er-Raki Salah , Chehbouni Abdelghani
Published: August 2021

Monitoring agricultural crops is of paramount importance for preserving water resources and increasing water efficiency over semi-arid areas. This can be achieved by modelling the water resources all along the growing season through the coupled water–surface energy balance. Surface albedo is a key land surface variable to constrain the surface radiation budget and hence the coupled water–surface energy balance. In order to capture the hydric status changes over the growing season, optical remote sensing becomes impractical due to cloud cover in some periods, especially over irrigated winter crops in semi-arid regions. To fill the gap, this paper aims to generate cloudless surface albedo product from Sentinel-1 data that offers a source of high spatio-temporal resolution images. This can help to better capture the vegetation development along the growth season through the surface radiation budget. Random Forest (RF) algorithm was implemented using Sentinel-1 backscatters as input. The approach was tested over an irrigated semi-arid zone in Morocco, which is known by its heterogeneity in term of soil conditions and crop types. The obtained results are evaluated against Landsat-derived albedo with quasi-concurrent Landsat/Sentinel-1 overpasses (up to one day offset), while a further validation was investigated using in situ field scale albedo data. The best model-hyperparameters selection was dependent on two validation approaches (K-fold cross-validation ‘k = 10’, and holdout). The more robust and accurate model parameters are those that represent the best statistical metrics (root mean square error ‘RMSE’, bias and correlation coefficient ‘R’). Coefficient values ranging from 0.70 to 0.79 and a RMSE value between 0.0002 and 0.00048 were obtained comparing Landsat and predicted albedo by RF method. The relative error ratio equals 4.5, which is acceptable to predict surface albedo.
A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost
Authors: Ismail Aya , Elpeltagy Marwa , S. Zaki Mervat , Eldahshan Kamal
Published: August 2021

Currently, face-swapping deepfake techniques are widely spread, generating a significant number of highly realistic fake videos that threaten the privacy of people and countries. Due to their devastating impacts on the world, distinguishing between real and deepfake videos has become a fundamental issue. This paper presents a new deepfake detection method: you only look once–convolutional neural network–extreme gradient boosting (YOLO-CNN-XGBoost). The YOLO face detector is employed to extract the face area from video frames, while the InceptionResNetV2 CNN is utilized to extract features from these faces. These features are fed into the XGBoost that works as a recognizer on the top level of the CNN network. The proposed method achieves 90.62% of an area under the receiver operating characteristic curve (AUC), 90.73% accuracy, 93.53% specificity, 85.39% sensitivity, 85.39% recall, 87.36% precision, and 86.36% F1-measure on the CelebDF-FaceForencics++ (c23) merged dataset. The experimental study confirms the superiority of the presented method as compared to the state-of-the-art methods.
Effectiveness Evaluation Method of Application of Mobile Communication System Based on Factor Analysis
Authors: Jia Guohui , Zhou Jie
Published: August 2021

The application mode of army mobile communication networks is closely related to combat mission and application environment. Different combat missions and application environments result in different network structures and different service priorities, which requires a semi-automatic system to support the network scheme design. Therefore, evaluating the efficiency of network schemes generated by automatic planning is a problem that needs to be urgently addressed. In the past, researchers have proposed a variety of methods to evaluate the effectiveness of mobile communication systems, most of which are based on simulation methods and ignore the historical data of network usage. This paper studies an effectiveness evaluation method of mobile communication network design schemes and proposes a design scheme for the evaluation and optimization of network plans. Furthermore, the improved method of effectiveness evaluation based on factor analysis is discussed in detail. The method not only effectively uses historical data but also reduces the amount of data collection and calculation. In order to adapt to the preference requirements of different task scenarios, a decision preference setting method based on cluster analysis is proposed, which can render the output optimization result more reasonable and feasible.
Using Machine Learning for Remote Behaviour Classification—Verifying Acceleration Data to Infer Feeding Events in Free-Ranging Cheetahs
Authors: Giese Lisa , Melzheimer Jörg , Bockmühl Dirk , Wasiolka Bernd , Rast Wanja , Berger Anne , Wachter Bettina
Published: August 2021

Behavioural studies of elusive wildlife species are challenging but important when they are threatened and involved in human-wildlife conflicts. Accelerometers (ACCs) and supervised machine learning algorithms (MLAs) are valuable tools to remotely determine behaviours. Here we used five captive cheetahs in Namibia to test the applicability of ACC data in identifying six behaviours by using six MLAs on data we ground-truthed by direct observations. We included two ensemble learning approaches and a probability threshold to improve prediction accuracy. We used the model to then identify the behaviours in four free-ranging cheetah males. Feeding behaviours identified by the model and matched with corresponding GPS clusters were verified with previously identified kill sites in the field. The MLAs and the two ensemble learning approaches in the captive cheetahs achieved precision (recall) ranging from 80.1% to 100.0% (87.3% to 99.2%) for resting, walking and trotting/running behaviour, from 74.4% to 81.6% (54.8% and 82.4%) for feeding behaviour and from 0.0% to 97.1% (0.0% and 56.2%) for drinking and grooming behaviour. The model application to the ACC data of the free-ranging cheetahs successfully identified all nine kill sites and 17 of the 18 feeding events of the two brother groups. We demonstrated that our behavioural model reliably detects feeding events of free-ranging cheetahs. This has useful applications for the determination of cheetah kill sites and helping to mitigate human-cheetah conflicts.
Kinetic Sensors for Ligament Balance and Kinematic Evaluation in Anatomic Bi-Cruciate Stabilized Total Knee Arthroplasty
Authors: Sabatini Luigi , Bosco Francesco , Barberis Luca , Camazzola Daniele , Bistolfi Alessandro , Risitano Salvatore , Massè Alessandro , Indelli Pier Francesco
Published: August 2021

Sensor technology was introduced to intraoperatively analyse the differential pressure between the medial and lateral compartments of the knee during primary TKA using a sensor to assess if further balancing procedures are needed to achieve a “balanced” knee. The prognostic role of epidemiological and radiological parameters was also analysed. A consecutive series of 21 patients with primary knee osteoarthritis were enrolled and programmed for TKA in our unit between 1 September 2020 and 31 March 2021. The VERASENSE Knee System (OrthoSensor Inc., Dania Beach, FL, USA) has been proposed as an instrument that quantifies the differential pressure between the compartments of the knee intraoperatively throughout the full range of motion during primary TKA, designed with a J-curve anatomical femoral design and a PS “medially congruent” polyethylene insert. Thirteen patients (61.90%) showed a “balanced” knee, and eight patients (38.10%) showed an intra-operative “unbalanced” knee and required additional procedures. A total of 13 additional balancing procedures were performed. At the end of surgical knee procedures, a quantitatively balanced knee was obtained in all patients. In addition, a correlation was found between the compartment pressure of phase I and phase II at 10° of flexion and higher absolute pressures were found in the medial compartment than in the lateral compartment in each ROM degree investigated. Moreover, those pressure values showed a trend to decrease with the increase in flexion degrees in both compartments. The “Kinetic Tracking” function displays the knee’s dynamic motion through the full ROM to evaluate joint kinetics. The obtained kinetic traces reproduced the knee’s medial pivot and femoral rollback, mimicking natural knee biomechanics. Moreover, we reported a statistically significant correlation between the need for soft tissue or bone resection rebalancing and severity of the initial coronal deformity (>10°) and a preoperative JLCA value >2°. The use of quantitative sensor-guided pressure evaluation during TKA leads to a more reproducible “balanced” knee. The surgeon, evaluating radiological parameters before surgery, may anticipate difficulties in knee balance and require those devices to achieve the desired result objectively.
‘Mind Your Business and Leave My Rolls Alone’: A Case Study of Fat Black Women Runners’ Decolonial Resistance
Authors: Ashdown-Franks Garcia , Joseph Janelle
Published: August 2021

The Black female body has been vilified, surveilled, and viewed as ‘obese’ and irresponsible for centuries in Western societies. For just as long, some Black women have resisted their mischaracterizations. Instead they have embraced a ‘fat’ identity. But little research has demonstrated how Black fat women participate in sport. The purpose of this study is to show how Black fat women who run use social media to unapologetically celebrate Blackness and fatness. This research uses a case-study approach to illuminate a broader phenomenon of decolonial resistance through running. In addition to analysis of websites, blogs, and news articles devoted to Black women’s running, we discuss the (social) media content of two specific runners: Mirna Valerio and Latoya Shauntay Snell. We performed a critical discourse analysis on 14 media offerings from the two runners, including websites, Twitter pages, and blogs collected over a five-month period from September 2020–January 2021. The analysis examined how they represent themselves and their communities and how they comment on issues of anti-fat bias, neoliberal capitalism, ableist sexism, and white supremacy, some of the pillars of colonialism. Whereas running is often positioned as a weight-loss-focused and white-dominated colonial project, through their very presence and use of strategic communication to amplify their experiences and build community, these runners show how being a Black fat female athlete is an act of decolonial resistance. This study offers a unique sporting example of how fat women challenge obesity discourses and cultural invisibility and how Black athletes communicate anti-racist, decolonial principles.
Good Lives Model: Importance of Interagency Collaboration in Preventing Violent Recidivism
Authors: Mallion Jaimee Sheila
Published: August 2021

Violence is a complex and multifaceted problem requiring a holistic and individualized response. The Good Lives Model (GLM) suggests violence occurs when an individual experiences internal and external obstacles in the pursuit of universal human needs (termed primary goods). With a twin focus, GLM-consistent interventions aim to promote attainment of primary goods, whilst simultaneously reducing risk of reoffending. This is achieved by improving an individuals’ internal (i.e., skills and abilities) and external capacities (i.e., opportunities, environments, and resources). This paper proposes that collaborations between different agencies (e.g., psychological services, criminal justice systems, social services, education, community organizations, and healthcare) can support the attainment of primary goods through the provision of specialized skills and resources. Recommendations for ensuring interagency collaborations are effective are outlined, including embedding a project lead, regular interagency meetings and training, establishing information sharing procedures, and defining the role each agency plays in client care.
Tea Bag Index to Assess Carbon Decomposition Rate in Cranberry Agroecosystems
Authors: Dossou-Yovo Wilfried , Parent Serge-Étienne , Ziadi Noura , Parent Élizabeth , Parent Léon-Étienne
Published: August 2021

In cranberry production systems, stands are covered by 1–5 cm of sand every 2–5 years to stimulate plant growth, resulting in alternate layers of sand and litter in soil upper layers. However, almost intact twigs and leaves remain in subsurface layers, indicating a slow decomposition rate. The Tea Bag Index (TBI) provides an internationally standardized methodology to compare litter decomposition rates (k) and stabilization (S) among terrestrial ecosystems. However, TBI parameters may be altered by time-dependent changes in the contact between litter and their immediate environment. The aims of this study were to determine the TBI of cranberry agroecosystems and compare it to the TBI of other terrestrial ecosystems. Litters were standardized green tea, standardized rooibos tea, and cranberry residues collected on the plantation floor. Litter decomposition was monitored during two consecutive years. Added N did not affect TBI parameters (k and S) due to possible N leaching and strong acidic soil condition. Decomposition rates (k) averaged (mean ± SD) 9.7 × 10−3 day−1 ± 1.6 × 10−3 for green tea, 3.3 × 10−3 day−1 ± 0.8 × 10−5 for rooibos tea, and 0.4 × 10−3 day−1 ± 0.86 × 10−3 for cranberry residues due to large differences in biochemical composition and tissue structure. The TBI decomposition rate (k) was 0.006 day−1 ± 0.002 in the low range among terrestrial ecosystems, and the stabilization factor (S) was 0.28 ± 0.08, indicating high potential for carbon accumulation in cranberry agroecosystems. Decomposition rates of tea litters were reduced by fractal coefficients of 0.6 for green tea and 0.4 for rooibos tea, indicating protection mechanisms building up with time in the tea bags. While the computation of the TBI stabilization factor may be biased because the green tea was not fully decomposed, fractal kinetics could be used as additional index to compare agroecosystems.
Effects of Heavy Isotopes (2H1 and 18O16) Depleted Water Con-Sumption on Physical Recovery and Metabolic and Immunological Parameters of Healthy Volunteers under Regular Fitness Load
Authors: Kharaeva Zaira , Hokonova Tamara , Elmurzaeva Jannet , Dzamihova Irlana , Mayer Wolfgang , De Luca Chiara , Trakhtman Ilya , Korkina Liudmila
Published: August 2021

Water depleted of heavy isotopes, such as 2H1 and 18O16 (HIDW), has shown numerous biological/health effects in vitro, in vivo, and in epidemiological studies. Major observations were related to cell growth/differentiation, immune/nervous system responses, endurance/adaptation, mitochondrial electron transfer, energy production, glucose metabolism, etc. No human studies to confirm physiological, metabolic, and immune responses to the consumption of HIDW have been performed. A placebo-controlled study on healthy volunteers (n = 50) under fitness load who consumed 1.5 L HIDW (58 ppm 2H and 1780 ppm 18O) or normal water for 60 days was carried out. Plasma content of 2H1 and 18O16, markers of energy, lipid, and glucose metabolism, anthropometric, cardio-vascular, oxidant/antioxidant, and immunological parameters were determined. Significant decrease in plasma heavy isotopes in the group consuming HIDW was observed in concomitance with an increase in ATP, insulin, and LDH, and diminished plasma lactate. Several anthropometric and cardio-vascular parameters were improved as compared to placebo group. Lipid markers demonstrated antiatherogenic effects, while oxidant/antioxidant parameters revealed HIDW-induced hormesis. Antibacterial/antiviral immunity was remarkably higher in HIDW versus placebo group. Conclusions: HIDW consumption by humans under fitness load could be a valid approach to improve their adaptation/recovery through several mechanisms.
Integrating Programmatic Expertise from across the US and Canada to Model and Guide Leadership Training for Graduate Students in Sustainability
Authors: Motzer Nicole , Weller Aleta Rudeen , Curran K , Donner Simon , Heustis Ronald J. , Jordan Cathy , Krebs Margaret , Olandar Lydia , Rowell Kirsten , Silka Linda , Wall Diana H. , York Abigail
Published: August 2021

It is critical that future sustainability leaders possess the skills and aptitudes needed to tackle increasingly ‘wicked’ challenges. While much has been done to identify this need, inadequate Leadership Training for graduate students in Sustainability (LTS) continues to plague even the most highly-resourced institutions. Collectively, the authors of this paper represent the small yet growing number of LTS programs across the United States and Canada working to close this training gap. In this paper, we describe the integrative approach we took to synthesize our collective knowledge of LTS with our diverse programmatic experiences and, ultimately, translate that work into concrete guidance for LTS implementation and design. We present a framework for the suite of key LTS aptitudes and skills yielded by our collaborative approach, and ground these recommendations in clear, real-world examples. We apply our framework to the creation of an open-access curricular database rich with training details, and link this database to an interactive network map focused on sharing programmatic designs. Together, our process and products transform many disparate components into a more comprehensive and accessible understanding of what we as LTS professionals do, with a view to helping others who are looking to do the same for the next generation of sustainability leaders.
Vegetation Changes in Response to Climatic Factors and Human Activities in Jilin Province, China, 2000–2019
Authors: Li Ying , Zhao Zhibo , Wang Lingzhi , Li Guanghui , Chang Lei , Li Yuefen
Published: August 2021

Dynamic change in vegetation is an integral component of terrestrial ecosystems, which has become a significant research area in the current context of global climate warming. Jilin Province in northeast China is an ecologically fragile area, and there is an urgent need to understand its vegetation changes and responses to both climatic factors and human activities. The normalized difference vegetation index (NDVI) was used to analyze trends in vegetation growth, and indicated significant growth overall. The NDVI of different vegetation cover types is increasing, indicating that the vegetation is continuously greening, and in descending order, the growth trends were grassland (0.0035/year) > permanent wetland (0.0028/year) > cropland (0.0027/year) > forest land (0.0022/year) > barren land (−0.0001/year). Grassland and cropland vegetation types included the most severely degraded areas, with fluctuating NDVI values. Precipitation was the main positive controlling climatic factor of NDVI in the western regions of the study area, while average temperature was the main factor in the eastern regions. Precipitation was the main climatic control factor for grassland and cropland, while forest land was limited by precipitation and average temperature. Barren land and permanent wetland were slightly negatively correlated with precipitation. From 2000 to 2019, the residual values for NDVI increased from −0.0121 to 0.0116, and the impact of human activities on vegetation changed from negative to positive. By 2019, the proportion of positively affected zones was as high as 94.01%, and the negatively affected zones were mainly distributed across transitional areas of cropland and grassland, and urban and built-up land and forest land.
Riding the Digital Product Life Cycle Waves towards a Circular Economy
Authors: Subramoniam Ramesh , Sundin Erik , Subramoniam Suresh , Huisingh Donald
Published: August 2021

Data driven organizations such as Amazon and Uber have raised the capabilities and expectations of customers to a new level by providing faster and cheaper products and services. The reviewed literature documented that 10–15% of the online products are returned and in many cases such products are not shelf-ready due to product obsolescence or slight wear and tear, thereby reducing profits. Many of these products are disposed of in landfills. There were very few publications that documented how integration of digitized product life cycle into the business model improves product returns and the remanufacturing processes. As societies continue on, environmentally responsible, digital journeys with connected devices and people, reverse supply chains and remanufacturing will play increased importance in fulfilling customers expanded expectations. The networks are evolving, wherein, data are collected from all phases of the product lifecycles from design, prototype, manufacturing, usage aftermarket, returns remanufacturing and recycling. The objective of this paper’s authors was to describe how all phases of product life cycles can be digitized to improve global reverse supply chains and remanufacturing. The authors performed a literature review and developed case studies to document current and to predict future transformational waves that will become increasingly used in many industrial sectors. The authors made recommendations about the importance of improved product design, reduced processing costs and increased use of remanufactured products based upon data on returns to manufacturers and service providers. This paper contributes to research by providing a framework of a digitized product life cycle integrated with the business process phases including remanufacturing and supported with real-world case studies for practitioners and academicians. The authors outlined potential future topics for academic researchers and practitioners, for expanding usage of digital tools in real-time predictive analytics to improve remanufacturing system’s efficiency and quality.
Multidimensional Assessment of Job Satisfaction in Telework Conditions. Case Study: Romania in the COVID-19 Pandemic
Authors: Petcu Monica Aureliana , Sobolevschi-David Maria Iulia , Anica-Popa Adrian , Curea Stefania Cristina , Motofei Catalina , Popescu Ana-Maria
Published: August 2021

Improving employee performance is one of the main levers for companies to achieve their goals. The detachment of work from place is a growing trend, affecting the performance levels of both employees and employers. The purpose of this research was to assess job satisfaction in telework conditions, based on areas considered decisive and evaluated by specific items. To this end, an opinion questionnaire was created, the information was processed with correlation analysis and regression analysis. This study showed that job satisfaction in telework is associated with higher skills, autonomy, and a favorable organizational climate, but also with a higher level of emotional exhaustion generated by constant access to work, as well as the lack of relationships and mentoring. We found a statistically significant positive link between competencies, autonomy, organizational climate, and job satisfaction. Significant differences were observed in the perception of job satisfaction with respect to the gender dimension, as well as between employees of various age categories.
The Individual Dimension of Digital Innovation: The Altered Roles of Innovation Agents and Market Actors
Authors: Allataifeh Haneen , Moghavvemi Sedigheh
Published: August 2021

Digital innovation entails the employment of new technologies to address business issues and to create practices that lead to the achievement of sustainability. It is observed that digital technology alters the individual dimension of the innovation process, allowing for a set of heterogenous actors to become active engagers in the process. A review of the previous research revealed a lack of focus on the roles these different actors play in the digital innovation process, as well as the mechanisms by which digital technology facilitates actor engagement, calling for research to shed some light on this topic. This phenomenological study undertakes an exploratory investigation of twenty-one Malaysian small- and medium-sized enterprises (SMEs) in the information and communication technology (ICT) sector, with the aim to demonstrate the importance of engaging market actors in each stage of the value co-creation process. Interviews with industry players show the shifted role of market actors in the innovation process—from product receivers to gatekeepers—at different stages of the innovation process. Market actors are extensively engaged in validating and evaluating the progress of ongoing digital innovation projects and, therefore, can modify their direction. Meanwhile, the role of innovation agents changes from an authoritative to reflective one. This study provides evidence that market actors are in a controlling position at certain points of the innovation process. As such, the view of the innovation process as being company-centric is challenged by the findings of this research. We provide new information regarding innovation practices, the roles of key actors, and their value in the digital context, which can serve as valuable knowledge for both academics and practitioners.