bluuebunny/crossref_metadata_2025 huggingface dataset:
Methodological Aspects of Indirect Calorimetry in Patients with Sepsis—Possibilities and Limitations
Authors: Wasyluk Weronika , Zwolak Agnieszka , Jonckheer Joop , De Waele Elisabeth , Dąbrowski Wojciech
Published: February 2022

The aim of the review was to analyse the challenges of using indirect calorimetry in patients with sepsis, including the limitations of this method. A systematic review of the literature was carried out. The analysis concerned the methodology and presentation of research results. In most studies assessing energy expenditure, energy expenditure was expressed in kcal per day (n = 9) and as the mean and standard deviation (n = 7). Most authors provided a detailed measurement protocol, including measurement duration (n = 10) and device calibration information (n = 7). Ten papers provided information on the day of hospitalisation when the measurements were obtained, nine on patient nutrition, and twelve on the criteria for inclusion and exclusion of participants from the study. Small study group sizes and study at a single centre were among the most cited limitations. Studies assessing energy expenditure in patients with sepsis by indirect calorimetry differ in the methodology and presentation of results, and their collective analysis is difficult. A meta-analysis of the results could enable multi-site and large patient evaluation. Standardisation of protocols and presentation of all collected data would enable their meta-analysis, which would help to achieve greater knowledge about metabolism in sepsis.
Effects of Whole Brown Bean and Its Isolated Fiber Fraction on Plasma Lipid Profile, Atherosclerosis, Gut Microbiota, and Microbiota-Dependent Metabolites in Apoe−/− Mice
Authors: Liu Jiyun , Hefni Mohammed E. , Witthöft Cornelia M. , Bergström Maria , Burleigh Stephen , Nyman Margareta , Hållenius Frida
Published: February 2022

The health benefits of bean consumption are widely recognized and are largely attributed to the dietary fiber content. This study investigated and compared the effects of whole brown beans and an isolated bean dietary fiber fraction on the plasma lipid profile, atherosclerotic plaque amount, gut microbiota, and microbiota-dependent metabolites (cecal short-chain fatty acids (SCFAs) and plasma methylamines) in Apoe−/− mice fed high fat diets for 10.5 weeks. The results showed that both whole bean and the isolated fiber fraction had a tendency to lower atherosclerotic plaque amount, but not plasma lipid concentration. The whole bean diet led to a significantly higher diversity of gut microbiota compared with the high fat diet. Both bean diets resulted in a lower Firmicutes/Bacteroidetes ratio, higher relative abundance of unclassified S24-7, Prevotella, Bifidobacterium, and unclassified Clostridiales, and lower abundance of Lactobacillus. Both bean diets resulted in higher formation of all cecal SCFAs (higher proportion of propionic acid and lower proportion of acetic acid) and higher plasma trimethylamine N-oxide concentrations compared with the high fat diet. Whole beans and the isolated fiber fraction exerted similar positive effects on atherosclerotic plaque amount, gut microbiota, and cecal SCFAs in Apoe−/− mice compared with the control diets.
A New Class of PSMA-617-Based Hybrid Molecules for Preoperative Imaging and Intraoperative Fluorescence Navigation of Prostate Cancer
Authors: Eder Ann-Christin , Matthias Jessica , Schäfer Martin , Schmidt Jana , Steinacker Nils , Bauder-Wüst Ulrike , Domogalla Lisa-Charlotte , Roscher Mareike , Haberkorn Uwe , Eder Matthias , Kopka Klaus
Published: February 2022

The development of PSMA-targeting low-molecular-weight hybrid molecules aims at advancing preoperative imaging and accurate intraoperative fluorescence guidance for improved diagnosis and therapy of prostate cancer. In hybrid probe design, the major challenge is the introduction of a bulky dye to peptidomimetic core structures without affecting tumor-targeting properties and pharmacokinetic profiles. This study developed a novel class of PSMA-targeting hybrid molecules based on the clinically established theranostic agent PSMA-617. The fluorescent dye-bearing candidates of the strategically designed molecule library were evaluated in in vitro assays based on their PSMA-binding affinity and internalization properties to identify the most favorable hybrid molecule composition for the installation of a bulky dye. The library’s best candidate was realized with IRDye800CW providing the lead compound. Glu-urea-Lys-2-Nal-Chx-Lys(IRDye800CW)-DOTA (PSMA-927) was investigated in an in vivo proof-of-concept study, with compelling performance in organ distribution studies, PET/MRI and optical imaging, and with a strong PSMA-specific tumor uptake comparable to that of PSMA-617. This study provides valuable insights about the design of PSMA-targeting low-molecular-weight hybrid molecules, which enable further advances in the field of peptidomimetic hybrid molecule development.
The Use of Janus Kinase Inhibitors in Axial Spondyloarthritis: Current Insights
Authors: Toussirot Eric
Published: February 2022

Current pharmacological treatments of axial spondyloarthritis (axSpA) are limited to non-steroidal anti-inflammatory drugs (NSAIDs) and biological agents, including TNFα inhibitors and IL-17 inhibitors. Despite the availability of these agents, many patients either fail to respond adequately, lose their initial therapeutic response over time, or develop undesirable side effects, thus highlighting the need for new treatment options. Janus kinase (JAK) and signal transducers and activators of transcription (STAT) are a group of intracellular kinases that play a role in the signaling pathway induced by cytokines and certain growth factors associated with the inflammatory process of axSpA. There are several lines of evidence implicating the JAK–STAT pathway in the pathophysiological process of axSpA, including genetic data, the use of certain JAK in the intracellular signal of specific cytokines involved in axSpA (IL-23, IL-22, and IL-6), and data from experimental models of SpA. This provides a rationale for the assessment of JAK inhibitors (JAKi) in clinical trials with patients with axSpA. In this review, we examine the role of JAK–STAT signaling in the pathogenesis of axSpA and summarize the results from recent clinical trials of JAKi (tofacitinib, upadacitinib, and filgotinib) in patients with axSpA.
Papaya Leaf Curl Virus (PaLCuV) Infection on Papaya (Carica papaya L.) Plants Alters Anatomical and Physiological Properties and Reduces Bioactive Components
Authors: Soni Sumit K. , Mishra Manoj Kumar , Mishra Maneesh , Kumari Swati , Saxena Sangeeta , Shukla Virendra , Tiwari Sudeep , Shirke Pramod
Published: February 2022

Papaya leaves are used frequently for curing scores of ailments. The medicinal properties of papaya leaves are due to presence of certain bioactive/pharmacological compounds. However, the papaya leaf curl virus (PaLCuV), a geminivirus, is a major threat to papaya cultivation globally. During the present investigation, we observed that PaLCuV infection significantly altered the anatomy, physiology, and bioactive properties of papaya leaves. As compared to healthy leaves, the PaLCuV-infected leaves were found to have reduced stomatal density (76.83%), stomatal conductance (78.34%), photosynthesis rate (74.87%), water use efficiency (82.51%), chlorophyll (72.88%), carotenoid (46.63%), osmolality (48.55%), and soluble sugars (70.37%). We also found lower enzymatic activity (superoxide dismutase (SOD), ascorbate peroxidase (APX), and catalase (CAT)—56.88%, 85.27%, and 74.49%, respectively). It was found that the size of guard cells (50%), transpiration rate (45.05%), intercellular CO2 concentration (47.81%), anthocyanin (27.47%), proline content (74.17%), malondialdehyde (MDA) (106.65%), and electrolyte leakage (75.38%) was elevated in PaLCuV-infected leaves. The chlorophyll fluorescence analysis showed that the infected plant leaves had a significantly lower value of maximal quantum yield of photosystem II (PSII (Fv/Fm), photochemical quantum yield of photosystem I (PSI (Y(I)), and effective quantum yield of PSII (Y(II)). However, in non-photochemical quenching mechanisms, the proportion of energy dissipated in heat form (Y(NPQ)) was found to be significantly higher. We also tested the bioactivity of infected and healthy papaya leaf extracts on a Caenorhabditis elegans (C. elegans) model system. It was found that the crude extract of papaya leaves significantly enhanced the life span of C. elegans (29.7%) in comparison to virus-infected leaves (18.4%) on application of 100 µg/mL dose of the crude extract. Our research indicates that the PaLCuV-infected leaves not only had anatomical and physiological losses, but that pharmacological potential was also significantly decreased.
Cowpea Physiological Responses to Terminal Drought—Comparison between Four Landraces and a Commercial Variety
Authors: Nunes Cátia , Moreira Rita , Pais Isabel , Semedo José , Simões Fernanda , Veloso Maria Manuela , Scotti-Campos Paula
Published: February 2022

Cowpea (Vigna unguiculata) is a robust legume; nevertheless, yield is always affected by drought, especially when it occurs during reproductive growth and seed filling. Considered a key crop in the effort to attain food security, and a suitable crop for a scenario of climate change, modern disregard for cowpea landraces is particularly detrimental as it causes genetic variability loss, compromising breeding efforts. To contribute to the evaluation of the cowpea germplasm, four Portuguese landraces (L1, L2, L3, L4) were compared with a commercial variety (CV) to evaluate their physiological responses to terminal drought and their inter-variation on productivity, under semi-controlled conditions. Despite no differences in relative water content (RWC) between the CV and the landraces under water deficit (WD), differences in leaf water potential (Ψ) defined the CV as having an isohydric control of stomata in contrast with anisohydric control for landraces. There was an identical decrease in the photosynthetic rate for all plants under stress, caused by both stomatal and non-stomatal limitations, namely, damages at the level of photosystem II as indicated by fluorescence measurements. Instantaneous water use efficiency (iWUE) was improved with stress in L1 and L3. Maintenance of higher relative chlorophyll content for longer periods in the CV revealed a stay-green phenotype. The slim differences observed in terms of stomatal control, iWUE and progression of senescence between the CV and the landraces under WD led to quite important differences in terms of productivity, as inferred from improved yield (number of pods and number of grains per plant). This is a clear result of pragmatic on-farm selection. On one hand it shows that small differences in stomatal responses or water saving strategies under WD may lead to desirable outcomes and should therefore be considered during breeding. On the other hand, it suggests that other traits could be explored in view of drought adaptation. These results highlight the need to preserve and characterize as many genetic pools as possible within a species.
Health Effect of N-Nitroso Diethylamine in Treated Water on Gut Microbiota Using a Simulated Human Intestinal Microbiota System
Authors: Wang Dedong , Jiang Yanting , Wu Di , Li Shuxue , Wu Zhendong , Zhong Yi , Zhang Lin , Guo Chongshan , Li Xiaotong , Sun Lili , Li Qin , Zhou Jinhua , He Weiyun
Published: February 2022

Chlorination disinfection byproducts (CDBPs) can exert adverse human health effects. Many toxicology-based studies confirmed the health hazards of CDBPs, but little research has been done on gut microbiome. We explored the effect of CDBPs on intestinal microbiota in the Simulator of the Human Intestinal Microbial Ecosystem (SHIME). The results showed that CDBPs slightly inhibited the production of short-chain fatty acids, and the abundance of Actinobacteria decreased in the transverse colon and descending colon. The abundance of Proteobacteria increased in the ascending colon and descending colon, while it decreased in the transverse colon. The abundance of Firmicutes decreased in both the ascending colon and descending colon. In particular, the abundance of Lachnospiraceae members, Bilophila, Oscillospira, Parabacteroides, Desulfovibrio, and Roseburia increased in the ascending colon, while the abundance of Sutterella, Bacteroides, Escherichia, Phascolarctobacterium, Clostridium, Citrobacter, and Klebsiella increased in the descending colon. The Shannon index differed significantly in both the ascending colon and descending colon before and after exposure. Overall, we demonstrate the feasibility of applying the SHIME model to studying the effects of intestinal toxicity on health of chlorinated by-products. The findings of this study improve our understanding of the health impact of CDBPs on the intestinal microbiota and better control of CDBPs in treated water is recommended.
A Combined Text-Based and Metadata-Based Deep-Learning Framework for the Detection of Spam Accounts on the Social Media Platform Twitter
Authors: Alhassun Atheer S. , Rassam Murad A.
Published: February 2022

Social networks have become an integral part of our daily lives. With their rapid growth, our communication using these networks has only increased as well. Twitter is one of the most popular networks in the Middle East. Similar to other social media platforms, Twitter is vulnerable to spam accounts spreading malicious content. Arab countries are among the most targeted, possibly due to the lack of effective technologies that support the Arabic language. In addition, as a complex language, Arabic has extensive grammar rules and many dialects that present challenges when extracting text data. Innovative methods to combat spam on Twitter have been the subject of many current studies. This paper addressed the issue of detecting spam accounts in Arabic on Twitter by collecting an Arabic dataset that would be suitable for spam detection. The dataset contained data from premium features by using Twitter premium API. Data labeling was conducted by flagging suspended accounts. A combined framework was proposed based on deep-learning methods with several advantages, including more accurate, faster results while demanding less computational resources. Two types of data were used, text-based data with a convolution neural networks (CNN) model and metadata with a simple neural networks model. The output of the two models combined identified accounts as spam or not spam. The results showed that the proposed framework achieved an accuracy of 94.27% with our combined model using premium feature data, and it outperformed the best models tested thus far in the literature.
Titanium-Containing Coagulants in Wastewater Treatment Processes in the Alcohol Industry
Authors: Kuzin Evgenii , Averina Yulia , Kurbatov Andrei , Kruchinina Natalia , Boldyrev Veniamin
Published: February 2022

As part of the work, studies of the quality of wastewater from alcohol production were carried out; the main characteristics were determined, such as the charge of dispersed particles and the content of organic compounds and fats. A comparison is made of the effectiveness of titanium salts, traditional aluminum, and iron-containing coagulants. It has been established that titanium salts are much more effective than traditional reagents, but their high cost significantly limits the scope of their application. The possibility of increasing the efficiency of traditional coagulants by adding various titanium salts in an amount of up to 10% of the mass has been proved. It has been established that the addition of titanium compounds to aluminum or iron-containing coagulants makes it possible to increase the cleaning efficiency by an average of 10–20% while a significant reduction in the dose of reagents is possible, which will have a positive effect on the cost of the cleaning process. It was found that the addition of titanium salts to a coagulant based on aluminum sulfate allows not only an increase in the cleaning efficiency but also further intensifies the processes of sedimentation and filtration of coagulation sludge, which will significantly reduce the size of the treatment equipment and reduce capital costs.
Assessment of Performance and Challenges in Use of Commercial Automated Sorting Technology for Plastic Waste
Authors: Lubongo Cesar , Alexandridis Paschalis
Published: February 2022

Recycling plastic is an important step towards a circular economy. Attaining high-quality recycled plastics requires the separation of plastic waste by type, color, and size prior to reprocessing. Automated technology is key for sorting plastic objects in medium- to high-volume plants. The current state of the art of commercial equipment for sorting plastic as well as challenges faced by Material Recovery Facilities (MRFs) to sort post-consumer plastics are analyzed here. Equipment for sorting plastic recyclables were identified using publicly available information obtained from manufacturers’ websites, press releases, and journal articles. Currently available automated sorting equipment and artificial intelligence (AI)-based sorters are evaluated regarding their functionality, efficiency, types of plastics they can sort, throughput, and accuracy. The information compiled captures the progress made during the ten years since similar reports were published. A survey of MRFs, reclaimers, and brokers in the United States identified methods of sorting used for plastic, sorting efficiency, and current practices and challenges encountered at MRFs in sorting plastic recyclables. The commercial sorting equipment can address some of the challenges that MRFs face. However, sorting of film, multilayered, blended, or mixed-material plastics is problematic, as the equipment is typically designed to sort single-component materials. Accordingly, improvements and/or new solutions are considered necessary.
The Volatility of the “Green” Option-Adjusted Spread: Evidence before and during the Pandemic Period
Authors: Ortolano Alessandra , Nissi Eugenia
Published: February 2022

The paper is an investigation on the impact of financial markets on the volatility of the green bonds credit risk component, measured by the option-adjusted spread/swap curve (OAS) before and during the pandemic period. To this purpose, after observing the dynamic joint correlations between all the variables, we adopt Exponential and Generalized AutoRegressive Conditional Heteroskedasticity models, putting the OAS as dependent variable. Our main results show that the conditional variance parameters are significant and persistent in both times, testifying the overall impact of the other markets on the OAS. In more detail, we highlight that the gamma in the two Exponential models is positive: so, the “green” credit risk volatility is more sensitive to positive shocks than to negative ones. With reference to the conditional mean, we note that if during the non-pandemic period only the stock market is significant, during the pandemic also conventional bonds and gold are impacting. To the best of our knowledge this is the first study that analyzes the specific credit risk component of the green bond yields: we deem our findings useful to observe the change of green bonds creditworthiness in a complex market context and interesting in terms of policy implications.
Novel Reconfigurable Spherical Parallel Mechanisms with a Circular Rail
Authors: Laryushkin Pavel , Antonov Anton , Fomin Alexey , Glazunov Victor
Published: February 2022

The COVID-19 pandemic has placed unprecedented stress on the world healthcare system and demonstrated the need for modern automated robotic solutions for numerous medical applications. Often, robots that provide spherical motion of the end-effector are used in this area. In this paper, we discuss a spherical mechanism with a circular rail and provide several possible variations of the design: spherical robots with three or four legs and 4-DOF robots with an additional translational DOF, including a decoupled mechanism. The screw theory is used to analyze the mobility of the discussed mechanisms, and their advantages and drawbacks are discussed.
Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks
Authors: Tsinganos Panagiotis , Jansen Bart , Cornelis Jan , Skodras Athanassios
Published: February 2022

In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. (1) Background: In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. Recently, a specific class of these models called Temporal Convolutional Networks (TCNs) has been successfully applied to this task. (2) Methods: In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using TCNs. Specifically, we investigate the real-time behavior of our previous TCN model by performing a simulation experiment on a recorded sEMG dataset. (3) Results: The proposed network trained with data augmentation yields a small improvement in accuracy compared to our existing model. However, the classification accuracy is decreased in the real-time evaluation, showing that the proposed TCN architecture is not suitable for such applications. (4) Conclusions: The real-time analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.
A Preliminary Exploration of the Placental Position Influence on Uterine Electromyography Using Fractional Modelling
Authors: Şan Müfit , Batista Arnaldo , Russo Sara , Esgalhado Filipa , dos Reis Catarina R. Palma , Serrano Fátima , Ortigueira Manuel
Published: February 2022

The uterine electromyogram, also called electrohysterogram (EHG), is the electrical signal generated by uterine contractile activity. The EHG has been considered an expanding technique for pregnancy monitoring and preterm risk evaluation. Data were collected on the abdominal surface. It has been speculated the effect of the placenta location on the characteristics of the EHG. In this work, a preliminary exploration method is proposed using the average spectra of Alvarez waves contractions of subjects with anterior and non-anterior placental position as a basis for the triple-dispersion Cole model that provides a best fit for these two cases. This leads to the uterine impedance estimation for these two study cases. Non-linear least square fitting (NLSF) was applied for this modelling process, which produces electric circuit fractional models’ representations. A triple-dispersion Cole-impedance model was used to obtain the uterine impedance curve in a frequency band between 0.1 and 1 Hz. A proposal for the interpretation relating the model parameters and the placental influence on the myometrial contractile action is provided. This is the first report regarding in silico estimation of the uterine impedance for cases involving anterior or non-anterior placental positions.
Bearing Fault Reconstruction Diagnosis Method Based on ResNet-152 with Multi-Scale Stacked Receptive Field
Authors: Yu Hu , Miao Xiaodong , Wang Hua
Published: February 2022

The axle box in the bogie system of subway trains is a key component connecting primary damper and the axle. In order to extract deep features and large-scale fault features for rapid diagnosis, a novel fault reconstruction characteristics classification method based on deep residual network with a multi-scale stacked receptive field for rolling bearings of a subway train axle box is proposed. Firstly, multi-layer stacked convolutional kernels and methods to insert them into ultra-deep residual networks are developed. Then, the original vibration signals of four fault characteristics acquired are reconstructed with a Gramian angular summation field and trainable large-scale 2D time-series images are obtained. In the end, the experimental results show that ResNet-152-MSRF has a low complexity of network structure, less trainable parameters than general convolutional neural networks, and no significant increase in network parameters and calculation time after embedding multi-layer stacked convolutional kernels. Moreover, there is a significant improvement in accuracy compared to lower depths, and a slight improvement in accuracy compared to networks than unembedded multi-layer stacked convolutional kernels.
Development of a Deep-Sea Submersible Chemiluminescent Analyzer for Sensing Short-Lived Reactive Chemicals
Authors: Taenzer Lina , Grabb Kalina , Kapit Jason , Pardis William , Wankel Scott D. , Hansel Colleen M.
Published: February 2022

Based on knowledge of their production pathways, and limited discrete observations, a variety of short-lived chemical species are inferred to play active roles in chemical cycling in the sea. In some cases, these species may exert a disproportionate impact on marine biogeochemical cycles, affecting the redox state of metal and carbon, and influencing the interaction between organisms and their environment. One such short-lived chemical is superoxide, a reactive oxygen species (ROS), which undergoes a wide range of environmentally important reactions. Yet, due to its fleeting existence which precludes traditional shipboard analyses, superoxide concentrations have never been characterized in the deep sea. To this end, we have developed a submersible oceanic chemiluminescent analyzer of reactive intermediate species (SOLARIS) to enable continuous measurements of superoxide at depth. Fluidic pumps on SOLARIS combine seawater for analysis with reagents in a spiral mixing cell, initiating a chemiluminescent reaction that is monitored by a photomultiplier tube. The superoxide in seawater is then related to the quantity of light produced. Initial field deployments of SOLARIS have revealed high-resolution trends in superoxide throughout the water column. SOLARIS presents the opportunity to constrain the distributions of superoxide, and any number of chemiluminescent species in previously unexplored environments.
Gender Identification in a Two-Level Hierarchical Speech Emotion Recognition System for an Italian Social Robot
Authors: Guerrieri Antonio , Braccili Eleonora , Sgrò Federica , Meldolesi Giulio Nicolò
Published: February 2022

The real challenge in Human-Robot Interaction (HRI) is to build machines capable of perceiving human emotions so that robots can interact with humans in a proper manner. Emotion varies accordingly to many factors, and gender represents one of the most influential ones: an appropriate gender-dependent emotion recognition system is recommended indeed. In this article, we propose a Gender Recognition (GR) module for the gender identification of the speaker, as a preliminary step for the final development of a Speech Emotion Recognition (SER) system. The system was designed to be installed on social robots for hospitalized and living at home patients monitoring. Hence, the importance of reducing the software computational effort of the architecture also minimizing the hardware bulkiness, in order for the system to be suitable for social robots. The algorithm was executed on the Raspberry Pi hardware. For the training, the Italian emotional database EMOVO was used. Results show a GR accuracy value of 97.8%, comparable with the ones found in the literature.
Formulation Optimization of Extemporaneous Oral Liquids Containing Naloxone and Propranolol for Pediatric Use
Authors: Attebäck Maria , Hedin Bengt , Mattsson Sofia
Published: February 2022

There is a need to develop dosage forms suitable for children to improve drug treatment. Extemporaneous compounding of drugs for children is one way to meet these needs. However, excipients generally considered as safe in adults may not be appropriate in dosage forms intended for children. The aim was to optimize the composition of two pediatric liquid preparations by substituting paraben as a microbiological preservative and ethanol as a solubilizer, with excipients more suitable for pediatric use. The oral liquids were Naloxone 1 mg/mL and Propranolol 10 mg/mL. Twelve different formulations were tested with propranolol and naloxone, respectively, during the screening process to select appropriate formulations. Sodium benzoate and glycerol were used as a preservative and solubilizer, respectively, and different pH of the formulations were evaluated. The formulations were characterized according to dispensed dose (dosing accuracy), viscosity and osmolality. The optimized formulations from the screening process were tested with two amounts of sodium benzoate and microbiological assays were performed. These formulations were shown to have satisfactory preservative properties and dosing accuracy. The results showed that the oral liquids could be prepared without the addition of solubilizer and with lower osmolality (naloxone), thus reducing the risk of gastrointestinal side effects.
Potato Farming Systems from a Social-Ecological Perspective: Identifying Key Points to Increase Resilience in a High Andean Productive Landscape
Authors: Martinez Eliana , Luna-Mancilla Lizeth Tatiana , Ramos-Zambrano Housseman Steven , Vidal-Gomez Ulianova
Published: February 2022

Social-ecological resilience (SER), understood as the capacity to prevent, react to, and mitigate crises that affect social-ecological systems, provides an integrative framework to analyze agricultural challenges. Based on this approach, key points that affect the sustainability of productive landscapes are addressed and evaluated, providing a baseline from which to improve farming systems at different scales. Hence, the aim of this work is to assess SER in potato crops in the Nariño area in southwestern Colombia, a region where strategies to increase resilience must be implemented. Following the methodology proposed by the UNU-IAS (2014), potato producers’ thoughts and perceptions were evaluated by implementing eleven workshops in seven municipalities. Five main integrative factors (twenty indicators of resilience) were examined and scored during the assessment: (1) governance and social equity, (2) livelihood and well-being, (3) knowledge and innovation, (4) landscape diversity and ecosystem protection, and (5) agrobiodiversity and sustainable natural resource management. Participants evaluated each indicator from 1 to 5 (1 being low performance and 5 extremely good performance). The results were calculated and averaged. Prior to the assessment, participatory techniques to generate collective reflection on resilience and landscape management were performed. The results showed that farmers rated SER resilience from low to moderate (from 2.5 to 3.2), with “well-being” (2.5) and “knowledge and innovation” (2.7) being the worst-rated factors. The data evidence deficiencies in all the indicators examined. Issues that constrain SER are related to the lack of capacity to create bio-industries, small livelihood portfolios, pollution, loss of natural areas (which impacts biodiversity and ecosystem services), and the loss of ancestral knowledge. The producers requested, as short-term actions, increases in technical assistance (to promote innovation and business initiatives) and farm diversification programs (to take advantage of their native potatoes diversity). They also agreed on the need for associative figures to enhance capacity-building among producers. These findings confirm deficiencies that minimize the sustainability of this system. Actions that impact positively almost all indicators are required to improve not only productivity but also the population’s well-being.
Towards a Software Tool Supporting Decisions in Planning Heap Revitalization Processes
Authors: Bialas Andrzej
Published: February 2022

This paper presents an advanced risk management methodology which supports the planning of the revitalization process of post-mining heaps. More specifically, it supports decision-makers in the selection of the most advantageous revitalization actions with respect to the defined criteria embracing risk and cost–benefit parameters as well as different qualitative factors. This methodology was elaborated with a view to software implementation and is a good example of ICT adoption in emerging domains of application. The question is how to organize the revitalization decision process and support it with a software tool. The methodology and tool are based on three pillars: risk, cost–benefit, and qualitative criteria assessments of the considered revitalization actions in order to select the target for the implementation of the given heap. This paper presents the methodology implemented in the software as well as its validation on an extensive example. The steps described in the example allow the decision-maker to identify a target revitalization alternative. The conclusions focus on the solution’s feasibility and the software implementation and extensions.
Reducing Food Waste at Retail Stores—An Explorative Study
Authors: Riesenegger Lena , Hübner Alexander
Published: February 2022

Grocery retailers are in a dilemma. They often prioritize availability over other aspects due to strong competition in this sector and the imperative of realizing sales. The target for many grocery retailers has been high on-shelf availability and large variety to increase customer satisfaction. However, this policy contributes to a significant share of overstock. The economic pressure of unsold products, the environmental impact of wasted resources, and the ethical questions arising from discarding edible food, have increasingly thrown the spotlight on grocery retailers to change their strategies. Grocery retailers are thus facing a trade-off between increasing attractiveness via high availability on the one hand, and the environmental, social, and financial impacts of overstock, on the other. One common practice in dealing with overstock is mainly being reactive to mitigate the impact, using initiatives such as price promotions or donations. This explorative study investigates options for how grocery retailers can proactively reduce food waste via better planning of their store operations. Seven case companies participated in this qualitative study, where we focused on ultra-fresh products as the most important waste category. Face-to-face interviews with managers were the primary source for data collection. The heterogeneity of our sample enabled us to build a common understanding of proactive options to reduce food waste with enhanced operations. The analysis reveals six coherent and distinct topics. A basis for all proactive operational planning processes is (1) the use of a comprehensive database and information systems. This builds the foundation for (2) tailored demand forecasts related to perishable product-specific requirements. Subsequently, consideration is needed of (3) the enhanced planning of assortment sizes, (4) the definitions of differentiated service levels and (5) the tailored ordering and replenishment processes that impact food waste. Finally, (6) salvage options, such as dynamic pricing, secondary usage, and sustainable waste streams constitute valuable mitigation strategies. We formulated 15 propositions that could support the decisions of grocery retailers developing proactive food waste reduction practices. These propositions will guide future research, as they provide a coherent and cohesive picture of related topics in grocery retail operations.
Assessment and Forecast of Green Total Factor Energy Efficiency in the Yellow River Basin—A Perspective Distinguishing the Upper, Middle and Lower Stream
Authors: Ma Minglu , Wang Qiang
Published: February 2022

As the fifth-longest river globally, the Yellow River is of great importance to the world’s ecological protection. Due to its location as an essential ecological barrier and economic zone, it is imperative to balance energy support and ecological management in the basin. In this process, improving energy efficiency is crucial solution. Distinguished into upstream, midstream, and downstream, we measured the trajectory of green total factor energy efficiency over the past fifteen years using the Super-Epsilon-based model. Further, we identified the heterogeneity of energy efficiency within different river basins with the help of kernel density estimation. We used it to analyze the geographical and policy reasons affecting energy efficiency fluctuations. Finally, we constructed high, medium, and low GDP growth scenarios, and used a long short-term memory neural network model to predict energy efficiency forecasts in each scenario. The study results clarified that the overall energy efficiency showed an upward trend since 2013. Among them, the most significant improvement in energy efficiency was observed upstream, while the energy efficiency in the middle and lower stream showed a decreasing trend. Regarding future development trends, an economic growth rate of 6.5% was most favorable for energy efficiency compared to 6% and 7%. This finding reminded us to be alert to the ecological condition of the lower Yellow River basin. In addition, maintaining an appropriate economic growth rate is helpful for the balance between development and ecology.
Assessment of the Mechanisms of Summer Thermal Environment of Waterfront Space in China’s Cold Regions
Authors: Fei Fan , Wang Yan , Jia Xiaoyun
Published: February 2022

Water is an essential part of the urban ecosystem and plays a vital role in alleviating urban heat island (UHI) problems. The contribution toward UHI mitigation made by bodies of water needs to be ascertained to establish waterfront thermal environment construction standards. In this study, the thermal environment of the waterfront space of Tianjin in the cold regions of China was the research object. Through a survey including 141 valid questionnaires and the field measurement of four typical waterfront spaces in Tianjin, the thermal demand characteristics of recreational use for the waterfront environment and the influence of water on microclimate are discussed, supplemented by results from low-altitude infrared remote sensing technology, which was mainly used to obtain a wider range of infrared thermal images with higher accuracy. To improve the urban heat island effect and the quality of the ecological environment, this paper used outdoor thermal environment simulation software to quantitatively analyze the thermal environmental impact of outdoor public activity spaces around the representative urban body of water and proposes the optimization scheme of the waterfront space’s thermal environment. The results show that, based on the factors of water itself, the most economical water width was 70–80 m, and the cooling effect intensity of water had an essential correlation with the distance between the measured site and the water center. In terms of the environmental factors around the water, when the green lawn of the waterfront space was 12 m and the water shore’s geometric form was S-shaped, this could improve the cooling effect of water significantly. Waterfront activity spaces should focus on thermal comfort on the east and south water shores. It is expected that this study could provide practical implications and useful guidance for the planning and design of urban waterfront space in China’s cold regions.
Core Self-Evaluation, Emotional Reactivity to Interpersonal Conflict, and Subjective Well-Being: The Moderating Role of Horizontal Collectivism
Authors: Oh Sunyoung
Published: February 2022

This study aims to examine a moderated mediation model wherein core self-evaluation (CSE) and horizontal collectivism (HC) interact to predict negative emotional reactivity to interpersonal conflict, and thus HC moderates the indirect effects of CSE on subjective well-being (SWB) through emotional reactivity. A short-term prospective study was conducted with 257 South Korean university students. Participants completed measures of CSE and HC, and then reported their experiences with interpersonal conflict and SWB in an online survey about two weeks later. We found that, among low HC participants, after controlling for the importance of conflict issues, individual differences in CSE predicted emotional reactivity to interpersonal conflict, and emotional reactivity was in turn negatively associated with SWB. By contrast, among high-HC participants, emotional reactivity to interpersonal conflict did not differ as a function of CSE, and the indirect effects of CSE on SWB were not significant. These findings highlight the importance of high CSE and HC values in fostering emerging adults’ resilience against the detrimental effects of interpersonal conflict in sustaining individuals’ SWB. Implications for conflict management interventions are discussed.
We’re All in This Together: Legitimacy and Coronavirus-Oriented CSR Messaging
Authors: Browning Nicholas , Lee Ejae , Lee Sung Hyun , Yang Sung-Un
Published: February 2022

This study investigates how legitimization strategies embedded in CSR messages related to the COVID-19 pandemic influenced multidimensional stakeholder assessments of reputation. The results of this 3 × 2 × 2 experimental survey, which manipulated pragmatic and moral legitimacy using three conditions (self- vs. other- vs. both-oriented messaging); substantive and symbolic management (informative vs. uninformative content); and popularization and standardization approaches (leadership vs. followership), indicate that popularization strategies communicated substantively and standardization strategies communicated symbolically generally yield the greatest reputational gains. More nuanced findings from three-way interaction effects are further discussed, with an emphasis on the role of double-sided messages seeking to simultaneously establish pragmatic and moral legitimacy.
The Effect of ICT Use on the Profitability of Young Agripreneurs in Malawi
Authors: Jolex Aubrey , Tufa Adane
Published: February 2022

The agricultural sector’s significant position in an economy and high potential benefits of agricultural transformation give developing countries major opportunities, especially for youth, to commercially start an agricultural enterprise. Increasing youth engagement in agriculture is fundamental for sustainably transforming agriculture and reducing youth unemployment. In achieving this, information and communication technologies hold great potential. Harnessing youth agribusiness opportunities through the use of ICT and its innovations are key to increasing profitability and providing employment. The study assesses the effect of the intensity of ICT use on profit using data collected from 317 young agripreneurs and an ordered logit model. The results show that profitability increases with the number of ICT tools used to receive and disseminate information relevant to agribusinesses. Therefore, relevant stakeholders should strive towards the implementation of programs that increase the number of ICT tools that can be used in agribusinesses.
Compilation of Chemistry Experiments for an Online Laboratory Course: Student’s Perception and Learning Outcomes in the Context of COVID-19
Authors: Chans Guillermo M. , Bravo-Gutiérrez Mireille E. , Orona-Navar Angelica , Sánchez-Rodríguez Elvia P.
Published: February 2022

The COVID-19 pandemic forced a quick change of the teaching styles to online lessons; specifically, experimental classes had to be redesigned to achieve the best possible academic performance within the imposed limitations. This work describes three different approaches: adaptation of a laboratory chemistry course to an online mode, learning proficiency, and students’ perception. First, a compilation of experiments that cover topics from general chemistry at an undergraduate level was included, with activities that can be conducted employing daily-use materials and substances. Next, the learning achieved was estimated, and the grades were related to a domain level of competency acquisition. The results indicated that at least 68% accomplished the highest level. Finally, the perception of the participants about the activities was inquired. The statistical analysis showed a generalized positive attitude towards the mode proposed, an appreciation of having earned meaningful knowledge, and most of the students stated they would recommend the course.
Performance Analysis of Machine Learning Algorithms for Energy Demand–Supply Prediction in Smart Grids
Authors: Cebekhulu Eric , Onumanyi Adeiza James , Isaac Sherrin John
Published: February 2022

The use of machine learning (ML) algorithms for power demand and supply prediction is becoming increasingly popular in smart grid systems. Due to the fact that there exist many simple ML algorithms/models in the literature, the question arises as to whether there is any significant advantage(s) among these different ML algorithms, particularly as it pertains to power demand/supply prediction use cases. Toward answering this question, we examined six well-known ML algorithms for power prediction in smart grid systems, including the artificial neural network, Gaussian regression (GR), k-nearest neighbor, linear regression, random forest, and support vector machine (SVM). First, fairness was ensured by undertaking a thorough hyperparameter tuning exercise of the models under consideration. As a second step, power demand and supply statistics from the Eskom database were selected for day-ahead forecasting purposes. These datasets were based on system hourly demand as well as renewable generation sources. Hence, when their hyperparameters were properly tuned, the results obtained within the boundaries of the datasets utilized showed that there was little/no significant difference in the quantitative and qualitative performance of the different ML algorithms. As compared to photovoltaic (PV) power generation, we observed that these algorithms performed poorly in predicting wind power output. This could be related to the unpredictable wind-generated power obtained within the time range of the datasets employed. Furthermore, while the SVM algorithm achieved the slightly quickest empirical processing time, statistical tests revealed that there was no significant difference in the timing performance of the various algorithms, except for the GR algorithm. As a result, our preliminary findings suggest that using a variety of existing ML algorithms for power demand/supply prediction may not always yield statistically significant comparative prediction results, particularly for sources with regular patterns, such as solar PV or daily consumption rates, provided that the hyperparameters of such algorithms are properly fine tuned.
Publisher’s Note: Page Numbers Changed to Article Numbers for Articles Published in Thalassemia Reports Volumes 1–11
Authors:   Thalassemia Reports Editorial Office
Published: February 2022

From Volume 1 (2011) to Volume 11 (2021), Thalassemia Reports [...]
Polarization Transfer Rates by Isotropic Collisions between Astrophysical SiO Molecule and Electrons
Authors: Derouich Moncef , Zaheer Ahmad Badruddin , Alruhaili Aied , Qutub Saleh
Published: February 2022

We are interested in quantum calculations of polarization transfer (PT) rates due to collisions of the SiO molecule with the electrons. We determine the inelastic PT rates associated to the transitions: X 1Σ+→3Π; X 1Σ+→3Σ+; X 1Σ+→3Δ; X 1Σ+→3Σ−. In addition, we calculate the elastic PT rates due to rotational transitions inside the electronic state X 1Σ+ which are related to observed astronomical SiO MASERs. Our PT rates are obtained through linear combination of excitation rates previously calculated for SiO-electron collisions. The calculations are performed on a collision energy grid large enough to ensure converged state-to-state rates for temperatures ranging from 1000 to 10,000 K for inelastic rates and from 5 to 5000 K for elastic rates. The dependence of the inelastic rates on temperatures is obtained analytically and given in useful form.