Miracle-dz/UniFAR-FTUs huggingface dataset:
A DOA Tracking Method Based on Offset Compensation Using Nested Array

In this brief, DOA tracking using a nested array (NA) is discussed and a modified tracking method is proposed. Discrete Fourier transform (DFT) is firstly used to estimate DOAs at the beginning moment. Thereafter, we realize DOA tracking through compensating offsets of adjacent moments. Offsets can be obtained by total least squares (TLS), which is constructed via first-order Taylor expansion. Meanwhile, the noise component is taken into account to strengthen the robustness to noise influence, whose power can be estimated as a by-product. The proposed method is simple to implement and requires no data association operation with multiple DOAs. Compared with other state-of-art DOA tracking methods, it has much better DOA tracking performance while with medium complexity, which achieves a good compromise between performance and computational complexity. The feasibility of this approach is verified by experimental simulations.
Industrially relevant pectinase production from Aspergillus parvisclerotigenus KX928754 using apple pomace as the promising substrate

The present study was focused on the potentiality of agro-based residues for the production of pectinase to meet the growing market demand by improving the yield with low cost of production. Among the agro-based residues used for the production of pectinase, apple pomace was able to produce the maximum of 1366.30 ± 36.71 U/ml using Aspergillus parvisclerotigenus KX928754 in liquid static surface fermentation, followed by sugarcane bagasse (973.12 ± 22.43 U/ml) and used tea (686.7 ± 45.06 U/ml). The process parameters optimization using a single variable at a time affirmed that pH 7.0, incubation period of 168 h, 30°C temperature, sucrose 2% as carbon source and peptone 3% as nitrogen source was found to be optimum for better production. The crude filtrate was purified by precipitation, dialyzed, eluted on Sephadex G-100 column followed by lyophilization and stored at −20°C. A. parvisclerotigenus KX928754 pectinase was purified to 2.10-fold, 2.91% of yield rate and having a specific activity of 1081.66 U/mg. Moreover, the electrophoretic analysis through sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) revealed 37.4 kDa of protein from the purified pectinase. Thus, the use of apple pomace as a substrate for scaling up pectinase with efficient recovery could reduce the price of the enzyme and increase its avenue for different industrial exploitation.
Biological Nitrogen Fixation in Sediments of a Cyanobacterial Bloom‐Occurring Bay in One Eutrophic Shallow Lake: Occurrence and Related Environmental Factors

Biological nitrogen fixation (BNF) is an important source of bioavailable nitrogen in the aquatic environment that supports primary production. However, little is known about its occurrence and potential in eutrophic freshwater habitats. This study investigated the diazotrophic diversity and seasonal variation of nitrogenase activity (NA) in the sediments of a cyanobacterial bloom‐occurring bay of a freshwater lake. Sequencing analysis of the nifH gene revealed that diazotrophs in Clusters I and III constituted the dominant components of the diazotrophic community in Meiliang Bay. Both nifH and 16S rRNA gene sequencing indicated that the microbes in sediments have different community structures during the winter than during other seasons. The concentrations of dissolved sulfide, sulfate, and nitrate in pore water were significantly correlated with NA in sediments; however, the Mantel test indicated that dissolved sulfide had a more significant effect on the structure of the diazotrophic community than did other environmental factors. Laboratory culture experiments under different redox conditions further confirmed the inhibitory effect of dissolved sulfide on BNF in sediments. This study revealed that the BNF in sediments of Meiliang Bay was closely linked to variations in dissolved sulfide concentration, which has broadened our understanding of BNF in freshwater lakes.
A Comparative Analysis of Multi-Criteria Decision-Making Methods for Resource Selection in Mobile Crowd Computing

In mobile crowd computing (MCC), smart mobile devices (SMDs) are utilized as computing resources. To achieve satisfactory performance and quality of service, selecting the most suitable resources (SMDs) is crucial. The selection is generally made based on the computing capability of an SMD, which is defined by its various fixed and variable resource parameters. As the selection is made on different criteria of varying significance, the resource selection problem can be duly represented as an MCDM problem. However, for the real-time implementation of MCC and considering its dynamicity, the resource selection algorithm should be time-efficient. In this paper, we aim to find out a suitable MCDM method for resource selection in such a dynamic and time-constraint environment. For this, we present a comparative analysis of various MCDM methods under asymmetric conditions with varying selection criteria and alternative sets. Various datasets of different sizes are used for evaluation. We execute each program on a Windows-based laptop and also on an Android-based smartphone to assess average runtimes. Besides time complexity analysis, we perform sensitivity analysis and ranking order comparison to check the correctness, stability, and reliability of the rankings generated by each method.
Locally-Constrained de Bruijn Codes: Properties, Enumeration, Code Constructions, and Applications

The de Bruijn graph, its sequences, and their various generalizations, have found many applications in information theory, including many new ones in the last decade. In this paper, motivated by a coding problem for emerging memory technologies, a set of sequences which generalize the window property of de Bruijn sequences, on its shorter subsequences, are defined. These sequences can be also defined and viewed as constrained sequences. Hence, they will be called locally-constrained de Bruijn sequences and a set of such sequences will be called a locally-constrained de Bruijn code. Several properties and alternative definitions for such codes are examined and they are analyzed as generalized sequences in the de Bruijn graph (and its generalization) and as constrained sequences. Various enumeration techniques are used to compute the total number of sequences for any given set of parameters. A construction method of such codes from the theory of shift-register sequences is proposed. Finally, we show how these locally-constrained de Bruijn sequences and codes can be applied in constructions of codes for correcting synchronization errors in the $\ell $ -symbol read channel and in the racetrack memory channel. For this purpose, these codes are superior in their size to previously known codes.
A new species of Gymnopus sect. Androsacei (Omphalotaceae, Agaricales) from China

A new species of Gymnopus sect. Androsacei, namely, G. pallipes is described and illustrated based on morphological and molecular phylogenetic evidence. It is characterized by marasmioid basidiomata, a dark brown to reddish brown pileus becoming dull white to yellowish grey with age, whitish to pale yellow stipe and presence of rhizomorphs. Phylogenetic analyses support it as a new species within Gymnopus sect. Androsacei. The detailed morphological description, colour photos of basidiomata, and line drawings of microcharacters are presented and delimitation characters from similar species are discussed. A key to the known species of Gymnopus s. str. from China is also provided.
Rescheduling‐based congestion management by metaheuristic algorithm: Hybridizing lion and moth search models

Congestion management (CM) is becoming a challenging task in the power system due to the increase in the system complexities in the deregulated environment. The congestion causes grave damage to the power system components if the outages tend to occur more frequently. Hence, in the present power system scenario, CM is a complex task of an independent system operator (ISO). This proposal intends to introduce a novel optimization‐based CM model with advanced soft computing techniques. A new hybrid algorithm is introduced in this paper, which optimizes the generating power of added generators with the bus system. Thereby manages the congestion with minimized rescheduling cost. The proposed optimization algorithm termed Lion Algorithm with Moth‐based Mutation (LA‐MM) hybridizes the concepts of both Lion Algorithm (LA) and Moth Search Algorithm (MS). Subsequently, the experimentation is conducted in two test bus systems like IEEE 14 and 30 bus systems. The performance of the proposed model is compared over other state‐of‐the‐art models concerning cost analysis and convergence analysis, respectively.
Production of Omega-3 Oil by Aurantiochytrium mangrovei Using Spent Osmotic Solution from Candied Fruit Industry as Sole Organic Carbon Source

Osmotic dehydration is an important phase in the production of dried products, including most fruits and vegetables, in the food industry. The drying process for candied fruit produces a liquid waste called “spent osmotic solution”, which is characterized by a high content of organic compounds, mostly dissolved sugars. The sugar content of this food by-product could be valorized through the growth of biomass with a high added value. In this study, the spent osmotic solution from the candied fruit industry was used as an organic carbon source for the growth and production of docosahexaenoic acid (DHA) in the cultivation of Aurantiochytrium mangrovei RCC893. The carbon content of the standard media was completely replaced by the sugars present in this food by-product. After that, the growth condition of this strain was optimized through response surface methodologies using a central composite design (CCD), and the optimal combination of the spent osmotic solution and nitrogen was established. Moreover, a scale-up trial was performed using the optimal conditions obtained after CCD to evaluate the scalability of the process.
How Political Identity Shapes Customer Satisfaction

This article examines the effect of political identity on customers’ satisfaction with the products and services they consume. Recent work suggests that conservatives are less likely to complain than liberals. Building on that work, the present research examines how political identity shapes customer satisfaction, which has broad implications for customers and firms. Nine studies combine different methodologies, primary and secondary data, real and hypothetical behavior, different product categories, and diverse participant populations to show that conservatives (vs. liberals) are more satisfied with the products and services they consume. This happens because conservatives (vs. liberals) are more likely to believe in free will (i.e., that people have agency over their decisions) and, therefore, to trust their own decisions. The authors document the broad and tangible downstream consequences of this effect for customers’ repurchase and recommendation intentions and firms’ sales. The association of political identity and customer satisfaction is attenuated when belief in free will is externally weakened, choice is limited, or the consumption experience is overwhelmingly positive.
The Review of Economics and Statistics

— Violent conﬂicts, particularly at election times in Africa, are a common cause of instability and economic disruption. This paper studies how ﬁrms react to electoral violence using the case of Kenyan ﬂower exporters during the 2008 postelection violence as an example. The violence induced a large negative supply shock that reduced exports primarily through workers’ absence and had heterogeneous effects: larger ﬁrms and those with direct contractual relationships in export markets suffered smaller production and loss of workers. On the demand side, global buyers were not able to shift sourcing to Kenyan exporters located in areas not directly affected by the violence or to neighboring Ethiopian suppliers. Consistent with difﬁculties in ensuring against supply-chain risk disruptions caused by electoral violence, ﬁrms in direct contractual relationships ramp up shipments just before the subsequent 2013 presidential election to mitigate risk.
Examining the resiliency of intertwined supply networks: a jury-rigging perspective

The COVID-19 pandemic has highlighted the salience of different supply networks merging together to provide critical products in short supply. The automotive supply network and the medical device supply network, for instance, became intertwined to meet rising demands for ventilators. As such, coming to the fore is a search mechanism, based on recombination of multiple components, conceptualized as jury-rigging behavior. Designing and changing the interdependencies across firms/supply chains become relevant. Drawing on complex adaptive systems and Ashby's law of requisite variety, we use a computational model to examine these mechanisms’ isolated and combined effect on supply chain network resiliency. Our results show statistically significant difference in the adaptiveness and mortality rate between a jury-rigged supply chain network and a non-jury-rigged one. In particular, the effect of jury-rigging is diminished by the level of coupling among the supply chains.
Isolation in Natural Host Cell Lines of Wolbachia Strains wPip from the Mosquito Culex pipiens and wPap from the Sand Fly Phlebotomus papatasi

Simple Summary Diverse strains of Wolbachia bacteria, carried by many arthropods, as well as some nematodes, interact in many different ways with their hosts. These include male killing, reproductive incompatibility, nutritional supplementation and suppression or enhancement of the transmission of diseases such as dengue and malaria. Consequently, Wolbachia have an important role to play in novel strategies to control human and livestock diseases and their vectors. Similarly, cell lines derived from insect hosts of Wolbachia constitute valuable research tools in this field. During the generation of novel cell lines from mosquito and sand fly vectors, we isolated two strains of Wolbachia and demonstrated their infectivity for cells from a range of other insects and ticks. These new insect cell lines and Wolbachia strains will aid in the fight against mosquitoes, sand flies and, potentially, ticks and the diseases that these arthropods transmit to humans and their domestic animals. Abstract Endosymbiotic intracellular bacteria of the genus Wolbachia are harboured by many species of invertebrates. They display a wide range of developmental, metabolic and nutritional interactions with their hosts and may impact the transmission of arboviruses and protozoan parasites. Wolbachia have occasionally been isolated during insect cell line generation. Here, we report the isolation of two strains of Wolbachia, wPip and wPap, during cell line generation from their respective hosts, the mosquito Culex pipiens and the sand fly Phlebotomus papatasi. wPip was pathogenic for both new C. pipiens cell lines, CPE/LULS50 and CLP/LULS56, requiring tetracycline treatment to rescue the lines. In contrast, wPap was tolerated by the P. papatasi cell line PPL/LULS49, although tetracycline treatment was applied to generate a Wolbachia-free subline. Both Wolbachia strains were infective for a panel of heterologous insect and tick cell lines, including two novel lines generated from the sand fly Lutzomyia longipalpis, LLE/LULS45 and LLL/LULS52. In all cases, wPip was more pathogenic for the host cells than wPap. These newly isolated Wolbachia strains, and the novel mosquito and sand fly cell lines reported here, will add to the resources available for research on host–endosymbiont relationships, as well as on C. pipiens, P. papatasi, L. longipalpis and the pathogens that they transmit.
Identification of potential biomarkers in ovarian carcinoma and an evaluation of their prognostic value

Background Ovarian cancer is one of the most common malignant tumors in female genital organs, and its incidence rate is high. However, the pathogenesis and prognostic markers of ovarian cancer are unclear. This study sought to screen potential markers of ovarian cancer and to explore their prognostic value. Methods The Cancer Genome Atlas, Gene Expression Omnibus, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were used in this study. The least absolute shrinkage and selection operator (LASSO), multivariate Cox regression and stepwise regression analysis were chosen to screen genes and construct risk model. Gene Set Enrichment Analysis (GSEA) and an immune-infiltration analysis were performed. Results One hundred thirty two co-expressed genes were found. They involved in metabolism, protein phosphorylation, mitochondria, and immune signaling pathways. Twelve genes significantly related to the survival of ovarian cancer were identified. Eight risk genes (i.e., CACNB1, FAM120B, HOXB2, MED19, PTPN2, SMU1, WAC.AS1, and BCL2L11) were further screened and used to construct the risk model. The risk status might be an independent prognostic factor of ovarian cancer, and most of the biological functions of genes expressed in high-risk ovarian cancer were related to synapse, adhesion, and immune-related functions. The clusters of CD4+ T cells and M2 macrophages were high in high-risk status samples. Conclusions In ovarian cancer, the abnormal expression of 8 genes, including CACNB1, FAM120B, HOXB2, MED19, PTPN2, SMU1, WAC.AS1, and BCL2L11, is closely related to ovarian cancer progression, and these genes can serve as independent prognosis markers of ovarian cancer.
Strengthening Resilience in Critical Infrastructure Systems: A Deep Learning Approach for Smart Early Warning of Critical States

Systems of critical infrastructures are characterized by strong interdependencies and the developments of urban areas towards Smart Cities even increase the underlying complexity due to growing automation and interconnectedness. A system of highly cross-linked components is especially prone to systemic risks making concepts of resilience accordingly important. One way for being able to withstand in times of stress, maintain security of supply, and promote adaptive and anticipative capabilities, is to establish early warning capabilities. As cities are complex and rather chaotic socio-technical systems reigned by randomness, the caused parametric uncertainties challenge modeling approaches that are intended to support robust decision-making. Sophisticated methods based on artificial intelligence can play an essential role in this case, as they perform well on highly complex environments and large data set. To study resilience, the urban area is split into zones where the city’s state is determined by the states of these zones and the state of a zone is characterize d by the criticalities of infrastructures accommodated there. Considering criticality as an atomic building block for urban performance assessments, this paper proposes a zone-based state forecast methodology by applying deep convolutional neural networks for learning state evolution that is influenced by non-linear demand dynamics. Furthermore, a case study is presented that applies agent-based simulations and underlines the relevance of deep learning approaches for Smart City early warning systems
The Effects of Heel Lifts on Dynamic Measures of Gait and Static Posture in Individuals with Parkinson’s disease

This study expands upon the Hastings et al (2018) study on the use of heel lifts to change static postural alignment in persons with Parkinson's Disease (PD) and further explores whether the use of heel lifts provide improvements in dynamic gait parameters, Timed Up and Go (TUG) scores, and balance confidence. Outcome measures were taken with and without the heel lifts in place, at two data collection dates. Photographs were analyzed for postural alignment measures using ImageJ software and video was analyzed for gait and turning parameters utilizing the Dite and Temple scale. The Activities-specific Balance Confidence (ABC) scale was collected at baseline and after two weeks of heel lift use, otherwise outcome measures were performed in the reverse order on the first and second session to account for ordering, practice, and fatigue effects. Statistical analysis included paired t-tests for outcome measures and Pearson product correlation between the degree of plantarflexion contracture and ABC scale and overall balance confidence and fall history. We found that head tilt angle and turn time significantly improved (p = .037) (p = .002) with use of heel lifts. Turn time correlated to overall balance confidence (r = -.802, p = .000) and overall balance confidence correlated to fall history (r = -.501, p = .041). This study showed that accommodating a lack of ankle dorsiflexion range of motion with heel lifts significantly improves head tilt angle and decreases turn time as compared to without heel lifts, but has no significant effect on step length, gait speed, or overall TUG score in individuals with PD. Because our work shows that limited range of motion at the ankle contributes to real and perceived balance difficulties healthcare providers working with any population of patients exhibiting balance problems should include this measurement in their assessment and consider intervention with a heel lift when limitations are found.
A Compact Widely Tunable Bandpass Filter Dedicated to Preselectors

This article presents a novel compact widely tunable bandpass filter. The filter consists of two resonators that are double-coupled, inductively, where the coupling inductances are elements of the input and output networks. The application of double-coupling enabled the transmission zero next to the upper cutoff frequency. This makes the filter useful for applications in preselector networks used in receiving systems with a low to intermediate frequency with the desired channel frequency lower than the image channel frequency. The article shows the practical realisation of the varactor-tuned example filter fabricated as a microstrip planar network of an overall size of 0. 03λg × 0. 045λg. The tuning range of the proposed filter is from 410 MHz to 880 MHz with the fractional bandwidth equal to 7.5–8.1% and an in-band insertion loss better than −3.4 dB. The achieved IP3 value exceeds 17.5 dBm.
Sentiment Analysis of Rumor Spread Amid COVID-19: Based on Weibo Text

(1) Background: in early 2020, COVID-19 broke out. Driven by people’s psychology of conformity, panic, group polarization, etc., various rumors appeared and spread wildly, and the Internet became a hotbed of rumors. (2) Methods: the study selected Weibo as the research media, using topic models, time series analysis, sentiment analysis, and Granger causality testing methods to analyze the social media texts related to COVID-19 rumors. (3) Results: in study 1, we obtained 21 topics related to “COVID-19 rumors” and “outbreak rumors” after conducting topic model analysis on Weibo texts; in study 2, we explored the emotional changes of netizens before and after rumor dispelling information was released and found people’s positive emotions first declined and then rose; in study 3, we also explored the emotional changes of netizens before and after the “Wuhan lockdown” event and found positive sentiment of people in non-Wuhan areas increased, while negative sentiment of people in Wuhan increased; in study 4, we studied the relationship between rumor spread and emotional polarity and found negative sentiment and rumor spread was causally interrelated. (4) Conclusion: These findings could help us to intuitively understand the impact of rumors spread on people’s emotions during the COVID-19 pandemic and help the government take measures to reduce panic.