Miracle-dz/UniFAR-FTUs huggingface dataset:
A general planar waveguide autoregressive optical filter

A general architecture for an autoregressive (AR) planar waveguide optical filter is demonstrated for the first time. Its advantages are a flatter passband, sharper rolloff and better rejection in the stopband compared to finite impulse response (FIR) filters with the same number of stages. The architecture can be extended to an arbitrary number of stages. A modified Levinson algorithm is derived for filter synthesis and analysis which includes waveguide loss and phase errors between stages. The filter analysis algorithm allows the filter's coupling ratios and phase errors for each stage to be determined from the filter's spectral response. When combined with a postfabrication tuning process, this analysis method provides feedback for optimizing the response. Autoregressive lattice filters were designed and fabricated using Ge-doped silica waveguides. Measurements are reported which demonstrate the synthesis and analysis algorithms. The impact of fabrication tolerances on filter synthesis and of measurement uncertainties on filter analysis are investigated.
Winter Roosting by Eastern Red Bats in Ozark Mountain Forests of Missouri

The eastern red bat (Lasiurus borealis Müller, 1776) is a widespread species that roosts in evergreen or dead foliage suspended in trees during winter but retreats to leaf litter during colder periods. Roosting in leaf litter by eastern red bats makes them vulnerable to prescribed fires in winter. Using radio telemetry, we tracked 33 male eastern red bats to 101 winter (November–February) roosts and quantified roost locations, habitat surrounding roosts, and landscape attributes of roost locations. When roosting in trees, bats preferred oaks but generally avoided other tree species; they used pines in proportion to their availability. During colder periods, bats retreated to roosts in leaf litter where 21% suffered mortality either from predation/scavenging or unknown causes while roosting on the ground. Models of roost selection indicated that southerly aspect was the most important factor determining roost selection, and both tree- and leaf-litter roosts were predominately (≥94%) on upper south-facing slopes. Prescribed burning in late morning/early afternoon on clear days when temperatures under leaf litter are warmest in winter could reduce potential mortality by allowing faster arousal time for hibernating bats.
Sequence Analysis in Routine Dynamics

Implicitly or explicitly, sequence analysis is at the heart of research on routine dynamics. Sequence analysis takes many forms in many different disciplines, because sequence is central to temporality, process, language, and narrative. In this chapter, we focus on sequence analysis in routine dynamics research. The goal of this chapter is to help researchers use sequence analysis in their research on routine dynamics. Hence, the chapter reviews prior literature that has used sequence analysis, it shows how to carry out sequence analysis and it provides implications as well as an agenda for future research.
Outcomes of 270° Labral Repair for Combined Shoulder Instability in Active-Duty Military Patients: A Retrospective Study

Background: There is a high prevalence of combined shoulder instability in military patients. Short-term outcomes after 270° labral repair are promising; however, there is a paucity of longer term outcome data in this high-demand group of patients. Purpose: To report the midterm outcomes of active-duty military patients treated with 270° labral repair for combined shoulder instability. Study Design: Case series; Level of evidence, 4. Methods: All consecutive patients between January 2011 and January 2019 who underwent 270° labral repair by the senior surgeon with complete outcome scores were identified. All patients had experienced a shoulder dislocation after a traumatic event and had magnetic resonance imaging and intraoperative findings consistent with combined-type instability. A total of 52 patients met the inclusion criteria for the study, and all were active-duty servicemembers at the time of surgery. Results: The mean follow-up was 78.21 months (range, 24-117 months). There was a statistically significant increase in the mean American Shoulder and Elbow Surgeons score (from 44.92 to 89.31; P < .0001), Single Assessment Numeric Evaluation score (from 52.32 to 93.17; P < .0001), and Rowe instability score (from 46.63 to 91.35; P < .0001) from preoperatively to postoperatively. Mean pain decreased significantly as measured by the visual analog scale for pain (from 8.04 to 1.44; P < .0001). Range of motion in forward flexion (from 155.29° to 155.96°; P = .6793), external rotation (from 67.50° to 65.29°; P = .0623), and internal rotation (from T9.58 to T9.56; P = .9650) did not change significantly postoperatively. Outcomes did not differ significantly for patients who underwent surgery on their dominant shoulder versus those who underwent surgery on their nondominant shoulder, nor did outcomes vary with the type of anchor utilized (biocomposite vs all-suture). The overall rate of return to active duty was 92.31%. Conclusion: Midterm outcomes in this population of active-duty patients undergoing 270° labral repair for combined shoulder instability demonstrated a statistically and clinically significant improvement in patient-reported outcome scores, a significant decrease in pain, and an overall rate of return to active duty of 92.31%.
Issues Related to Use of Contractor Quality Control Data in Acceptance Decision and Payment: Benefits and Pitfalls

Several agencies throughout the United States use contractor data as a means of acceptance. This is a permissible practice as long as certain safeguards are in place and as long as the functions of quality control and quality acceptance remain separate. The benefit of this type of procedure is potential decreases in the personnel and testing facilities required by the state agency. However, this type of approach for acceptance also has inherent risks. In most cases, the changes required to implement a system in which contractor data are used in the acceptance decision are more philosophical than technical. In other words, the amount of testing that the contractor will have to do to meet the requirements of the change will more than likely be about the same as (or only slightly more than) that undertaken in the current specification system. However, even though a state agency may see a drop in the amount of testing required to support acceptance, it will now be faced with the fact that it must trust the contractor’s data for determination of pay factors and, ultimately, acceptance of the work. Thus, when the procedure of contractor testing used for acceptance is fully implemented, there will be two major impacts on a state agency. One is a psychological adjustment for agency personnel to assimilate the fact that contractor test results will be used to establish the pay factor. Discussion is needed to address this adjustment, and the state should plan training sessions to address the reasons for this decision and the importance of the steps that will be taken to implement it. The other impact will be the need to implement and monitor the validation system.
Data processing and image reconstruction methods for the HEAD PENN-PET scanner

Methods of reconstruction and quantitation are developed for a 3D system and are evaluated on the septa-less HEAD PENN-PET scanner, which has a very large axial acceptance angle (/spl theta//sub max/=/spl plusmn/28/spl deg/ in the center) and large axial field-of-view of 256 mm. To overcome the difficulties of data storage and reconstruction time with 3D reconstruction, the authors have reduced the size of the 4-D projection matrix required for 3D-RP reconstruction, and compared the results to the Fourier rebinning (FORE) algorithm. Both approaches achieve a favorable tradeoff in data storage requirements, reconstruction time, and accuracy that are suitable for clinical use. The authors have also studied the application of the FORE algorithm to transmission scans acquired with a single point source (/sup 137/Cs) so that data quantitation can be performed.
Adaptive Security Modules in Incrementally Deployed Sensor Networks

Abstract Distributed wireless sensor networks often suffer problems on detecting malicious nodes, which always bring destructive threats. Thus, sensor networks have to supply authentication services for sensor identity and data communication. As matter of fact, intrusion detection and prevention schemes are always integrated in sensor security appliances so that they can enhance network security by discovering malicious or compromised nodes. This paper provides adaptive security modules to improve secure communication in distributed sensor networks. The primary security module provides online identity authentication services to new incoming sensor nodes which being distributed after initial deployment. The advanced security module addresses compromised node detection issues to exclude internal compromised nodes. The proposed schemes can accomplish secure communications in the sensor networks when the network lifetime is divided into multiple time intervals. The network security and network performance are evaluated with the adaptive security modules, which shows efficient protection and sensible overheads to sensor nodes can be achieved.
Active Use of DFIG-Based Variable-Speed Wind-Turbine for Voltage Control in Power System Operation

This paper presents an active use of doubly-fed induction-generator (DFIG)-based variable-speed wind-turbine for voltage control in power system operation. For reasonable simulation studies, a detail dynamic model of a DFIG-based wind-turbine grid-connected system is presented. For the research objective, an innovative reactive power control scheme is proposed that manipulates dynamically the reactive power from the voltage source converter (VSC) with taking into account its operating state and limits.
Reverse Genetic Screen for Deleterious Recessive Variants in the Local Simmental Cattle Population of Switzerland

Simple Summary Today’s Swiss Simmental represents a local dual-purpose breed of cattle. Within closed populations, deleterious variants can reach problematic frequencies, explaining substantial proportions of inbreeding depression. Depletions in homozygous genotypes for certain haplotypes among large cohorts of animals genotyped for the purpose of genomic selection is a widely used approach to pinpoint undesired recessive alleles. In the course of a reverse genetic screen, we aimed to identify single recessive Mendelian variants that potentially affect fertility and rearing success without any phenotypic information available. We detected eleven genome regions showing obvious depletion of homozygosity based on genome-wide SNP data. Furthermore, after performing whole-genome sequencing of selected animals, we propose three candidate causative variants affecting different genes with possibly detrimental effects for embryonic development. The established haplotypes, as well as the identified protein-changing variants, can be directly implemented into breeding practice to avoid the risk of mating carriers and thereby increase breeding success. Abstract We herein report the result of a large-scale reverse genetic screen in the Swiss Simmental population, a local dual-purpose cattle breed. We aimed to detect possible recessively inherited variants affecting protein-coding genes, as such deleterious variants can impair fertility and rearing success significantly. We used 115,000 phased SNP data of almost 10 thousand cattle with pedigree data. This revealed evidence for 11 genomic regions of 1.17 Mb on average, with haplotypes (SH1 to SH11) showing a significant depletion in homozygosity and an allele frequency between 3.2 and 10.6%. For the proposed haplotypes, it was unfortunately not possible to evaluate associations with fertility traits as no corresponding data were available. For each haplotype region, possible candidate genes were listed based on their known function in development and disease. Subsequent mining of single-nucleotide variants and short indels in the genomes of 23 sequenced haplotype carriers allowed us to identify three perfectly linked candidate causative protein-changing variants: a SH5-related DIS3:p.Ile678fs loss-of-function variant, a SH8-related CYP2B6:p.Ile313Asn missense variant, and a SH9-related NUBPL:p.Ser143Tyr missense variant. None of these variants occurred in homozygous state in any of more than 5200 sequenced cattle of various breeds. Selection against these alleles in order to reduce reproductive failure and animal loss is recommended.
Subjective versus objective captured Social Networks: Comparing standard self- report questionnaire data with observational RFID technology data

Social network analysis provides insights into human social phenomena. Commonly, social network data is captured via surveys or newly via technical devices. In the described study, we combine subjective survey data with objective data from RFID tags in order to completely picture the closed network of an entire freshman students’ cohort. RFID tags are small proximity sensing devices using radio frequency identification technology and can be worn as badges. Results indicate that the information of the two different data sources is similar but not entirely equal. Rather, they picture unique but complementing information and both should be considered in social network research.
Optimization of Liquid Desiccant Dehumidifier Performance Using Taguchi Method

An attempt has been made to improve the performance of dehumidifier using liquid desiccant in this study. Experiments have been conducted based on L16 orthogonal array in order to experimentally investigate the influence of dehumidification process parameters like desiccant temperature, desiccant concentration, desiccant flow rate, air flow rate, and relative humidity on the effectiveness of dehumidifier. The optimum dehumidification condition has been found through Taguchi method. It has been confirmed that the optimum condition exhibited better effectiveness for the dehumidification process.
Digital Innovation: Orchestrating Network Activities

Digitization of analogue everyday artifacts, i.e. when physical products are equipped with digital capabilities, has a profound impact on today’s society. Some examples of these digital innovations aimed at consumer markets are the “connected” car, the digitized television set, and in the near future, digitized IKEA furniture. Digital innovation provides endless opportunities for providing value adding products and services. However, in digital innovation there is a need to find new ways of organizing network activities, i.e. activities such as e.g. production and translation of knowledge and enrollment of actors. These activities need to embrace and build on the networked aspects and the complexity inherent to digital innovation. This requires network activities that can overcome challenges with the ambiguous and messy characteristics of digital innovation. In this thesis, I propose that the theoretical perspective of network orchestration can enlighten fruitful ways to address challenges that are encountered when organizing network activities in digital innovation. Inspired by practical challenges with digital innovation, as well as contemporary calls for research within IS, this thesis investigates: How can network activities be orchestrated in digital innovation? Two cases of digital innovation aimed at consumer markets are studied. The first case concerns the digitization of the newspaper. The second case regards the digitization of door locks. Literature about digital innovation is used to understand the context of the studied phenomenon. Furthermore, theories about network orchestration as well as activities in innovation are used as a theoretical framework to help answer the research question. The thesis is based on an interpretative perspective where a multi-method approach has been applied to address the research question. The contribution is divided into two different parts. The first part presents four categories of empirically derived network activities that address socio-technical challenges with organizing digital innovation. The second part is a proposed model detailing orchestration of network activities in digital innovation. The model is based around the four suggested categories of network activities: (1) Supporting flexible innovation networks, (2) Production and translation of layered architectural knowledge, (3) Addressing heterogeneous user communities, and (4) Harnessing generativity to leverage value. The categories of network activities can be viewed as building blocks for the orchestration process. By emphasizing both a proactive and a reactive way of orchestrating digital innovation, the model proposes a means for organizations to address the ambiguity and complexity of digital innovation.
The Effects of Brand Community Identification on Consumer Behavior in Online Brand Communities

Abstract Today, companies must create brand community identification on social media in order to transition from traditional marketing to online marketing. This transition can increase advertising in online platforms, the customers’ trust in the brand and, ultimately, their refusal to buy and use rival brands in competitive online environments. This study investigates the effects of brand community identification on consumer behavior of online brand communities which consists of social media community users. The sample size includes 245 observations (Facebook, Instagram, and other social networks users) in Iran. After an in-depth review of the relevant literature, a conceptual model was developed and the proposed hypotheses were tested using Structural Equation Modeling technique. Results indicate that brand community identification in online brand communities has important and different effects on customer behavior, so companies could use it to support their online marketing strategies. Ultimately, companies would be able to create a unique sense in customers who avoid buying and using competitor brands.
Comparing the Forecast Performance of Advanced Statistical and Machine Learning Techniques Using Huge Big Data: Evidence from Monte Carlo Experiments

This research compares factor models based on principal component analysis (PCA) and partial least squares (PLS) with Autometrics, elastic smoothly clipped absolute deviation (E-SCAD), and minimax concave penalty (MCP) under different simulated schemes like multicollinearity, heteroscedasticity, and autocorrelation. The comparison is made with varying sample size and covariates. We found that in the presence of low and moderate multicollinearity, MCP often produces superior forecasts in contrast to small sample case, whereas E-SCAD remains better. In the case of high multicollinearity, the PLS-based factor model remained dominant, but asymptotically the prediction accuracy of E-SCAD significantly enhances compared to other methods. Under heteroscedasticity, MCP performs very well and most of the time beats the rival methods. In some circumstances under large samples, Autometrics provides a similar forecast as MCP. In the presence of low and moderate autocorrelation, MCP shows outstanding forecasting performance except for the small sample case, whereas E-SCAD produces a remarkable forecast. In the case of extreme autocorrelation, E-SCAD outperforms the rival techniques under both the small and medium samples, but further augmentation in sample size enables MCP forecast more accurate comparatively. To compare the predictive ability of all methods, we split the data into two halves (i.e., data over 1973–2007 as training data and data over 2008–2020 as testing data). Based on the root mean square error and mean absolute error, the PLS-based factor model outperforms the competitor models in terms of forecasting performance.
The Efficacy of Democracy and Freedom in Fostering Economic Growth

The relationship between democracy, freedom, and economic development has a multidisciplinary and diverse literature without a settled or concrete answer. Studies that attempt to validate this relationship have a cacophony of diametrically opposed narratives with a degree of dissonance that does not always articulate a complete understanding of the issue. This study attempts to contribute to the existing complex and ever-changing literature by empirically estimating whether democracy, personal and economic freedom affect economic growth. This paper empirically investigates 115 countries over the period 2006–2018 using panel regressions and found that the quality of democracy was significant in explaining the per capita income growth dynamics of a country. Economic freedom also has a statistically significant and positive relationship with per capita income progression. Personal freedom, on the other hand, is detrimental to growth in countries that have low levels of freedom but acts as a catalyst for growth when a society has reached a mature level of personal freedom. Other factors related to development entrapment such as income inequality, unemployment, and urbanization also have a significant impact on economic progression. In line with historical consensus, countries institutionally insulated from democracy fare worse economically than their democratic counterparts.