Miracle-dz/UniFAR-FTUs huggingface dataset:
Underlying mechanism for exchange bias in single-molecule magnetic junctions

Magnetic proximity has been observed in a variety of solid-state magnetic devices, but has been less discussed at the molecular scale. In this study, the magnetotransport calculation is carried out using the generalized Landau-Lifshitz-Gilbert (LLG) equation combined with density functional theory (DFT) and our self-developed junpy calculated spin-torque effect. Except for the current driven spin torque, which is a promising approach for magnetization switch in magnetic random access memory, the equilibrium fieldlike spin torque also plays a crucial role in the strain-controlled exchange bias with current-controlled magnetic coercivity in single-molecule magnetic junctions. The tight-binding model is further employed to clarify the critical role of the interfacial spin filter effect arising from the hybridization between the linker and Co apex. These multidisciplinary DFT+junpy+LLG results may provide important and practical implications in the dual control of magnetic proximity and magnetization switching in molecular spintronics at low temperature, either by tensile strain or via smaller applied current density of the order of $\mathrm{MA}/{\mathrm{cm}}^{2}$.
Is CAD A Good Paradigm for MBSE?

Those looking to advocate for Model‐Based Systems Engineering (MBSE) in the Systems Engineering field often turn to more established fields that have made a similar transition to models to assure others it will be beneficial. One practice that is often compared to MBSE is Computer‐Aided Design (CAD) from the field of mechanical engineering. However, the adequacy of this comparison is challenged upon a side‐by‐side examination of what MBSE and CAD are. Based on the established definitions, it is evident that while CAD can be considered a method of mechanical drawing, MBSE cannot be described as only a method. MBSE is more than installing and utilizing software, it is a process in and of itself. Comparing MBSE to CAD runs the risk of oversimplifying MBSE and setting up expectations that may not be met. Therefore, while CAD may represent a similar paradigm shift as MBSE in terms of digitalization, it may not serve as an adequate paradigm in terms of adoption and benefits. In this paper, we characterize and compare CAD and MBSE and identify the similarities and differences between them. We use the resulting insights to level the expectations of adopting and using MBSE.
Preparation of Beef Burger Supplemented with Quinoa Seeds Powder as Healthy Food

This study was conducted to identify the possibility of preparing meat burgers by adding quinoa seeds powder and its quality characteristics. Therefore, the aim of this study was to assess the effects of quinoa seeds powder (QSP) in improving the quality characteristics of raw and cooked beef burgers. The effects of quinoa seeds powder addition (2.5, 5, 7.5, and 10%) on physicochemical properties, cooking parameters, color, texture, and sensory evaluation of cooked burgers were evaluated. Furthermore, via a biological study, the impact of dietary supplementation with quinoaseeds powder (QSP) on hyperlipidemic rats were also, determined. The nutritional parameters, including food intake, weight gain or mass increase, and feed efficiency ratio, were recorded. Additionally, the triglycerides, cholesterol profiles, liver and kidney functions were measured. Theincorporate QSP into the beef burger. The use of QSP in raw and cooked beef burgers resulted in an increment L* and b* and a decrement in a* values at all levels (P<0.05). Texture analysis showed that with the addition of QSP, the hardness values of burgers increased, and adhesiveness values decreased. regarding to the taste; flavor; color, and overall acceptability sensory evaluated, no significant differences (P < 0.05), among the tested fortified burger at all levels of QSP, in relative to the control, were detected. From the obtained results, the rat groups fed on the high-fat diet were significantly suffering from risk of hyperlipidemia. However, the results indicated that all the fortified diets at different levels improved the weight gain and feed consumption, reduced lipid profiles, and improved liver and kidney functions, in relative to positive control group. More specifically, a dietwith 10% QSP reduced the adverse effect of hyperlipidemia. Therefore, these new beef burger formulations might be a viable option for the improvement of nutritional, technological, and sensory properties.
Alternative methods for linear programming problem under triangular fuzzy uncertainty

Abstract In this paper, linear programming problem under triangular fuzzy uncertainty has been taken into consideration. Accordingly, here the coefficient matrix of the constraints and coefficients of the objective functions are assumed as crisp. However, decision variables and the right-hand side vector of the constraints are assumed as uncertain. Two new methods have been developed here using the concept of a fuzzy centre, core and radius for the solution. In first method, coefficients are considered as non-negative in nature, whereas both non-negative and negative coefficients are considered for the second method. Obtained results are compared with Nasseri (2008) and Lone et al. (2017) for validation.
A soil moisture‐based framework for guiding the number and location of soil moisture sensors in agricultural fields

Soil moisture information is a key variable for guiding in‐season management decisions in rainfed and irrigated agricultural systems. However, methods for deciding the number and location of soil moisture sensors (SMS) per field still remain poorly explored in the scientific literature. The goal of this study was to evaluate a quantitative framework based on soil moisture‐based management zones (MZs) to determine the minimum number and tentative deployment location of SMS. Multiple spatially intensive (n > 100 observations) surveys of near‐surface (0–12 cm) soil moisture were conducted during the fallow periods and early growing seasons of 2017, 2018, and 2019 on three agricultural fields using a calibrated handheld soil water reflectometer. The fuzzy C‐means (FCM) clustering method was used to delineate MZs based on the soil moisture surveys, and the silhouette clustering evaluation method was used to identify the optimal number of MZs per field. Then, a sensor location index that considered the distance to the MZ boundaries and the FCM membership grade was developed to identify the tentative optimal deployment location of SMS. The proposed method effectively identified field areas with distinct soil moisture regimes and revealed the complex soil moisture spatial patterns that were not captured with elevation or soil texture alone. Dividing the fields using soil moisture‐based MZ reduced the intrazone soil moisture spatial variability by about 50% compared with that of the entire field. In the three studied fields, a total of two SMS were sufficient to capture the salient soil moisture spatial regimes.
Behavioral Analysis of Digital Banking Acceptance and Customer Satisfaction, in Greece

Greeks seem to be unfamiliar with online banking services, which was an obstacle to the smooth conduct of their transactions, especially during the Covid-19 period. The objective of the study is to reveal the dimensions that influence the use of Digital Banking, including users’ satisfaction and no users’ perceptions in Greece during Covid-19 period, using both econometrical and behavioral analysis approaches. Performing factor analysis, a Structural Equation Model and Multiple Logistic Regressions Models derived that both technological and personal factors, such as, personality and familiarity with banking products could impact the use and maintenance of the use of Digital-Banking in Greece. Besides, technical characteristics of services applications’ such as easiness in login and security influenced users’ satisfaction, while other traditional banking services such as ATM’s and automatic machines’ use, including the behavioral intention to change the way someone is conducting his/her banking transactions, could also be an obstacle in adoption. The particular study examines at the first time not only the perceptions of Greek consumers about electronic banking services, such as Digital-Banking, but also the factors, which will enhance the existing users’ satisfaction, in conjunction with the barriers that lead in non-adoption, including other banking services.
Evaluation of machine learning applications using real-world EHR data for predicting diabetes-related long-term complications

ABSTRACT The biggest concern about diabetes-related complications is that they are unrecognised in the early stages but can be immutable and devastating with time. Identifying the population at high risk of developing such complications can help intervene in preventative care at an early stage. This study aims to present a data-driven approach to predict the patients at higher risk for diabetes-related complications using real-world data. We used comorbid diagnostic features from the electronic health records called “Cerner Health Facts EMR Data” to build machine learning-based prediction models for three diabetes-related long-term complications: (a) eye diseases, (b) kidney diseases, and (c) neuropathy. Our developed pipeline was able to generate highly accurate models for predictions. We deduced from the F1-scores that applying the class balancing techniques improved the overall performance of the models, and SVM with oversampling technique was the most consistent classifier for all three cohorts.
Strong El Niño reduces fruit production of Brazil-nut trees in the eastern Amazon

ABSTRACT The Brazil-nut tree (Bertholletia excelsa) is native to the Amazon rainforest, and its fruit production varies naturally with climatic conditions. Our aim was to evaluate the temporal variation in Brazil-nut production associated with climatic variables, including the strong El Niño of 2015/2016. The study was carried out in two 9-ha permanent plots in the northeastern Brazilian Amazon from 2007 to 2018: one in forest (12-year monitoring) and the other in savannah/forest transition (eight years). Overall, we monitored fruit production of 205 trees with diameter at breast height ≥ 50 cm. Annual fruit production was related to temporal series (2005-2018) of climatic data (the Oceanic Niño Index; and precipitation and air temperature from two local meteorological stations). Average fruit production per tree in 2017 was eight times lower than in 2015 and two times lower than the general average for both sites, and was significantly associated to the El Niño of 2015/2016, that increased average maximum monthly temperature and reduced the precipitation in the region, extending the dry season from three to six months. Years with higher and lower fruit production per tree coincided in both sites. Annual fruit production was significantly and negatively correlated with thermal anomalies that occurred in the third semester prior to harvest monitoring. Years with higher production were related with predominance of neutrality or the La Niña phenomenon at the global scale, and higher rainfall at the local scale. The relationship of fruit production with climate was independent of the local habitat.
MN-PGSOR Method for Solving Nonlinear Systems with Block Two-by-Two Complex Symmetric Jacobian Matrices

For solving the large sparse linear systems with 2 × 2 block structure, the generalized successive overrelaxation (GSOR) iteration method is an efficient iteration method. Based on the GSOR method, the PGSOR method introduces a preconditioned matrix with a new parameter for the coefficient matrix which can enhance the efficiency. To solve the nonlinear systems in which the Jacobian matrices are complex and symmetric with the block two-by-two form, we try to use the PGSOR method as an inner iteration, with the help of the modified Newton method as an efficient outer iteration method. This new method is called the modified Newton-PGSOR (MN-PGSOR) method. Local convergence properties of the MN-PGSOR are analyzed under the Hölder condition. Finally, we give the comparison of our new method with some previous methods in the numerical results. The MN-PGSOR method is superior in both iteration steps and computing time.
Towards a Software Defined Truck

Heavy vehicles are part of the US Critical Infrastructure as they provide the means to deliver goods across the globe. To effectively manage the delivery of goods and improve the performance of the logistics operations, heavy vehicles have been increasingly equipped with wireless telematics systems that bridge the on‐board vehicle networks with Internet based systems. The interconnection of these systems creates a complex system comprising both traditional information technology (IT) systems and automotive operational technology (OT) systems. Using a systems approach to the design, testing, and implementation of complex logistic systems requires a testing and verification strategy to encompass many configurations of vehicles. Using existing vehicles for comprehensive testing is cost prohibitive and building representative testbeds for each configuration of the vehicle is labor intensive. However, testing numerous configurations of heavy vehicles remains a necessity for proper systems evaluation and verification. To this end, a software defined truck (SDT) system is proposed to rapidly test numerous vehicle configurations and achieve testing and verification tasks using automated techniques. This approach drastically expands the coverage of the testing and verification of technologies that leverage vehicle networks. The approach sets the requirements of the software defined truck, explains the advantages of using switched packet networking to virtually reconfigure the truck's network topology, and the appropriate use of simulation engines, emulated hardware, and actual hardware. The result is a scalable, sharable resource where organizations can conduct research, testing, and evaluation at significantly lower cost and wider coverage. This is particularly useful for performing cybersecurity testing, compliance testing, or evaluating other software driven solutions that use the vehicle's controller area network (CAN).
Inductive Processes, Heuristics, and Biases Modulated by High-Reliability Organizing (HRO) for COVID-19 and Disasters

We view and understand the world through our internal logic, both public and private internal logic. The logic of practice differs by the frame of reference – a fixed point or within the flux of events, which can have “cosmology episodes” that collapse sensemaking. We have different perceptions and capabilities from the different reference frames, Eulerian and Lagrangian specificities, Euclidean, and topological spaces. When approaching a situation, all we have is observation, induction, and the capability to learn through action. Because people have limited time and knowledge, they must make inferences from the information they have available. We almost universally use heuristic, subjective approaches for better decision-making for complex, interactive problems and processes. Heuristics work through the nearness of information between the old problem-solution and the new problem, a topological space. In routine operations, we are susceptible to heuristic bias, yet error corrects this heuristic bias counterintuitively. We have found four predominant heuristics that cause consequential bias and interfere with effective decision-making: availability, representativeness, confirmation bias, and over-conservative revision. Motivated reasoning, a fifth bias but not from a heuristic, overly scrutinizes information that conflicts with closely held beliefs. Unless we assume that every word and behavior could instantly be wrong, we can too easily begin treating our treatments.
Admission of Turkey into the European Union: “Does Religion Matter?”

With the rising fear of Islamisation in Europe and increasing Islamist sentiment in Turkey, the issue of religion in the context of integration of Turkey into the European Union remains relevant. After a long-lasted wait and continuous negotiations, Turkey seems to be moving further away from getting accepted in the EU. The research focuses on the religious factor in this unrelenting friction between the two regions / entities and answers the question as to why the EU and Turkey should consider religion as one of the variables in the European integration. The analysis is done with the use of primary and secondary sources with special focus on Samuel Huntingtons realism-inspired work Clash of the Civilization. Turkish sources were used for better understanding the dynamics of Turkey. In terms of methodological approach the research is done with using the content analysis and contemporary observation of socio-political scenario and behavioral changes that the EU and Turkey going through. The authors conclude that there is no proper mechanism to consider religion as a variable, but psychologically it is still playing a major role in both the EU and Turkey. The authors believe that the EU and Turkey should discuss the religious dimension of integration while talking other dynamics.
Deep learning based models for software effort estimation using story points in agile environments

In the era of agile software development methodologies, traditional planning and software eﬀort estimation methods are replaced to meet customer’s satisfaction in agile environments. However, software eﬀort estimation remains a challenge. Although teams have achieved better accuracy in estimating story points eﬀort required to implement user stories or issues, these estimations mostly rely on subjective assessments, leading to inaccuracy and impacting software project delivery. Some researchers are pointing good results by the adoption of deep learning to address this issue. Given the foregoing, this study proposes deep learning-based models for story points estimation in agile projects. Diﬀerent algorithms are proposed and trained over a large dataset for story points estimation made by 16 open-source projects. In addition, we take advantage of natural language processing techniques to excavate better features from the software requirements written as user stories.
Translating heritage: a study of visitors’ experiences mediated through multilingual audio guides in Edinburgh Castle

ABSTRACT Heritage tourism is a vital part of the global economy. To attract more international tourists, many heritage sites provide multilingual interpretation. Previous research suggests that cultural adaptations in translations are essential to international visitors having a positive experience. This article challenges this view based on two theoretical premises: visitors are active participants in engaging with heritage interpretation, and their experience, mediated through verbal interpretation, interacts with other semiotics in the heritage site. This project used the Chinese and English audio guides to Edinburgh Castle as a case study. The methods adopted in this study include textual analysis, post-visit interviews, and the use of a custom smartphone application to track the participants’ locations and use of the guide. The findings suggest that users of audio guides in different languages used different learning strategies to process unknown and familiar information, and foreign visitors, in particular, drew from different semiotic resources to construct their visiting experience. This research suggests that the key to engaging with international visitors is a well-prepared source text, which interacts with a range of multisensory semiotic resources, and facilitates visitor engagement with the physical setting.
Application of Raman Spectroscopy in Biomedical Diagnostics

In vivo cellular imaging and in vitro assays or sensors are fundamentally used to study the spatiotemporal interaction of molecules at biological interfaces. The study of these interfaces informs various applications such as diagnostics/detection of foreign materials or processes in the biological system. Raman spectroscopy, an optical, non-destructive, label-free fingerprinting tool offers a wide array of applications in both in vitro and in vivo diagnostics owing to its relatively short acquisition time, non-invasiveness and ability to provide biochemical molecular information. It has been explored in tissue imaging, in vitro diagnosis, DNA/RNA analysis, metabolic accretions, single cell analysis photodynamic therapy, etc. The chapter details the application of the optical Raman platform in the detection and imaging of diseases/tissues. The challenges associated with SERS applications and the future outlook as a biomedical diagnostic tool are also discussed.
Flipped Classrooms in Undergraduate Statistics: Online Works Just Fine

Background The flipped classroom method requires that students engage with homework before coming to the classroom so that class time can be spent on active and collaborative learning exercises. Research has demonstrated that this can improve student performance versus traditional lecturer-led teaching methods. Objective During the COVID-19 pandemic, the vast majority of teaching has been entirely online such that even ‘in-class’ time has been virtual. The current article examined whether online-only delivery affects the efficacy of the flipped classroom approach. Method Grades for a research methods and statistics module and a statistics portfolio assignment were compared across consecutive cohorts of undergraduate psychology students taught by different methods. Results Overall grades on the module did not differ significantly across teaching methods but student performance on statistics tests did. Flipped classrooms, whether accompanied by on-campus or synchronous online classes, led to significantly better performance than traditional methods. No detriment was observed by teaching entirely online. Conclusion The key advantages of the flipped classroom method appear driven by active learning which can occur irrespective of classroom context. Teaching Implications Using flipped classrooms can be a useful tool, particularly in subjects where students may otherwise be less engaged with the content.
Ambulatory Heart Rate Variability Monitoring: Comparisons Between the Empatica E4 Wristband and Holter Electrocardiogram

ABSTRACT Objective Heart rate variability (HRV) is a useful index of psychological and physiological stress. Although several wristband devices have purported to measure HRV, none have demonstrated reliability when compared with the criterion-standard Holter monitor. We evaluated the reliability of HRV readings from the Empatica E4 wristband compared with a Holter monitor over a 24-hour period of simultaneous monitoring. Methods Agreement between the monitors was assessed by examining correlations and intraclass correlations (ICCs) for fixed sets in 13 individuals in a treatment trial for posttraumatic stress disorder (4 women; mean [standard deviation] age = 51.92 [6.17] years). Agreement was calculated at 1-second and 5-minute intervals for interbeat intervals (IBIs) and for 5-minute intervals of the root mean square of successive differences between normal heartbeats (RMSSD) and standard deviation of all normal R-R intervals (SDNN). Agreement across the entire 24-hour observation period was also measured. Frequency-domain measures of HRV could not be calculated because of too much missing data from the E4. Results Although high interdevice correlations and ICCs were observed between the E4 and Holter monitors for IBIs at 1-second (median r = 0.88; median ICC = 0.87) and 5-minute (median r = 0.94; median ICC = 0.94) intervals, reliabilities for 5-minute RMSSD (median r = −0.09; median ICC = −0.05) and 5-minute SDNN (median r = 0.48; median ICC = 0.47) were poor. Agreement between the devices on 24-hour measures of HRV was satisfactory (IBI: r = 0.97, ICC = 0.97; RMSSD: r = 0.77, IBI = 0.76; SDNN: r = 0.92, IBI = 0.89). Conclusions Findings suggest that the low reliability of Empatica E4 as compared with the Holter monitor does not justify its use in ambulatory research for the measurement of HRV over time periods of 5 minutes or less.