Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Efficient Multi-Factor Authentication Approaches for Smart Door Lock Systems

In a smart environment smart door lock is very important because previously they carried the traditional methods like carrying keys etc. after that they developed a biometric method for security for the users in different sectors because they are unique characters and that are also involved in hacking and using less quality component . and It is most important in present environment because the technology is very fast and they are not carrying key because if they lose the key it is hard to break the lock and it give high security for safety purpose because if we add smart door lock your home means it gives notification to your phone directly open or lock the door if any person came in front of the door if you are in another place it is easy to allow the person . there are so many methods used for smart door lock system but even though there are still having the challenges for security and photography. This summarized all the existing methods for solving the problem using iris recognition. By observing all the existing solutions Iris gives high security compared to other biometric.
Prompt-NER: Zero-shot Named Entity Recognition in Astronomy Literature via Large Language Models

This study delves into the application of Large Language Models (LLMs) for Named Entity Recognition (NER) tasks in the field of astronomy literature. To enhance the zero-shot recognition capabilities of LLMs for astronomical named entities, we propose a strategy called Prompt-NER. Prompt-NER includes five prompt elements: Task Descriptions, Entity Definitions, Task Emphasis, Task Examples, and Second Conversation. To assess the e ff ectiveness of the Prompt-NER strategy, we utilize three representative LLMs (Claude-2, GPT-3.5, and LLaMA-2-70b) to identify telescope and celestial object named entities in astronomical literature. Our experiments are conducted based on two distinct datasets. The first dataset comprises 30 original PDF documents, which we split into paragraphs in sequential order, resulting in a second dataset consisting of 30 paragraph collections. Additionally, we incorporate 30 astronomical telegrams to diversify our experiments and assess the performance of LLMs based on Prompt-NER on concise, complete texts. Our experimental results indicate that the Prompt-NER strategy enables LLMs to e ff ectively accomplish NER tasks in the field of astronomy, even without prior astronomical knowledge during training. We carefully analyze the experimental results, including the mechanism of di ff erent prompt elements and the influence of di ff erent features of long and short texts on their respective experimental results. This research provides experience for zero-shot NER tasks in astronomical literature and suggests future work in this area.
Postmenopausal endogenous oestrogens and risk of endometrial cancer: results of a prospective study

We assessed the association of postmenopausal serum levels of oestrogens and sex hormone-binding globulin (SHBG) with endometrial cancer risk in a case–control study nested within the NYU Women’s Health Study cohort. Among 7054 women postmenopausal at enrolment, 57 cases of endometrial cancer were diagnosed a median of 5.5 years after blood donation. Each case was compared to 4 controls matched on age, menopausal status at enrolment, and serum storage duration. Endometrial cancer risk increased with higher levels of oestradiol (odds ratio = 2.4 in highest vs lowest tertile, P for trend = 0.02), percent free oestradiol (OR = 3.5, P< 0.001), and oestrone (OR = 3.9, P< 0.001). Risk decreased with higher levels of percent SHBG-bound oestradiol (OR = 0.43, P = 0.03) and SHBG (OR = 0.39, P = 0.01). Trends remained in the same directions after adjusting for height and body mass index. A positive association of body mass index with risk was substantially reduced after adjusting for oestrone level. Our results indicate that risk of endometrial cancer increases with increasing postmenopausal oestrogen levels but do not provide strong support for a role of body mass index independent of its effect on oestrogen levels. © 2001 Cancer Research Campaign
Generative AI in Introductory Programming

Generative AI tools based on Large Language Models (LLMs) such as OpenAI’s ChatGPT, and IDEs powered by them such as GitHub Copilot, have demonstrated impressive performance in myriad types of programming tasks. They can often produce syntactically and logically correct code from natural language prompts that rival the performance of high-performing introductory programming students – an ability that has already been shown to extend beyond introductory programming. However, their impact in the classroom goes beyond producing code. For example, they could help level the playing field between students with and without prior experience. Generative AI has been shown to be proficient in not only explaining programming error messages but in repairing broken code, and pair programming might evolve from two students working together into “me and my AI”. On the other hand they could have negative effects. Students could become over-reliant on them and they may open up new divides due to different backgrounds, experience levels and access issues. From the educator perspective, Generative AI has been successful in generating novel exercises and examples including providing correct solutions and functioning test cases. They can be used to assess student work, provide feedback, and have the potential to act as always-available virtual teaching assistants, easing the burden not only on the educator but on their assistants and the broader educational systems where learning takes place. They could even affect student intakes given their prominence in the media and the effect that such forces can have on who chooses to – and who chooses not to – study computing. Given that Generative AI has the potential to reshape introductory programming, it is possible that it will impact the entire computing curriculum, affecting what is taught, when it is taught, how it is taught
Pattern Recognition

Bottom-up methods and general Bayesian framework for saliency detection commonly suffer from two drawbacks. First, they are sensitive to background noise, thus background regions similar to objects are also highlighted. Second, they only consider appearance features and thus object with several different parts will not be highlighted uniformly. In this paper, we propose a novel and uniﬁed geodesic weighted Bayesian model which considers spatial relationship by reformulating Bayes’ formula. First, we infer a more precise initial salient regions via fully connected CRF model. Second, to highlight the whole object uniformly, we learn a robust measure of region similarity which describes the probability of two regions belonging to the same object, so regions belonging to the same object will be given similar saliency value. Third, using our learnt region similarity as edge weight, we construct an undirected weighted graph to compute geodesic distance of regions. Regions with short geodesic distance from initial salient regions will be attached more importance, thus suppressing background noise. By using results of existing methods as prior distribution, our model can integrate into all methods and improve their performance. Experiments on benchmark datasets demonstrate that our model can signiﬁcantly improve the quality of saliency detection.
Workplace harassment and burnout syndrome among health personnel at a referral hospital

Objective: To determine the association between workplace harassment and burnout syndrome among healthcare personnel of a Peruvian referral hospital. Materials and methods: An analytical cross-sectional study which included internists, surgeons, nurses, residents, interns and nursing technicians from the Medicine and Surgery departments of Hospital Nacional Hipólito Unanue in Lima, Peru. The Maslach Burnout Inventory–Human Services Survey (MBI–HSS) and the Negative Acts Questionnaire–Revised (NAQ–R) were used for the assessment of burnout and harassment, respectively. Additionally, the association with age, gender, occupation, marital status, workplace, salary satisfaction, family burden, physical activity, harmful habits, vacations, religious affiliation, number of jobs, monthly remuneration, job tenure, working hours per week, number of patients seen per day, weekly shifts and rest hours was evaluated. A multivariate analysis was conducted using a multiple logistic regression model and the presence or absence of burnout as an outcome variable. Results: The study consisted of 206 participants, out of whom 22 (10.7 %) suffered burnout and 27 (14 %) moderate to severe harassment. In the bivariate analysis, age ( OR 0.94; 95 % CI 0.89–0.99; p = 0.02), marital status such as married and cohabiting ( OR 2.85; 95 % CI 1.01–8.06; p = 0.04) and harassment ( OR 5.20; 95 % CI 1.92–14.09; p = 0.009) were associated with burnout. In the multivariate analysis, the only significant predictor of burnout was workplace harassment. Moderate to severe harassment was associated with OR 4.00 (95 % CI 1.4–11.3; p = 0.009) compared to mild harassment. Conclusions: It is important to identify health workers suffering workplace harassment due to its strong association with burnout syndrome. It is essential to carry out further research to understand and address the problem of workplace harassment and its influence on the development of burnout, as well as studies to evaluate interventions aimed at preventing both workplace harassment and burnout.
A 10.5 ppm/ ◦ C Modified Sub-1 V Bandgap in 28 nm CMOS Technology with Only Two Operating Points

: Reference voltage/current generation is essential to the Analog circuit design. There have been several ways to generate quality reference voltage using bandgap reference (BGR) and there are mainly two types: current mode and voltage mode. The current-mode bandgap reference (CBGR) is widely accepted in industry due to having an output voltage which is below 1 V. However, its drawbacks include a lack of proportional to absolute temperature (PTAT) current availability, a large silicon area, multiple operating points, and a large temperature coefficient (TC). In this paper, various operating points are explained in detail with diagrams. Similar to the conventional voltage mode bandgap reference (VBGR) circuits, modifications of the existing circuits with only two operating points have also been proposed. Moreover, the proposed BGR occupies a much smaller area due to eliminating the complimentary to absolute temperature (CTAT) current-generating resistor. A new self-biased opamp was introduced to operate from a 1.05 V supply, reducing systematic offset and TC of the BGR. The proposed solution has been implemented in 28 nm CMOS TSMC technology, and extraction simulations were performed to prove the robustness of the proposed circuit. The targeted mean BGR output is 500 mV, and across the industrial temperature range ( − 40 to 125 ◦ C), the simulated TC is approximately 10.5 ppm/ ◦ C. The integrated output noise within the observable frequency band is 19.6 µ V (rms). A 200-point Monte Carlo simulation displays a histogram with a 2.6 mV accuracy of 1.2% ( ± 3-sigma). The proposed BGR circuit consumes 32.8 µ W of power from a 1.05 V supply in a fast process and hot (125 ◦ C) corner. It occupies a silicon area of 81 × 42 µ m (including capacitors). This design can aim for use in biomedical and sensor applications.
SMU Data Science Review SMU Data Science Review

A chasm exists between the active public equity investment management industry's fundamental, momentum, and quantitative styles. In this study, the researchers explore ways to bridge this gap by leveraging domain knowledge, fundamental analysis, momentum, crowdsourcing, and data science methods. This research also seeks to test the developed tools and strategies during the volatile time period of 2020 and 2021.
Preventive Veterinary

The presentation of sick and unthrifty pigs for slaughter at abattoirs, farmers’ inadvertent role, an infected neighbourhood, a pig to pig contact, rodents and wild birds may contribute to infections of farms, whereas washing, disinfection of tools, food and water control, and separation of sick pigs reduces the likelihood of infections. Underlying reasons for these observations
Conditions Influencing Hurricane Emily's (2005) Precipitation Patterns, Convection and Upper Tropospheric Outflow

Hurricane Emily (2005) became the earliestforming category five hurricane on record in the Atlantic basin on 17 July 2005, becoming another standout storm in the record breaking hurricane season of 2005. The storm was monitored by the Tropical Cloud Systems and Processes (TCSP) field campaign that was conducted from July 1 – 27, 2005. Through the use of the ER-2 instruments, AMPR and EDOP, and GOES-11 infrared imagery, this study will analyze the eyewall structure during the morning hours of 17 July. Precipitation patterns will be determined using TRMM TMI, AMSR-E, and SSM/I and correlated with environment factors to determine the reasons behind the witnessed patterns. The upper-tropospheric outflow will be examined using GOES-11 satellite derived winds from CIMSS at the University of Wisconsin – Madison. A hypothesis will be examined linking the relationship between Inertial Available Kinetic Energy (IAKE) and convection in the inner radii of the storm. The primary purpose of this study is to better understand the relationship between tropical cyclone convection and the environmental effects impacting the system.
A Diffusion Tensor Imaging Study on the Auditory System and Tinnitus

Tinnitus is an auditory percept in the absence of an external sound source. Mechanisms in the central nervous system are believed to be key in the pathophysiology of tinnitus. Diffusion tensor imaging (DTI) is an MR imaging technique that allows in vivo exploration of white matter tissue in the human brain. Using a probabilistic DTI approach, we determined the characteristics of fiber tracts from the inferior colliculus to the medial geniculate body up to the primary auditory cortex. We also investigated the connections between the auditory system and the amygdala, which may be involved in some forms of tinnitus. White matter tracts were characterized by three quantities: the mean fractional anisotropy, the weighted mean fractional anisotropy and the path strength. All these quantities are measures of the patency of white matter tracts. The most important finding is an increased patency of the white matter tracts between the auditory cortex and the amygdala in tinnitus patients as compared to healthy controls.
Synthesis and Characterization of Polyethylene Glycol Mediated Silver Nanoparticles by the Green Method

The roles of green chemistry in nanotechnology and nanoscience fields are very significant in the synthesis of diverse nanomaterials. Herein, we report a green chemistry method for synthesized colloidal silver nanoparticles (Ag NPs) in polymeric media. The colloidal Ag NPs were synthesized in an aqueous solution using silver nitrate, polyethylene glycol (PEG), and β-D-glucose as a silver precursor, stabilizer, and reducing agent, respectively. The properties of synthesized colloidal Ag NPs were studied at different reaction times. The ultraviolet-visible spectra were in excellent agreement with the obtained nanostructure studies performed by transmission electron microscopy (TEM) and their size distributions. The Ag NPs were characterized by utilizing X-ray diffraction (XRD), zeta potential measurements and Fourier transform infrared (FT-IR). The use of green chemistry reagents, such as glucose, provides green and economic features to this work.
Isolation and Screening of Lovastatin Producing Fungi: Fusarium pseudocircinatum IBRL B3-4 as a Potential Producer

In the present study, 17 fungal isolates were isolated from various soils and substrate samples. However, only 11 identified isolates were found to be lovastatin producers. Lovastatin is known as a competitive inhibitor of 3-hydroxy-3-methylglutaryl-CoA reductase (HMG-CoA reductase), a rate-limiting enzyme in the cholesterol biosynthesis. The lovastatin production was carried out by solid substrate fermentation technique using a combination of rice bran and brown rice as substrate. The production was primarily determined using thin layer chromatography (TLC) and all the positive results were evaluated by confirmatory HPLC. The results revealed that Fusarium sp. IBRL B3-4 produced the highest lactone type lovastatin with 281.7±44.4 mg/g dry solid of lovastatin after seven days incubation period. The initial profile of the lovastatin production by Fusarium sp. IBRL B3-4 in solid substrate fermentation using a combination of rice bran and brown rice (1:1 ratio) showed the highest lovastatin production of about 425.0±33.3 mg/g dry solid of lovastatin was achieved on day twelfth of cultivation with 2.8±0.1 mg/ g fungal growth. The lovastatin and fungal growth production decreased after achieving its maximal production. Fusarium sp. IBRL B3-4 was identified as Fusarium pseudocircinatum IBRL B3-4 by using macroscopic, in situ microscopic and 18S rRNA molecular approach.
Digital Commons@Becker Digital Commons

Background: Diabetes is prevalent among people with CF (PwCF) and associated with worse clinical outcomes. CFTR modulators are highly effective in improving the disease course of CF. However, the effects of elexacaftor/ tezacaftor/ivacaftor (ETI) on glucose metabolism in PwCF are unclear. Methods: Twenty youth and adults with CF underwent frequently sampled oral glucose tolerance tests (fsOGTT) before and after ETI initiation. Glucose, insulin, and C-peptide were collected at 0, 10, 30, 60, 90, and 120 min after 1.75 g/kg (max 75 g) of dextrose. HbA1c and continuous glucose monitoring (CGM) were collected in a subset. Estimates of insulin secretion (C-peptide index), insulin resistance (HOMA2 IR and IS(OGTT Cpep)), and β -cell function (C-peptide oral disposition index, oDI coeo ), were compared before and after ETI. Results: Participants were a median (IQR) of 20.4 (14.1, 28.6) years old, 75 % male. Follow-up occurred 10.5 (10.0, 12.3) months after ETI initiation. BMI z-score increased from 0.3 (-0.3, 0.8) to 0.8 (0.4, 1.5), p = 0.013 between visits. No significant differences were observed in glucose tolerance, glucose area under the curve, nor fsOGTT glucose concentrations before and after ETI. Median (IQR) C-peptide index increased from 5.7 (4.1, 8.3) to 8.8 (5.5, 10.8) p = 0.013 and HOMA2 IR increased (p < 0.001), while oDI coeo was unchanged (p = 0.67). HbA1c decreased from 5.5 % (5.5, 5.8) to 5.4 % (5.2, 5.6) (p = 0.003) while CGM variables did not change. Conclusions: BMI z-score and measures of both insulin resistance and insulin secretion increased within the first year of ETI initiation. β -cell function adjusted for insulin sensitivity (oDI coeo ) did not change.
Designing and modeling of a new MSMA vibration energy transducer

The conversion of mechanical vibrational energy into electrical energy to power wireless electronic devices using the smart material magnetic shape memory alloy (MSMA) has garnered substantial attention. This paper presents a vibration energy transducer founded on the inverse effect of MSMA, elucidating the principle of power generation. The variations in martensite and magnetic domain characteristics within MSMA were analyzed, and a constitutive model was established for the MSMA vibration energy transducer, integrating the thermodynamic theory of the Gibbs free energy function. Although this model performs well in predicting experimental outcomes, it falls short in capturing all features of the experimental data. To comprehensively encompass these features, the PSO-XGBoost machine learning approach was introduced to train the experimental data by incorporating factors such as stress, magnetic field, and induced voltage. An experimental prototype of the MSMA vibration energy transducer is fabricated, and the predictions of both models are compared with the collected experimental data, validating the accuracy of the model and indicating the enhanced effectiveness of machine learning methods in prediction. This research not only validates the correctness of the models but also emphasizes the potential for more precise predictions using machine learning methods, thereby establishing a robust foundation for the thorough study and broader application of MSMA vibration energy transducers.
Substantial soil organic carbon retention along floodplains of mountain streams

Small, snowmelt‐dominated mountain streams have the potential to store substantial organic carbon in floodplain sediment because of high inputs of particulate organic matter, relatively lower temperatures compared with lowland regions, and potential for increased moisture conditions. This work (i) quantifies mean soil organic carbon (OC) content along 24 study reaches in the Colorado Rocky Mountains using 660 soil samples, (ii) identifies potential controls of OC content based on soil properties and spatial position with respect to the channel, and (iii) and examines soil properties and OC across various floodplain geomorphic features in the study area. Stepwise multiple linear regression (adjusted r2 = 0.48, p < 0.001) indicates that percentage of silt and clay, sample depth, percent sand, distance from the channel, and relative elevation from the channel are significant predictors of OC content in the study area. Principle component analysis indicates limited separation between geomorphic floodplain features based on predictors of OC content. A lack of significant differences among floodplain features suggests that the systematic random sampling employed in this study can capture the variability of OC across floodplains in the study area. Mean floodplain OC (6.3 ± 0.3%) is more variable but on average greater than values in uplands (1.5 ± 0.08% to 2.2 ± 0.14%) of the Colorado Front Range and higher than published values from floodplains in other regions, particularly those of larger rivers.