Miracle-dz/UniFAR-FTUs huggingface dataset:
System integration of hybrid assembled large aperture Micro Scanner Array for fast scanning LiDAR sensors

Abstract. We present the design and system integration of a hybrid MEMS scanning mirror (MSM) array developed for real-time three-dimensional imaging with a panoramic optical field of view (FOV) of 360 deg × 60 deg (horizontal × vertical). The pulsed time-of-flight light detection and ranging (LiDAR) system targets a distance measurement range of 100 m with a video-like frame rate of 10 Hz. The fast vertical scan axis is realized by a synchronous scanning MSM array with large receiver aperture. It increases the scanning rate to 3200 Hz, which is four times faster in comparison with state-of-the-art fast macroscopic polygon scanning systems used in current LiDAR systems. A hybrid assembly of frequency selected scanner elements was chosen instead of a monolithic MEMS array to guaranty high yield of MEMS fabrication and a synchronous operation of all resonant MEMS elements at 1600 Hz with large FOV of 60 deg. The hybrid MSM array consists of a separate emitting mirror for laser scanning of the target and 22 reception elements resulting in a large reception aperture of Deff = 23 mm. All MSM are driven in parametric resonance to enable a fully synchronized operation of all individual MEMS scanner elements. Therefore, piezoresistive position sensors are integrated inside the MEMS chip, used for position feedback of the driving control. We focus on the MEMS system integration including the microassembly of multiple MEMS scanning elements using micromechanical self-alignment. We present technical details to meet the narrow tolerance budgets for (i) microassembly and (ii) synchronous driving of multiple MEMS scanner elements.
Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

The use of application media, gamming, entertainment, and healthcare engineering has expanded as a result of the rapid growth of mobile technologies. This technology overcomes the traditional computing methods in terms of communication delay and energy consumption, thereby providing high reliability and bandwidth for devices. In today’s world, mobile edge computing is improving in various forms so as to provide better output and there is no room for simple computing architecture for MEC. So, this paper proposed a secure and energy-efficient computational offloading scheme using LSTM. The prediction of the computational tasks is done using the LSTM algorithm, the strategy for computation offloading of mobile devices is based on the prediction of tasks, and the migration of tasks for the scheme of edge cloud scheduling helps to optimize the edge computing offloading model. Experiments show that our proposed architecture, which consists of an LSTM-based offloading technique and routing (LSTMOTR) algorithm, can efficiently decrease total task delay with growing data and subtasks, reduce energy consumption, and bring much security to the devices due to the firewall nature of LSTM.
Quality in Standardized Patient Training and Delivery: Retrospective Documentary Analysis of Trainer and Instructor Feedback

Background An important aspect of developing and maintaining a high-quality standardized patient (SP) program is incorporating quality assurance processes. Trainer and instructor feedbacks are considered critical in achieving these goals. The aim of this study is to determine programmatic and systematic issues in the scope of quality assurance and improvement through trainer and instructor feedback on SP performance. We also presented a logic model based on a synthesis of the current literature to ensure the development and maintenance of a quality management culture in the SP program. Methods A retrospective analysis of SP scoring was conducted, and written feedback forms completed by trainers and instructors in a large Canadian university’s SP program were collected. The previous six years (2014-2020) of SP feedback forms in the scope of quality assurance were reviewed and analyzed. Descriptive statistics were utilized to analyze the ratings. Thematic analysis was conducted on the data gathered from the written feedback. Results A total of 138 feedback forms were reviewed and analyzed in the study. The mean ratings given by the trainers for feedback and professionalism were 4.27 ± 1.29 and 4.77 ± 0.8, respectively. The mean ratings given by the instructors for knowledge of case information, appropriate responses, and affect were 4.84 ± 0.64, 4.86 ± 0.35, and 4.71 ± 0.76, respectively (from a range of 1 to 5). Four key themes emerged from the written feedback: nonverbal behaviors in simulation activity or feedback sessions, providing feedback from the patient perspective, consistency between role portrayal and scenario, and adapting easily to changing situations. Conclusions Component scoring on SP performance did not discriminate individual issues, but the qualitative comments identified certain specific issues. Further research is needed to establish standards of continuous quality improvement (CQI) within an SP program.
Magnetic Induction Technology-Based Wireless Sensor Network for Underground Infrastructure, Monitoring Soil Conditions, and Environmental Observation Applications: Challenges and Future Aspects

Wireless sensor networks (WSNs) especially with sensor nodes communicating with each other in medium other than air have been naive area of research since the last few years. In comparison to underwater communication, wireless underground sensor networks (WUSNs) are now being used in a large number of applications ranging from environmental observation, estimating chances of earthquake, communicating in underground tunnels or mines, and infrastructure monitoring to soil monitoring for agricultural purposes. In spite of all such promising applications, due to harsh and dynamically changing soil characteristics including soil type, water content in soil, and soil temperature, underground communication with conventional electromagnetic (EM) wave-based technology could not prove to be feasible for long-distance communication. Alternatively, due to magnetic permeability of soil being similar to air, magnetic induction- (MI-) based approach was adopted using magnetic coils as antenna for sensor nodes. Subsequently, MI waveguide and 3D coil mechanisms were considered to improve the system efficiency. Attributing to different characteristics of underlying transmission channels, communication protocols as well as architecture of MI-based WUSNS (MI-WUSNs) have been developed with different approaches. In this review paper, in addition to the latest advancements made for MI-WUSNs, closely associated areas of MI-WUSNs have also been explored. Additionally, research areas which are still open to be worked upon have been detailed out.
Almost All About Unit Roots

Many economic theories depend on the presence or absence of a unit root for their validity, and econometric and statistical theory undergo considerable changes when unit roots are present. Thus, knowledge on unit roots has become so important, necessitating an extensive, compact, and nontechnical book on this subject. This book is rested on this motivation and introduces the literature on unit roots in a comprehensive manner to both empirical and theoretical researchers in economics and other areas. By providing a clear, complete, and critical discussion of unit root literature, In Choi covers a wide range of topics, including uniform confidence interval construction, unit root tests allowing structural breaks, mildly explosive processes, exuberance testing, fractionally integrated processes, seasonal unit roots and panel unit root testing. Extensive, up to date, and readily accessible, this book is a comprehensive reference source on unit roots for both students and applied workers.
Mental Health Treatment Histories, Recovery, and Well-being

Epidemiological and sociological research on recovery from mental disorder is based on three rarely tested medical model assumptions: (1) recovery without treatment is the result of less severe illness, (2) treatment predicts recovery, and (3) recovery and well–being do not depend on individuals’ treatment histories. I challenge these assumptions using National Comorbidity Survey-Replication data for individuals with any disorder occurring prior to the current year (N = 2,305). Results indicated that (1) untreated remissions were fully explained by less serious prior illness, (2) treated individuals were less likely to recover due to more serious illness, and (3) people who had past–only treatment were more likely to recover than the never–treated, while those in recurring and recently initiated care were less likely to recover. Treatment histories predicted greater well–being only if recovery had been attained. Histories of care help to explain recovery rates and suggest new directions for treatment–seeking theory and research.
Does Sincere Social Interaction Stimulate Tourist Immersion? A Conservation of Resources Perspective

Drawing on conservation of resources (COR) theory, this study examines how and when sincere social interaction affects tourist immersion at the destination. We develop a moderated mediation model in which emotional solidarity mediates the relationship between sincere social interaction and tourist immersion, while extraversion moderates the link between sincere social interaction and emotional solidarity. Data were collected from 391 tourists via an online survey and were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings indicate that sincere social interaction directly influences tourist immersion, and this relationship is mediated by emotional solidarity. Furthermore, extraversion significantly moderates the effects of sincere social interaction on emotional solidarity, and this effect is stronger for tourists with high extraversion scores. Additionally, extraversion strengthens the indirect link between sincere social interaction and tourist immersion, and again, the link is stronger for highly extraverted tourists.
VANET Based Four Way Road Intersection Traffic Light Control Model

Traffic lights are responsible in maintaining the smooth passage of traffic in urban scenario. To enhance the efficiency of traffic control system we are utilizing the concept of Vehicular Ad-hoc Network (VANET) along with our proposed VANET Based Four Way Road Intersection Traffic Light Control Model. The proposed model facilitates more number of vehicles to cross the intersection simultaneously; reduces the vehicles waiting time at intersection. The dynamic cycle traffic control provides an adaptive mechanism to adjust the timing behavior of traffic signal in accordance to traffic demand. Traffic density estimation and traffic density analysis are the two major phases of the proposed traffic signal control. The proposed system comprises of estimation of vehicle density in different lanes approaching the intersection. The density estimation is carried out using (V2I) Vehicle to Infrastructure and (V2V) Vehicle to Vehicle communication. Further the assessment and analysis of obtained data from the estimated density part is carried out by the traffic signal controller to adjust the traffic signal cycles in accordance with the traffic requirement such that unnecessary traffic waiting time can be minimized. The proposed model and proposed Green Light Allocation algorithm is evaluated against the existing static and dynamic cycle control system using Matlab. We found that our proposed system is giving better performance by allowing more traffic volume to cross through the intersection in each cycle. Also our proposed system is reducing the waiting time of vehicles at intersection by frequently switching the Green Light among the phases of same signal cycle.
Evaluation and selection of lean resourced employee in the manufacturing industries using the TOPSIS-Simos method

Among modern manufacturing methods, lean manufacturing has been a dominant method used in many industrial sectors. The principle of the lean manufacturing is to eliminate the wastes present in various forms of the industry. The adoption of lean manufacturing in an industry could be identified by the leanness measurement through lean assessment. In lean assessment, the evaluator plays a vital role. Selecting the right person for the assessment will reduce the ambiguity, time consumption and computation time. Decision-makers play a vital role in lean assessment. Few studies have been found in the literature to select or identify the decision-maker. In this article, the TOPSIS-Simos method has been proposed to identify a lean resourced employee in the industry. The proposed method is case studied in the manufacturing industries. The lean resourced employee has been identified after computation. The proposed method could be applied to identify the lean resourced employee in all types of manufacturing industries.
Light‐driven dynamics between calcification and production in functionally diverse coral reef calcifiers

Coral reef metabolism underpins ecosystem function and is defined by the processes of photosynthesis, respiration, calcification, and calcium carbonate dissolution. However, the relationships between these physiological processes at the organismal level and their interactions with light remain unclear. We examined metabolic rates across a range of photosynthesising calcifiers in the Caribbean: the scleractinian corals Acropora cervicornis, Orbicella faveolata, Porites astreoides, and Siderastrea siderea, and crustose coralline algae (CCA) under varying natural light conditions. Net photosynthesis and calcification showed a parabolic response to light across all species, with differences among massive corals, branching corals, and CCA that reflect their relative functional roles on the reef. At night, all organisms were net respiring, and most were net calcifying, although some incubations demonstrated instances of net calcium carbonate (CaCO3) dissolution. Peak metabolic rates at light‐saturation (maximum photosynthesis and calcification) and average dark rates (respiration and dark calcification) were positively correlated across species. Interspecies relationships among photosynthesis, respiration, and calcification indicate that calcification rates are linked to energy production at the organismal level in calcifying reef organisms. The species‐specific ratios of net calcification to photosynthesis varied with light over a diurnal cycle. The dynamic nature of calcification/photosynthesis ratios over a diurnal cycle questions the use of this metric as an indicator for reef function and health at the ecosystem scale unless temporal variability is accounted for, and a new metric is proposed. The complex light‐driven dynamics of metabolic processes in coral reef organisms indicate that a more comprehensive understanding of reef metabolism is needed for predicting the future impacts of global change.
Antibiotic Azithromycin inhibits brown/beige fat functionality and promotes obesity in human and rodents

Obesity, a metabolic disease caused by multiple factors, has become a global health problem. In addition to nutrient intake and sedentary lifestyle, environmental pollutants exposure has been shown to be involved in obesity epidemics. Antibiotics, a new type of environmental pollutant, have been widely used in animal husbandry, aquaculture and microorganism. However, the effects of antibiotics exposure on fat metabolism and metabolic diseases are largely unknown. Methods: We screened major types of antibiotics to examine their effects on the differentiation capacity and thermogenic functionality of brown and beige adipocytes, and found that azithromycin, one major kind of macrolide antibiotics suppressed brown and beige adipocyte functionality. We thus examined azithromycin accretion in adipose tissues of obese patients that correlates with BMI by high performance liquid chromatography-tandem mass spectrometry and systematically explore the influences of azithromycin on adiposity and metabolic performance in mice under high diet. Results: Azithromycin (macrolides) inhibits the mitochondrial and thermogenic gene programs of brown and beige adipocytes, thus disrupting their mitochondrial function and thermogenic response. Consistently, azithromycin treatment are more prone to diet-induced obesity in mice, and this was associated with impaired energy expenditure. Importantly, azithromycin is more accumulated in adipose tissue of obese patients and correlates with BMI and body weight. Mechanistically, we found that azithromycin inhibits mitochondria respiratory complex I protein levels and increases reactive oxidative species (ROS) levels, which causes damage of mitochondrial function in brown and beige adipocytes. The deleterious effects of azithromycin can be ameliorated by antioxidant N-acetyl-L-cysteine. Conclusions: Taken together, this work highlights the possible role of azithromycin in obesity epidemic and presents strategies for safe applications of antibiotics in the future.
Resolving Protein Conformational Plasticity and Substrate Binding Through the Lens of Machine-Learning

A long-standing target in elucidating the biomolecular recognition process is the identification of binding-competent conformations of the receptor protein. However, protein conformational plasticity and the stochastic nature of the recognition processes often preclude the assignment of a specific protein conformation to an individual ligand-bound pose. In particular, we consider multi-microsecond long Molecular dynamics simulation trajectories of ligand recognition process in solvent-inaccessible cavity of two archtypal systems: L99A mutant of T4 Lysozyme and Cytochrome P450. We first show that if the substrate-recognition occurs via long-lived intermediate, the protein conformations can be automatically classified into substrate-bound and unbound state through an unsupervised dimensionality reduction technique. On the contrary, if the recognition process is mediated by selection of transient protein conformation by the ligand, a clear correspondence between protein conformation and binding-competent macrostates can only be established via a combination of supervised machine learning (ML) and unsupervised dimension reduction approach. In such scenario, we demonstrate that an a priori random forest based supervised classification of the simulated trajectories recognition process would help characterize key amino-acid residue-pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction via time-lagged independent component analysis of the selected residue-pairs would delineate a conformational landscape of protein which is able to demarcate ligand-bound pose from the unbound ones. As a key breakthrough, the ML-based protocol would identify distal protein locations which would be allosterically important for ligand binding and characterise their roles in recognition pathways.
Does intellectual property protection deliver economic benefits? A multi‐outcome meta‐regression analysis of the evidence

Both theoretical and empirical work reports contingent and conflicting findings on how intellectual property (IP) protection affects related outcomes such as innovation, technology diffusion, productivity, or growth. To establish where the balance of the evidence lies, we conduct a multi-outcome meta-regression analysis to synthesize findings form 91 primary studies that report 1,626 effect-size estimates for one or more outcomes. Controlling for unobserved heterogeneity and selection bias only, we find that the effect on innovation, technology diffusion, productivity, and economic growth is statistically or practically insignificant. The effect remains insignificant when we control for observed sources of heterogeneity and estimate meta-effects based on different scenarios for ‘best-practice’ research. Our work contributes to the existing research effort by extending the application of the multi-outcome meta-regression analysis into evidence synthesis in economics. It also provides verifiable/replicable evidence indicating that the sanguine claims about the economic benefits of IP protection voiced in some legal studies and the advocacy literature are misleading.
Ginger nanoparticles mediated induction of Foxa2 prevents high-fat diet-induced insulin resistance

Rationale: The obesity epidemic has expanded globally, due in large part to the increased consumption of high-fat diets (HFD), and has increased the risk of major chronic diseases, including type 2 diabetes. Diet manipulation is the foundation of prevention and treatment of obesity and diabetes. The molecular mechanisms that mediate the diet-based prevention of insulin resistance, however, remain to be identified. Here, we report that treatment with orally administered ginger-derived nanoparticles (GDNP) prevents insulin resistance by restoring homeostasis in gut epithelial Foxa2 mediated signaling in mice fed a high-fat diet (HFD). Methods: Ginger-derived nanoparticles (GDNP) were added into drinking water to treat high-fat diet fed mice for at least one year or throughout their life span. A micro array profile of intestinal, liver and fat tissue of GDNP treated mice was used to analyze their gene expression profile. Genes associated with metabolism or insulin signaling were further quantified using the real time polymerase chain reaction (RT-PCR). Surface plasmon resonance (SPR) was used for determining the interaction between Foxa2 protein and phosphatic acid lipid nanoparticles. Results: HFD-feeding inhibited the expression of Foxa2; the GDNPs increased the expression of Foxa2 and protected Foxa2 against Akt-1 mediated phosphorylation and subsequent inactivation of Foxa2. Increasing expression of Foxa2 leads to altering the composition of intestinal epithelial cell (IEC) exosomes of mice fed a HFD and prevents IEC exosome mediated insulin resistance. Collectively, oral administration of GDNP prevents insulin resistance in HFD mice. Interestingly, oral administration of GDNP also extended the life span of the mice and inhibited skin inflammation. Conclusion: Our findings showed that GDNP treatment can prevent HFD-induced obesity and insulin resistance via protecting the Foxa2 from Akt-1 mediated phosphorylation. GDNP treatment provides an alternative approach based on diet manipulation for the development of therapeutic interventions for obesity.
SINR Statistics of Correlated MIMO MMSE Receivers

In this paper, we develop an analytical framework to characterize the signal-to-interference-plus-noise ratio of multiple-input-multiple-output (MIMO) minimum mean-square error receivers in the presence of multiple MIMO cochannel interferers. The analysis resorts to the Berezin’s supermathematics which enables us to quantify the unequal-power and spatially correlated interference effects.
Outcomes following surgical treatment of distal radial fracture: a comparison of older and younger patients using PROMIS

The purpose of this study was to assess the recovery patterns following surgery for distal radial fracture (DRF) in patients over (n = 99) and under (n = 273) the age of 65 using the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function (PF) and Pain Interference (PI) questionnaires. Both the older and younger cohorts showed postoperative improvement in PF and PI. The younger cohort had higher PF scores from 1 to 6 months postoperatively, however, PI scores were not significantly different between the cohorts during any period. A greater proportion of younger patients achieved the minimal clinically important difference improvement on the PROMIS PF (80% versus 66%) and PI (88% versus 75%) scales. To appropriately manage postoperative expectations, older patients should be counselled that they would likely experience most of their functional recovery by 3 months and limitations due to pain would likely be stable by 1 month. Level of evidence: II