Miracle-dz/UniFAR-FTUs huggingface dataset:
An $L_{0}$-Norm Constrained Unsupervised Non-Negative Matrix Factorization Algorithm for Privacy Preserving Energy Usage Decomposition

By energy usage decomposition, this paper refers to the decomposition of energy use time series data into its major load classes. Specifically, a novel unsupervised learning algorithm is proposed to classify energy usage in a residential unit into fixed loads and one or more classes of shiftable loads. Unlike traditional disaggregation, the algorithm does not require any prior information about the load consumption patterns of individual appliances in the home, thereby fully preserving the home's privacy. Shiftable loads are characterized by ON/OFF duty cycles. The proposed algorithm is based on non-negative matrix factorization (NMF), where fixed loads are represented in terms of real-valued basis vectors, whereas shiftable loads are divided into binary signals. This binary decomposition approach directly applies $L_{0}$-norm constraints on individual shiftable loads. The new approach obviates the need for more computationally intensive methods (e.g. spectral decomposition or mean-field annealing) that have been used in earlier research for these constraints. A probabilistic framework for the proposed approach has been outlined.
Affective Computing based on One-dimensional Convolutional Spiking Neural Network

Affective computing is an important branch of human-computer interaction, which aims to develop algorithms to identify, interpret, process and simulate human emotions. In this work, we propose an affective computing neural network which is based on spiking neural network, temporal spike sequence learning is utilized via back-propagation for establishing our one-dimensional convolution deep spiking neural networks. Experimental results show that our network structure can achieve excellent results.
Design of a Variable Stiffness Actuator Based on Flexures

Variable stiffness actuators can be used in order to achieve a suitable trade-off between performance and safety in robotic devices for physical human―robot interaction. With the aim of improving the compactness and the flexibility of existing mechanical solutions, a variable stiffness actuator based on the use of flexures is investigated. The proposed concept allows the implementation of a desired stiffness profile and range. In particular, this paper reports a procedure for the synthesis of a fully compliant mechanism used as a nonlinear transmission element, together with its experimental characterization. Finally, a preliminary prototype of the overall joint is depicted.
A Systematic Approach to Designing Broadband Millimeter-Wave Cascode Common-Source With Inductive Degeneration Low Noise Amplifiers

This paper presents a design methodology that can effectively extend the bandwidth of a cascode common-source with inductive degeneration low noise amplifier (LNA), which is one of the most popular LNA topologies in the millimeter-wave bands. Specifically, this methodology addresses how to broaden the input matching bandwidth by realizing dual-resonant ${S}$ 11, and how to extend the gain bandwidth by synthesizing a transformer-based second-order bandpass output network. As a proof of concept, a 27–46 GHz LNA is implemented in the GlobalFoundries 45-nm CMOS SOI process, achieving 25.5–50 GHz 3-dB gain bandwidth, 27–46 GHz return loss bandwidth, 21.2 dB peak gain, 2.4 dB minimum noise figure, and −9.5 dBm peak IIP 3, under 25.5 mW DC power consumption. Consistent performance is measured across multiple samples, demonstrating the robustness of the presented design methodology.
Burnout, Resilience, Supervisory Support, and Quitting Intention among Healthcare Professionals in Saudi Arabia: A National Cross-Sectional Survey

Although personal resilience and supervisory support are known to reduce the impact of burnout and quitting intention, there is limited data available to explore these relationships among healthcare professionals (HCPs) in Saudi Arabia. This study aimed to assess the prevalence of burnout and explore its association with resilience, supervisory support, and intention to quit among Saudi Arabian HCPs. Methods: A cross-sectional survey was distributed to a convenience sample of HCPs between April and November 2022. Participants responded to socio-demographic questions, the Maslach Burnout Inventory-Human Services Survey for Medical Personnel (MBI-HSS (MP)), the Connor-Davidson resilience scale 10 (CD-RISC 10), and the Perceived of Supervisor Support Scale (PSS). Descriptive, inferential, correlation, and logistic regression tests were performed for data analyses. Results: Of the 1174 HCPs included in the analysis, 77% were presented with high burnout levels: 58% with emotional exhaustion (EE), 72% with depersonalization (DP), and 66% with low personal accomplishment (PA). Females were associated with increased odds of burnout (OR: 1.47; 95% CI: 1.04–2.06) compared to males. Burnout and its subscales were associated with higher intention to leave practice, with 33% of HCPs considering quitting their jobs. Furthermore, HCPs reported a low resilience score overall, and negative correlations were found between EE (r = −0.21; p < 0.001) and DP (r = −0.12; p < 0.01), and positive correlation with low PA (r = 0.38; p < 0.001). In addition, most HCPs perceived supervisory support as low, and it is associated with increased burnout and quitting intention. Conclusion: Burnout is common among HCPs across all clinical settings and is associated with higher intention to quit and low resilience and supervisory support. Workplace management should provide a supportive workplace to reduce burnout symptoms and promote resiliency.
Online Adaptive CNN: a Session-to-session Transfer Learning Approach for Non-stationary EEG

The convolutional neural network (CNN) automatically learns EEG representations in higher and nonlinear space via backpropagation and outputs the predictions in an end-to-end manner. Owing to these advantages, CNN has been used to decode electroencephalogram (EEG) and drive brain computer interface (BCI). However, its applications in BCI-assisted post-stroke neurorehabilitation remain limited for it is unable to address the inherent session-to-session non-stationarity in the EEG between the initial calibration session and subsequent online sessions. In this paper, we present a simple but effective online adaptive CNN (aCNN) to address the non-stationarity in multi-session EEG by progressively updating the subject-specific model. The performance of the proposed aCNN is evaluated on two neurorehabilitation datasets with a large population of post-stroke patients (33 patients with a total of 358 EEG sessions). Results indicate that, our proposed aCNN reaches at least as good a performance as the widely used online adaptive Filter Bank Common Spatial Patterns (aFBCSP) and with significantly higher accuracies than that for DeepConv and offline FBCSP algorithms. Our results support, for the first time, the use of a CNN-based adaptive learning method to decode non-stationary EEG signals for BCI-assisted post-stroke rehabilitation.
Improved Bat Algorithm for RFID Indoor Localization

At present, most Radio Frequency Identification (RFID) indoor localization algorithms are based on geometric methods. However, the traditional geometric localization method is time consuming. In this paper, an improved bat algorithm (IBA) based localization algorithm is proposed, which transforms the localization problem into an optimization problem. Inspired by the multiple signal classification (MUSIC) based angle estimation and the frequency hopping ranging principle, the evaluation function merged with angle weight and range weight is designed to evaluate the location of each bat, and constantly iterate to find the bat's position with the optimal evaluation value to realize localization. The experimental results show that the median posltioning error of this algorithm is 6.82 cm,
An Algorithm for Computing Critical Buckling Loads of Elastic Structures

ABSTRACT The algorithm presented enables one to calculate the number of critical buckling loads of an elastic structure which lie below any specified total load, and hence to converge on all required buckling loads with ease and certainly. The algorithm is always valid when the total load is applied proportionally, it is also valid for nonproportional loading, subject to a single restriction. The algorithm applies to continuous structures, as well as to structures with a finite number of degrees of freedom. It is based on a stiffness matrix analysis and substructures can be used.
Near‐field optics: Light for the world of NANO

The wealth of optical phenomena offers interesting opportunities in the world of nanometer dimensions. The field of radiation has to be confined and controlled for this purpose by material structures such as small apertures or slits. Many optical effects manifest themselves in a novel way if the relevant dimensions are small compared to the wavelength. Our theoretical understanding therefore lags somewhat behind the experimental results which have made rapid progress in the past two to three years. This paper concentrates for this reason on the description of a model scanning near‐field optical microscope which allows us to gain some insight into the complex problems of coupling light into a nanometer‐sized structure, the distribution and possible field enhancement inside such structures, and the radiation from the nanoscale structure into the macroscopic world, where it can be detected by conventional means.
Simultaneous Bilateral Total Hip Arthroplasty with Straight-Stems and Short-Stems: Does the Short One Do a Better Job?

Background: Total hip arthroplasty (THA) is known to be the most successful orthopaedic surgery of the last century, but it is still struggling with controversies concerning one-stage bilateral THA. The current study aimed to compare the clinical outcome of patients with unilateral or simultaneous bilateral THA by using short-stem and straight-stem designs and focusing on operation time, blood loss, and length of hospital stay (LOS). Material and Methods: Between 2006 and 2018, 92 patients were enrolled in this study. Forty-six patients underwent a bilateral THA in one session, and forty-six matched patients underwent a unilateral THA. In each of the two groups (unilateral vs. bilateral), 23 patients received either a straight (unilateral: 10 females, 13 males, mean age 63; bilateral: 12 females, 11 males, mean age 53 years) or short stem (unilateral: 11 females, 12 males, mean age 60 years; bilateral: 12 females, 11 males, 53 mean age 62 years). The blood count was checked preoperatively as well as one and three days after surgery. Furthermore, the operation time and LOS were investigated. Results: Compared to THA with straight-stems, short-stem THA showed significantly less blood loss; there was no difference in the LOS of both groups. A significantly shorter operative time was only observed in the bilateral THA. Conclusion: The current study showed that simultaneous bilateral THA appears to be safe and reliable in patients without multiple comorbidities. In addition, short-stem THA appears to be beneficial in terms of clinical performance and outcome, and it appears to be superior to straight-stem THA, regardless of whether the patient underwent unilateral or simultaneous bilateral THA.
X-ray emission from supernovae: a review of the observations

Supernovae have been expected to be X-ray sources for many years. This paper reviews the observations that have detected or placed upper limits on the emission of X-rays from supernovae. A firm upper limit exists on X-ray emission from Type Ia supernovae based upon an observation of SN 1992A (parent galaxy: NGC 1380) made with ROSAT. The type II supernovae detected include SN 1978K (NGC 1313), SN 1980K (NGC 6946), SN 1986J (NGC 891), SN 1987A (Large Magellanic Cloud), and, most recently, SN 1993J (NGC 3031). These supernovae emit X-rays by at least two different processes. The author briefly reviews the proposed emission mechanisms.
A New Method for Sequential Fractionation of Nitrogen in Drained Organic (Peat) Soils

The aim of this study was to assess the transformation of organic matter in organic soils undergoing a phase of secondary transformation, based on a new method of nitrogen compound fractionation. Laboratory tests were carried out for 31 layers of muck (after secondary transformation) and peat (parent material of the soil) of drained organic soils (peat). The new method consists of sequential extraction in the following steps: (1) 0.5 M K2SO4 (extraction at room temperature); (2) 0.25 M H2SO4 (hot hydrolysis) (3) 3.0 M H2SO4 (hot hydrolysis); and (4) concentrated H2SO4 (mineralization of the post-extraction residue). As a result of the extraction process, the following fractions (operating forms) were obtained: mineral nitrogen (Nmin), dissolved organic nitrogen (N-DON), readily hydrolyzing organic nitrogen (N-RH), non-readily hydrolyzing organic nitrogen (N-NRH), and non-hydrolyzing organic nitrogen (N-NH). The study demonstrates the usefulness of the applied method for assessing the degree of secondary transformation of drained organic soils. The obtained results of nitrogen fractionation indicate the significant dynamics of nitrogen forms’ transformations and a significant relationship between these forms and soil properties. Nitrogen transformation processes during the secondary transformation process after dehydration resulted in an increase in the share of N-DON (on average: 1.47% of Norg for the peat layers and 2.97% of Norg for the muck layers) and in an increase in the share of NRHON (on average: 20.7% of Norg for the peat layers and 33.5% of Norg for the muck layers). The method of sequential nitrogen fractionation used in our study allowed us to define an index determining the degree of transformation of organic matter in peat after drying. We defined it as the ratio of readily hydrolyzable forms (the fraction is very variable in the secondary transformation process) to non-readily hydrolyzable forms (relatively stable fraction in the secondary transformation process): N-RH/N-NRH. The average value of this index was significantly lower in the peat layers (0.64 on average) than in the muck beds (1.04 on average). The value of this index is significantly correlated with soil properties: bulk density (R2 = 0.470); general porosity (R2 = 0.503); total carbon content (TC) (R2 = 0.425); total carbon to total nitrogen ratio (TC/TN) (R2 = 0.619); and share of carbon of humic substances (C-HS) (R2 = 0.466). We believe that the method of sequential nitrogen fractionation may be useful for other soils and organic materials.
A Distributed Data-Driven Optimal Fault Detection Approach for Complex Interconnected Systems

In this paper, a distributed data-driven optimal fault detection approach for complex interconnected systems is proposed, which uses sensor networks to collect process variable data. Recall that the average consensus algorithm is generally adopted for distributed fault detection, which would inevitably result in detection delay. To deal with this issue, an alternative iterative approach is developed to achieve the optimal detection in each iteration step in this paper. To be specific, covariance coefficient matrix in the iterative process of the average consensus algorithm is first decomposed into the matrix product with the same structure, which lays the foundation for further distributed fault detection. Based on it, a distributed data-driven optimal fault detection is developed which consists of the offline training and online detection. In the offline training stage, the detection statistic function is constructed through the obtained covariance in the iterative fashion. In the online detection stage, the optimal fault detection scheme is carried out to solve detection delay problem in the iterative process of the average consensus algorithm. A case study on the Tennessee Eastman benchmark are used to demonstrate the proposed approach.
Infrared imaging of hand vein patterns for biometric purposes

A novel non-invasive imaging technique to image the vein patterns in various parts of the hand for biometric purposes is evaluated. Two imaging methods are investigated: far-infrared (FIR) thermography and near-infrared (NIR) imaging. Experiments involving data acquisition from various parts of the hand, including the back of the hand, palm and wrist, were carried out using both imaging techniques. Analysis of the data collected shows that FIR thermography is less successful at capturing veins in the palm and wrist. FIR thermography can capture the large veins in the back of the hand, but it is sensitive to ambient temperature and humidity conditions as well as human body temperature. NIR imaging produces good quality images when capturing veins in the back of the hand, palm and wrist. NIR imaging is also more tolerant to changes in the environment and body condition but faces the problem of pattern corruption because of visible skin features being mistaken for veins. This corruption is not present in FIR imaging. An initial biometric system is investigated to test both FIR and NIR images for biometric purposes. The results show all the subjects were correctly identified, which indicates vein pattern biometrics with infrared imaging is a potentially useful biometric.
Raclopride-Molecularly Imprinted Polymers: A Promising Technology for Selective [11C]Raclopride Purification

In this work, we developed a novel approach to purify [11C]Raclopride ([11C]RAC), an important positron emission tomography radiotracer, based on tailored shape-recognition polymers, with the aim to substitute single-pass HPLC purification with an in-flow trap & release process. Molecular imprinting technology (MIT) applied to solid phase extraction (MISPE) was investigated to develop a setting able to selectively extract [11C]RAC in a mixture containing a high amount of its precursor, (S)-O-Des-Methyl-Raclopride (DM-RAC). Two imprinted polymers selective for unlabeled RAC and DM-RAC were synthesized through a radical polymerization at 65 °C using methacrylic acid and trimethylolpropane trimethacrylate in the presence of template molecule (RAC or DM-RAC). The prepared polymer was characterized by scanning electron microscopy and Fourier transform infrared spectroscopy and tested in MISPE experiments. The polymers were used in testing conditions, revealing a high retention capacity of RAC-MISPE to retain RAC either in the presence of similar concentrations of RAC and DM-RAC precursor (96.9%, RSD 6.6%) and in the presence of a large excess of precursor (90%, RSD 4.6%) in the loading solution. Starting from these promising results, preliminary studies for selective purification of [11C]Raclopride using this RAC-MISPE were performed and, while generally confirming the selectivity capacity of the polymer, revealed challenging applicability to the current synthetic process, mainly due to high backpressures and long elution times.
Kramers–Kronig constrained variational analysis of optical spectra

A universal method of extraction of the complex dielectric function ϵ(ω)=ϵ1(ω)+iϵ2(ω) from experimentally accessible optical quantities is developed. The central idea is that ϵ2(ω) is parameterized independently at each node of a properly chosen anchor frequency mesh, while ϵ1(ω) is dynamically coupled to ϵ2(ω) by the Kramers–Kronig (KK) transformation. This approach can be regarded as a limiting case of the multioscillator fitting of spectra, when the number of oscillators is on the order of the number of experimental points. In the case of the normal-incidence reflectivity from a semi-infinite isotropic sample the new method gives essentially the same result as the conventional KK transformation of reflectivity. In contrast to the conventional approaches, the proposed technique is applicable, without readaptation, to virtually all types of linear-response optical measurements, or arbitrary combinations of measurements, such as reflectivity, transmission, ellipsometry, etc., done on different types of sa.
Differences in Cardiac-Pulsatility-Induced Displacement and Geometry Changes between the Cook ZBIS and Gore IBE: Postoperative Comparison Using ECG-Gated CTA Scans

To what extent the stentgraft design of iliac branch devices (IBDs) relates to dynamic deformation is currently unknown. Therefore, this study aimed to quantify and compare displacement and geometry changes during the cardiac cycle of two common IBDs. This paper presents a two-center trial with patients treated with a Zenith bifurcated iliac side (ZBIS) or Gore iliac branch endoprosthesis (IBE). All patients underwent a retrospective electrocardiogram (ECG)-gated computed tomographic angiography (CTA) during follow-up. Cardiac-pulsatility-induced displacement was quantified for the following locations: (neo) bifurcation of the aorta, IBD flow divider, distal markers of the internal iliac artery (IIA) component and first IIA bifurcation. Geometrical parameters (length, tortuosity index, curvature and torsion) were quantified over centerlines. Displacement was more pronounced for the IBE than the ZBIS, e.g., craniocaudal displacement of 0.91 mm (0.91–1.13 mm) vs. 0.57 mm (0.40–0.75 mm, p = 0.004), respectively. The IBDs demonstrated similar geometrical parameters in the neo-common iliac artery and distal IIA, except for the larger dynamic curvature and torsion of the distal IIA in IBEs. The IBEs showed more dynamic length and curvature change compared to the ZBIS in the stented IIA. The IIA trajectory showed more pronounced deformation during the cardiac cycle after placement of an IBE than a ZBIS, suggesting the IBE is more conformable than the ZBIS.
Strategic Innovation Management at Netflix: A Case Study

: Netflix is a company that implemented a disruptive innovation and shocked the business market with its way to create and deliver value to their customers, breaking away with the old way to watch a movie at home. The culture of freedom and responsibility engaged by a radical transparency is committed by the whole company where every kind of employee at all teams share ideas and know everything about the company’s strategy. This research consists of a description and analysis of the strategic approaches used by Netflix that explain its business success and demonstrates the technology and the business development made by one the most important players in the streaming service. It is entirely based on secondary data obtained through an exploratory literature review over the last ten years. The model by which Netflix manages its innovation process is based on agile frameworks who actively engage the developer’s team, team leaders, and top management and have the costumer at the center of the value stream. The internal culture of the firm, based on ample freedom and responsibility, and engaged by a radical transparency, is pursued by the whole company where every kind of employee, in all teams, share ideas and know everything about the company’s strategy. The article explores and emphasizes the main aspects of the company’s strategy to innovation which is based on the development of specific technological approaches based on purpose made algorithms that works to map the streaming user’s preferences. It is coupled with the implementation and constant dissemination of the agile mindset linked with frameworks, methods, and techniques such as Scrum, Kanban, and Agile at Scale, which together enable Netflix to sustain and promote its innovative business process. Netflix approach to innovation has become a model for many other companies over the globe to create an environment focused on customer centricity, elect quality as a statement, and align employees with strategy.