Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivalley envelope function equations and effective potentials for phosphorus impurity in silicon

We propose a system of real-space envelope function equations without fitting parameters for modeling the electronic spectrum and wave functions of a phosphorus donor atom embedded in silicon. The approach relies on the Burt-Foreman envelope function representation and leads to coupled effective-mass Schroedinger equations containing smooth effective potentials. These potentials result from the spatial filtering imposed on the exact potential energy matrix elements in the envelope function representation. The corresponding filter function is determined from the definition of the envelope function. The resulting effective potentials and the system of envelope functions jointly reproduce the valley-orbit coupling effect in the doped silicon. Including the valley-orbit coupling not only of the 1s, but also for 2s atomic orbitals, as well as static dielectric screening is found crucial to accurately reproduce experimental data. The measured binding energies are recovered with a maximum relative error of 1.53 %. The computed wave functions are in a good agreement with experimental measurements of the electron density provided by scanning tunneling microscopy.
Kernel Subspace Clustering based on Block Diagonal Representation and Sparse Constraints

Subspace clustering is an effective method for high-dimensional data clustering. On the premise of global linearity of data, it uses data self-representation to reconstruct each sample linearly, and achieves good results. However, the actual original data structure is usually nonlinear, which makes the subspace clustering algorithm designed on the premise of linear subspace not achieve satisfactory results in dealing with nonlinear data. In order to deal with nonlinear data better, we use kernel function to introduce block diagonal structure and sparse prior into kernel feature space, and propose a kernel subspace clustering method based on block diagonal representation and sparse constraints (KSCBS). Firstly, we perform subspace learning by combining block diagonal representation and sparse constraints. In this way, the obtained coefficient matrix can maintain the block diagonal structure and better reveal the real attributes of the data. Secondly, we use the kernel technique to map the nonlinear original data space into the appropriate high-dimensional feature space, and then transform it into linear data for processing to solve the nonlinear problem of subspace data. Finally, we use the alternating minimization algorithm to solve the objective function. Compared with other advanced linear subspace and nonlinear subspace algorithms, our algorithm has better clustering performance on several common data sets.
Decimal States Smart Grid Operations Concept: Technical Solution and Benefit for Renewable Energy Integration

The energy transition is a global challenge that retains the attention of a wide range of stakeholders including engineers, regulators, consumers, investors, and academicians. With higher intermittent renewable energy penetration, electrification of mobility, and intensification of air conditioning demand, important stress on the power grid, resulting in overloading of distribution network infrastructures and grid stability issues may arise in near future. Various solutions such as smart metering, demand response, and energy storage have been explored, however, their cost-to-benefit ratio is still not satisfying. This paper explores a new grid operation concept named "Decimal State" grid which dynamically throttles the subscribed power to match the grid supply capability without a direct load control. A simple yet effective communication method has been built and tested to support the implementation of the decimal state grid. Also, simulations showed that decimal state grid operation can avoid power outages in both fossil fuel or fully solar powered grid with a power deficit. Further, the simulation revealed that a decimal state grid can significantly reduce the size of long-duration energy storage.
Risk Assessment of Acute Kidney Injury and Decision Making by Nonparametric Statistics at Coronary Angiography

The paper describes the computer modeling of risk assessment for acute kidney injury caused by the coronary angiography in patients with suspected coronary artery disease. The main data for the modeling are samples of creatinine and age of patients: 139 men and 139 women. The methods of nonparametric statistics are used in the work; they allow to process medical examination data samples which do not follow any of the known distribution laws. We describe the procedure for reconstruction of the unknown density function on the basis of Parzen-Rosenblatt estimate and selection of the optimal value of the blur parameter (window width). The authors have developed the program that involves the reconstruction of the distribution density function for the glomerular filtration rate (GFR) variation after the coronary angiography as well as the calculation of quantile estimates for the risk of the GFR decrease. The simulation results have shown that the coronary angiography results in 66.328% probability of the GFR decrease in men and 66.346% probability in women. This program will help the attending physician to calculate the probability of the patient’s GFR decrease from the initial level after the coronary angiography below the maximum allowable value: 60 ml/min/1.73 sq.m. and to decide whether it is reasonable to take preventive measures to preserve kidney function.
Investigating microwave properties and modulating dampling parameter in NM/FM thin films for spintronics applications

Microwave properties were investigated in Ta/NiFe $N_{i 81} F_{e 19} \approx$. Permalloy, Py) bilayer system using electrically driven spin torque ferromagnetic resonance (STFMR) technique focusing particularly using frequency sweep measurements. Frequency sweep STFMR signals were detected via spin Hall effect of Ta. This technique was optimized for achieving high signal-to-noise (SNR) ratio and anisotropic magnetoresistance of 1.5% was achieved. The Gilbert damping constant can be tuned by applying an electric current which shows the evidence of spin torque in the system. The value of $\alpha$ is found to be $0.0085 \pm 0.0002$ at $I_{\mathrm{dc}}=0 \mathrm{mA}$. Further, $\theta_{\mathrm{SH}}=-0.160 \pm 0.027$, an important parameter describing performance of Ta was calculated and the parameters quantified corroborate with widely used field sweep technique discussed in literature. The interface transparency, $T=\sim 0.40$, is large which accounts for the flow of spin current density that diffuses into the Py layer via SHE of Ta. Further, these studies are beneficial for the development of Ta/Py based spin Hall nano-oscillators (SHNO) which can generate RF power by application of dc current.
Programming with intersection types, union types, and polymorphism

Type systems based on intersection types have been studied extensively in recent years, both as tools for the analysis of the pure A-calculus and, more recently, as the basis for practical programming languages. The dual notion, union types, also appears to have practical interest. For example, by refining types ordinarily considered as atomic, union types allow a restricted form of abstract interpretation to be performed during typechecking. The addition of second-order polymorphic types further increases the power of the type system, allowing interesting variants of many common datatypes to be encoded in the "pure" fragment with no type or term constants. This report summarizes a preliminary investigation of the expressiveness of a programming language combining intersection types, union types, and polymorphism. This research was supported in part by the Office of Naval Research and in part by the Defense Advanced^ t ^ ^ l ^ } ' m ° n i t 0 r e d ^ ° f f i C e ° f N a V a l R e S e a i C h U n d 6 r C ° n " r a C t N00014-84-K o i l 5 The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of ONR, DARPA or the U.S. government.
Geometry-dependent constitutive law for granular slow frictional drag

Frictional constitutive law for very slow vertical withdrawing of a thin rod from a granular bed is experimentally studied. Using a very precise creep meter, geometry-dependent granular frictional constitutive law is particularly examined. In some previous works, a dimensionless number $I=\dot{\gamma}D_g/\sqrt{p/\rho_g}$ has been used to characterize granular frictional constitutive laws, where $\dot{\gamma}$, $D_g$, $p$, and $\rho_g$ are the shear strain rate, grain diameter, confining pressure, and bulk density of granular bed, respectively. It has been considered that granular frictional constitutive law expressed by $I$ is universal (almost geometry-independent) in dense flow regime. In this study, however, we find that the geometry of the system is much more crucial to characterize granular friction in a very slow withdrawing regime. Specifically, the ratio between rod and grain diameters must be an essential parameter to describe the granular frictional constitutive law. Physical meaning of the geometry-dependent constitutive law is discussed on the basis of grains-contact-number dependence of granular behavior.
CoSpot: a cooperative VM allocation framework for increased revenue from spot instances

Most large cloud operators offer a lower-priced, lower-priority alternative to regular (on-demand or reserved) virtual machines, commonly referred to as spot instances. Spot instances are opportunistically allocated to servers in order to utilize any residual cloud capacity, but are evicted whenever regular virtual machines need to use that capacity. This paper proposes CoSpot, a lightweight framework for cooperative allocation of regular virtual machines and spot instances, which allows for easy integration of arbitrary virtual machine and spot allocators. In our experiments, employing the framework achieves up to 245% improvement (average 34% improvement) in spot revenue, with no loss in virtual machine revenue, compared to the baseline VM and spot allocation without using our framework. We also derive and release a reusable workload with both virtual machines and spot instances, based on data previously shared by Microsoft Azure.
Artificial Intelligence Supporting Sustainable and Individual Mobility: Development of an Algorithm for Mobility Planning and Choice of Means of Transport 3

Mobility planning is rarely individually tailored. Instead people have to make an active effort to adapt standard solutions to their requirements. Routing apps like Google Maps allow for personalization only by saving important places like home and a workplace but do not allow the user to in ﬂ uence the routing suggestions or choice of mode of transport based on preferences, limitations, or situation. It becomes even more dif ﬁ cult when different means of transport are to interact since most routing applications offer very little multimodal optimization aside from the last mile. Thus, the objective of this article is to present a concept for the utilization of arti ﬁ cial intelligence and regression models in order to enable individual and sustainable mobility planning. To achieve this objective, initially existing routing and mobility planning applications are examined and are conceptually expanded in order to outlay the bene ﬁ ts of personalized route planning. The concrete objective alongside with a method for the development of a new solution is summarized. An algorithm ful ﬁ lling these objectives based on multiple linear regression is conceptualized. Relevant factors with coef ﬁ cient are identi ﬁ ed, as well as necessary data sources and interfaces. This algorithm is then implemented in a limited prototype as a proof of concept. Finally, this prototype is tested based on a set of mobility scenarios in order to validate the achievement of the de ﬁ ned objective.
Detecting Obfuscated Function Clones in Binaries using Machine Learning

—The complexity and functionality of malware is ever-increasing. Obfuscation is used to hide the malicious intent from virus scanners and increase the time it takes to reverse engineer the binary. One way to minimize this effort is function clone detection. Detecting whether a function is already known, or similar to an existing function, can reduce analysis effort. Outside of malware, the same function clone detection mechanism can be used to find vulnerable versions of functions in binaries, making it a powerful technique. This work introduces a slim approach for the identification of obfuscated function clones, called O FCI , building on recent advances in machine learning based function clone detection. To tackle the issue of obfuscation, O FCI analyzes the effect of known function calls on function similarity. Furthermore, we investigate function similarity classification on code obfuscated through virtualization by applying function clone detection on execution traces. While not working adequately, it nevertheless provides insight into potential future directions. Using the A LBERT transformer O FCI can achieve an 83% model size reduction in comparison to state-of-the-art approaches, while only causing an average 7% decrease in the ROC-AUC scores of function pair similarity classification. However, the reduction in model size comes at the cost of precision for function clone search. We discuss the reasons for this as well as other pitfalls of building function similarity detection tooling.
Location Heartbleeding: The Rise of Wi-Fi Spoofing Attack Via Geolocation API

Location spoofing attack deceiving a Wi-Fi positioning system has been studied for over a decade. However, it has been challenging to construct a practical spoofing attack in urban areas with dense coverage of legitimate Wi-Fi APs. This paper identifies the vulnerability of the Google Geolocation API, which returns the location of a mobile device based on the information of the Wi-Fi access points that the device can detect. We show that this vulnerability can be exploited by the attacker to reveal the black-box localization algorithms adopted by the Google Wi-Fi positioning system and easily launch the location spoofing attack in dense urban areas with a high success rate. Furthermore, we find that this vulnerability can also lead to severe consequences that hurt user privacy, including the leakage of sensitive information like precise locations, daily activities, and demographics. Ultimately, we discuss the potential countermeasures that may be used to mitigate this vulnerability and location spoofing attack.
A meta-analysis and trial sequential analysis of high intensity focused ultrasound ablation combined with transhepatic arterial chemotherapy and embolization for hepatoma

Objective The efficacy of High Intensity Focused Ultrasound Ablation(HIFU) combined with Transhepatic Arterial Chemotherapy And Embolization(TACE) versus TACE alone in the treatment of hepatoma was evaluated by meta-analysis and trial sequential analyses(TSA). Methods Pubmed, Cochrane, Embase, Web of Science, Scoups and CNKI, CQVIP, Wanfang Data(China National Knowledge Infrastructure) databases were searched from database construction to April 2022, and randomized controlled trials were included. Revman and Stata software were used for meta-analysis of tumor changes, survival rate, laboratory indicators and adverse reactions in the included studies, and TSA0.9 was used for sequential analysis. Grade Pro was also used to evaluate the included indicators. Results Twelve studies were included with a sample size of 1025 cases. Meta-analysis showed that the tumor response rate in the combined treatment group was 1.54 times higher than that in TACE alone (OR: 2.54; 95%CI:1.81-3.57) and the 6-month to 5-year survival rate was 1-4 times higher, with statistically significant differences (P<0.05). Subgroup analysis showed that country, pathological type and study type were the sources of heterogeneity. Egger results showed that there was no publication bias (95%CI: -1.333, 3.552; Ppublication=0.276), and the sensitivity analysis results were reliable. TSA results suggest that there may be false positive results, which need to be further confirmed by more studies. Grade evaluation results indicated that the quality of evidence for response rate and one-year survival was low. Conclusion HIFU combined with TACE has better efficacy in the treatment of hepatoma, which is worthy of promotion. However, there may be false positive results in this study, which needs to be further verified by more extensive and more tests.
MXene-based flexible sensors: A review

MXenes are an emerging family of two-dimensional (2D) materials which exhibits unique characteristics such as metal-like thermal and electrical conductivity, huge surface area, biocompatibility, low toxicity, excellent electrochemical performance, remarkable chemical stability, antibacterial activity, and hydrophilicity. Initially, MXene materials were synthesized by selectively etching metal layers from MAX phases, layered transition metal carbides, and carbonitrides with hydrofluoric acid. Multiple novel synthesis methods have since been developed for the creation of MXenes with improved surface chemistries using non-aqueous etchants, molten salts, fluoride salts, and various acid halogens. Due to the promising potential of MXenes, they have emerged as attractive 2D materials with applications in various fields such as energy storage, sensing, and biomedical. This review provides a comprehensive overview of MXenes and discusses the synthesis and properties of MXenes, including the methods of etching, delamination, and modification/functionalization, as well as the electrical properties of MXenes. Following this, the recent advances in the development of various MXene-based sensors are presented. Finally, the challenges and opportunities for future research on the development of MXenes-based sensors are discussed.
The role of nanomaterials in enhancing natural product translational potential and modulating endoplasmic reticulum stress in the treatment of ovarian cancer

Ovarian cancer, and particularly its most frequent type, epithelial ovarian carcinoma, constitutes one of the most dangerous malignant tumors among females. Substantial evidence has described the potential of phytochemicals against ovarian cancer. The effect of natural compounds on endoplasmic reticulum (ER) stress is of great relevance in this regard. In ovarian cancer, the accumulation of misfolded proteins in the ER lumen results in decompensated ER stress. This leads to deregulation in the physiological processes for the posttranslational modification of proteins, jeopardizes cellular homeostasis, and increases apoptotic signaling. Several metabolites and metabolite extracts of phytochemical origin have been studied in the context of ER stress in ovarian cancer. Resveratrol, quercetin, curcumin, fucosterol, cleistopholine, fucoidan, and epicatechin gallate, among others, have shown inhibitory potential against ER stress. The chemical structure of each compound plays an important role concerning its pharmacodynamics, pharmacokinetics, and overall effectiveness. Studying and cross-comparing the chemical features that render different phytochemicals effective in eliciting particular anti-ER stress actions can help improve drug design or develop multipotent combination regimens. Many studies have also investigated the properties of formulations such as nanoparticles, niosomes, liposomes, and intravenous hydrogel based on curcumin and quercetin along with some other phytomolecules in ovarian cancer. Overall, the potential of phytochemicals in targeting genetic mechanisms of ovarian cancer warrants further translational and clinical investigation.
Fatal cases involving new psychoactive substances and trends in analytical techniques

New psychoactive substances (NPS) are an emerging public health issue and deaths are commonly associated with polydrug abuse. Moreover, the number of new substances available is constantly increasing, causing intoxications in low doses, characteristics that impose to toxicology and forensic laboratories to keep routine methods up to date, with high detectability and constantly acquiring new analytical standards. Likewise, NPS metabolites and respective elimination pathways are usually unknown, making it difficult the detection and confirmation of the drug involved in the fatal case in an analytical routine. A literature search was performed on PubMed, Scopus and Web of Science databases for papers related to chromatographic analyses from fatal cases related to NPS use published from 2016 to 2021. A total of 96 papers were retrieved and reviewed in this study. Opioids, synthetic cathinones, phenethylamines/amphetamines and cannabinoids were the NPS classes most found in the fatal cases. In many cases, multiple compounds were detected in the biological samples, including prescription and other illegal drugs. Liquid chromatography-tandem mass spectrometry, an alternative to overcome the gas chromatography–mass spectrometry limitations for some compounds, was the analytical technique most used in the studies, and high resolution mass spectrometry was often applied to NPS metabolite investigation and structural characterization and identification of unknown compounds. Toxicological screening and quantitation methods need to be continuously updated to include new substances that are emerging on the drug market that can be fatal at very low doses.
Coupling optical and SAR imagery for automatic garlic mapping

Accurate garlic identification and mapping are vital for precise crop management and the optimization of yield models. However, previous understandings of garlic identification were limited. Here, we propose an automatic garlic mapping framework using optical and synthetic aperture radar (SAR) images on the Google Earth Engine. Specifically, we firstly mapped winter crops based on the phenology of winter crops derived from Sentinel-2 data. Then, the garlic was identified separately using Sentinel-1 and Sentinel-2 data based on the winter crops map. Additionally, multi-source validation data were used to evaluate our results. In garlic mapping, coupled optical and SAR images (OA 95.34% and kappa 0.91) outperformed the use of only optical images (OA 74.78% and kappa 0.50). The algorithm explored the potential of multi-source remote sensing data to identify target crops in mixed and fragmented planting regions. The garlic planting information from the resultant map is essential for optimizing the garlic planting structure, regulating garlic price fluctuations, and promoting a healthy and sustainable development of the garlic industry.