Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal replenishment and transshipment management with two locations

We study the problem of optimally managing an inventory system with backorders over a finite time horizon where the objective is minimization of expected total discounted costs. The system consists of two locations each stocking the same product. At the beginning of each time period decisions are made about replenishment at each location and about any quantity to transship between the locations before demand is observed. Leveraging the L♮‐convexity of the problem's cost function; we characterize the optimal replenishment and transshipment policy for this system. More specifically, we show the optimal policy can be described using switching curves monotone in the system state. We also discuss two extensions. For a lost‐sales model, we establish L♮‐convexity and apply it to characterize the optimal policy, simplifying the analysis found in previous work. In the other extension, we investigate the optimal policy for a partial transshipment problem where only one location orders from the external supply source and then transships to the other location.
The relationship between children’s fear of crime and pedestrian volume in school zones

ABSTRACT Focusing on reducing the fear of crime among elementary school students, this study examined the relationship between pedestrian volume and fear of crime in elementary school zones. For this, we i) identified why and where in the school zones students fear crime, by conducting a survey among fifth and sixth grade students of two elementary schools; ii) calculated the volume of pedestrian traffic across the school zones through local integration analysis of axial map, and iii) determined the relationship between fear of crime and pedestrian volume and the difference in patterns of fear of crime perceived by the students depending on the pedestrian volume. The results indicated a higher prevalence of fear of crime in places with low and high pedestrian volumes compared with those with an average volume. Notably, in high pedestrian traffic areas, despite improvement in natural surveillance, the major determinants of high fear of crime were human factors, such as strangers and peers who bully. The findings will help architects, building engineers, and urban planners effectively plan and design schools and public facilities in school zones. Moreover, it will enable policy makers develop effective and accurate guidelines regarding positioning patrol personnel or surveillance.
Mainlobe Interference Suppression for Radar Network via RPCA-Based Covariance Matrix Reconstruction

Radar networks can provide spatial diversity by observing targets from different perspectives with adequately separated receiving stations. This article proposes a mainlobe interference suppression approach for radar networks based on covariance matrix reconstruction. First, we formulate the mainlobe interference suppression under multiradar configuration as a multichannel signal enhancement problem and perform range and Doppler equalization to improve interferences’ spatial correlation. Then, we estimate the signal and interference covariance matrix from the sampling covariance matrix by solving a robust principal component analysis (RPCA) model. After that, we devise a time-domain-constrained (TDC) linear estimator dependent on the reconstructed covariance matrices to enhance target echo. To remove those duplicate false targets caused by signal enhancement and estimate the state of the physical targets, we establish and solve a generalized multiple measurement vector (GMMV) model based on the fact that targets have unique locations in the state space. Finally, we confirm the effectiveness of the proposed approach with numerical experiments and demonstrate better performance in comparison with the subspace projection method.
Speeding up the interpretation of differential gene expression analysis results

The existing methods for interpreting the differential gene expression analysis results are mainly divided into three categories: cluster analysis, enrichment analysis, and the construction of genetic networks. Despite the rich abilities, all approaches take a lot of time to compute, and the final results are not always sufficient for understanding of the logic that binds genes into groups.In this paper, we propose a complete pipeline in order to make the process of understanding the results of differential gene expression analysis much faster, easier, and more efficient. The pipeline takes in Gene Ontology terms along with descriptions of collected genes, and returns the output of gene clusters, topics they are related to, and a filtered list of most common words that can be found in each of them. The processing involves an artificial neural network model BERT for semantic information extraction, BERTopic for unsupervised topic extraction, dimensional reduction for data simplification, and clustering for the search of dependencies.The pipeline was tested with ablation study and its performance was evaluated by an expert with gene expression datasets from NCBI GEO that include different types of cardiomyopathy: dilated, inflammatory, ischemic, non-ischemic, and healthy individuals.
The Benefit of Epidural Transforaminal Injection of Ozone in Comparison With Transforaminal Steroids Injection in the Management of Chronic Low Back Pain in Lebanese Population: One-Year Retrospective Study

Background Chronic low back pain (CLBP) is one of the most common complaints affecting the population worldwide including in Lebanon. Until 15 years ago, surgery was the treatment of choice. However, conservative measures are now preferred because of the large number of post-surgical complications, in addition to the many conditions where surgery cannot be performed. Objective The aim of our study is to determine the effectiveness of transformational epidural injection of ozone (TFEOI) in the management of CLBP among the Lebanese population in the Nabatieh area in comparison with patients who received transformational epidural steroid injection (TFESI). Methods A one-year (2016-2017) retrospective study where 100 patients with CLBP were selected from two hospitals (Alnajdah, and Ragheb Harb hospitals) and divided into two groups. Fifty patients were treated with Ozone injections while the other 50 were treated with steroid injections. For each patient, we recorded the type of pain, irradiation, paresthesia, and the type of injection given (steroid or Ozone). We used the patients' files and contacted them via phone calls. The results of this study were reached based on Vas Score and Mac Nab criteria which are subjective questionnaires. Results The study showed that the TFESI was effective for a short duration (86% of results were excellent and good after one month of injection, but they decreased to 16% after six months). On the other hand, TFEOI was effective over both short and long duration (82% excellent and good after one month, 64% excellent and good after six months). Conclusion Results from this study provide that ozone injection has high benefits in the management of CLBP in the Lebanese population.
Proteolytic Biosensors with Functional Nanomaterials: Current Approaches and Future Challenges

Proteolytic enzymes are one of the important biomarkers that enable the early diagnosis of several diseases, such as cancers. A specific proteolytic enzyme selectively degrades a certain sequence of a polypeptide. Therefore, a particular proteolytic enzyme can be selectively quantified by changing detectable signals causing degradation of the peptide chain. In addition, by combining polypeptides with various functional nanomaterials, proteolytic enzymes can be measured more sensitively and rapidly. In this paper, proteolytic enzymes that can be measured using a polypeptide degradation method are reviewed and recently studied functional nanomaterials-based proteolytic biosensors are discussed. We anticipate that the proteolytic nanobiosensors addressed in this review will provide valuable information on physiological changes from a cellular level for individual and early diagnosis.
Ensemble-based Intrusion Detection for Internet of Things Devices

Security, privacy, and interoperability challenges have arisen as the Internet of Things (IoT) devices proliferate and become increasingly connected. IoT devices have resource constraints such as computational capabilities, power consumption, onboard storage, and network bandwidth, which limit the implementation of cryptographic solutions. The heterogeneous nature of IoT devices makes them an avenue for attackers to exploit threats like spoofing, routing, MITM, and DoS attacks. With the current sophistication of threats IoT devices are subjected to, an Intrusion Detection System (IDS) is the preferred solution for IoT devices. An IDS continuously monitors incoming traffic and discovers potential threats in incoming and outgoing traffic. This research proposes a novel intelligent ensemble-based IDS that will reside in the IoT gateway. The uniqueness of our approach lies in an ensemble learning approach that combines multiple machine learning methods to improve prediction performance and detection accuracy. Ensemble learning has been studied to increase the detection rate while obtaining better generalization performance due to combining several Machine Learning (ML) models, also known as base learners. Three popularly known ensemble models (i.e., boosting, stacking, and voting) are employed to assess our proposed IDS performance. The proposed method use algorithms such as Naïve Bayes (NB), Support Vector Classification (SVC), and $k$-Nearest Neighbors ($k$ NN). Lastly, the proposed approach will be evaluated on two publicly available datasets; Intrusion Detection Evaluation Dataset (CIC-IDS2017) and N-BaIoT.
An automatic speech recognition system using time‐delays self‐organizing maps with physiological parametric extraction

Physiological parametric extraction uses auditory models as a front end for speech recognition. These last methods assume that if speech signals are coded in the same way that the auditory system does, speech could be later identified showing the main properties that biological systems do: robustness and accuracy. The proposed system consists of a cochlear model implemented by gammatone filterbanks as proposed by Patterson [J. Acoust. Soc. Am. 96, 1409–1418 (1994)]. This stage will feed a nonlinear mechanical‐to‐neural transduction module based on the Meddis hair‐cell model [J. Acoust. Soc. Am. 79, 702–711 (1986)], which will compute auditory‐nerve firings. Finally, a temporal integration/component–extraction module will integrate neural patterns for identifying the relevant components embedded in the speech signals [characteristic frequency (CF), frequency modulation (FM) and noise burst (NB)], which are shared by human speech and animal sounds for communication. The model adopts a spatiotemporal strateg.
S-Palmitoylation of Synaptic Proteins in Neuronal Plasticity in Normal and Pathological Brains

Protein lipidation is a common post-translational modification of proteins that plays an important role in human physiology and pathology. One form of protein lipidation, S-palmitoylation, involves the addition of a 16-carbon fatty acid (palmitate) onto proteins. This reversible modification may affect the regulation of protein trafficking and stability in membranes. From multiple recent experimental studies, a picture emerges whereby protein S-palmitoylation is a ubiquitous yet discrete molecular switch enabling the expansion of protein functions and subcellular localization in minutes to hours. Neural tissue is particularly rich in proteins that are regulated by S-palmitoylation. A surge of novel methods of detection of protein lipidation at high resolution allowed us to get better insights into the roles of protein palmitoylation in brain physiology and pathophysiology. In this review, we specifically discuss experimental work devoted to understanding the impact of protein palmitoylation on functional changes in the excitatory and inhibitory synapses associated with neuronal activity and neuronal plasticity. The accumulated evidence also implies a crucial role of S-palmitoylation in learning and memory, and brain disorders associated with impaired cognitive functions.
Contribution to Bridge Damage Analysis

Structural damage detection has become an important research area since several works [2] were focused on the crack zones detection in order to foresee the appropriate solutions. The present research aims to carry out the reinforced concrete bridge damage detection with the finite element mathematical model updating method (MMUM). Unknown degrees of freedom dof are expanded from measured ones. The partitioned system of equations has provided a large sub-system of equations which can be solved efficiently by handling sparse matrix algorithms at each time step of the finite time centered space FTCS discretization. A new and efficient method for the calculation of the constant strain tetrahedron shape functions has been developed [1,3,4,5,6]. The topological and analytical geometry of the tetrahedron and its useful formulae enabled us to develop its shape functions and its corresponding finite element matrices. The global finite element matrices and sparse matrix computations have been achieved with a calculus source code. The reinforced concrete mixture has been modeled with the mixture laws [16] which led to its material properties matrix as an orthotropic case with 9 constants and 2 planes of symmetry from the generalized Hooke’s law [1]. It is noticed that the material is made of steel, cement, gravels, sand and impurities. The data computations have been implemented with optimized cpu time and data storage using vectorial programming of efficient algorithms [11,12]. The sparse matrix algorithms used in this study are: solution of symmetric systems of equations UTDUd=R, multiplication, addition, transposition, permutation of rows and columns, and ordering of the matrices representations. All the sparse matrices are given in row-wise sparse format.
Urinary Risk Profile, Impact of Diet, and Risk of Calcium Oxalate Urolithiasis in Idiopathic Uric Acid Stone Disease

The role of diet in the pathogenesis of uric acid (UA) nephrolithiasis is incompletely understood. This study investigated the effect of dietary intervention on the risk of UA stone formation under standardized conditions. Twenty patients with idiopathic UA stone disease were included in the study. Dietary intake and 24 h urinary parameters were collected on the usual diet of the patients and a standardized balanced mixed diet. Although urinary UA excretion did not change, the relative supersaturation of UA decreased significantly by 47% under the balanced diet primarily due to the significant increase in urine volume and pH. Urinary pH was below 5.8 in 85% of patients under the usual diet, and in 60% of patients under the balanced diet. The supersaturation of calcium oxalate declined significantly under the balanced diet due to the significant decrease in urinary calcium and oxalate excretion and the increase in urine volume. Dietary intervention is a key component in the management of UA nephrolithiasis. Urinary calcium and oxalate excretion should also be monitored in patients with pure UA calculi to reduce the risk of mixed stone formation with calcium oxalate. Lower urinary pH in UA stone patients can only be partially explained by diet.
Functioning of People with Lipoedema According to All Domains of the International Classification of Functioning, Disability and Health: A Scoping Review

Lipoedema is a painful non-pitting diffuse “fatty” swelling, usually confined to the legs, that occurs mainly in women. This scoping review aimed to provide an overview of the available research on the functioning of people with lipoedema, according to the International Classification of Functioning, Disability and Health (ICF) framework. Relevant publications and gray literature were retrieved until October 2022. The results sections of each publication were organized using a thematic framework approach. All included studies reported at least one outcome fitting within the domains of body functions and body structures, with most studies focusing on the categories of “sensation of pain”, “immunological system functions”, and “weight maintenance functions”. The ICF domains of activities and participation and environmental factors were mentioned in a small number of the included studies (17 and 13%, respectively), while the domain of personal factors was studied in half of the included studies. In conclusion, the emphasis of lipoedema research is on its description from a disorder-oriented point of view in the form of body functions and body structures, with a lack of information about the other domains of functioning.
Synthesis, Crystal, and Electronic Structure of (HpipeH2)2[Sb2I10](I2), with I2 Molecules Linking Sb2X10 Dimers into a Polymeric Anion: A Strategy for Optimizing a Hybrid Compound’s Band Gap

In searching for a tool for optimizing the band gap of a hybrid compound capable of serving as a light-harvesting material in lead-free photovoltaics, we synthesized a new polyiodoantimonate (HpipeH2)2[Sb2I10](I2) and analyzed its crystal and electronic structure by application of X-ray crystal structure analysis, Raman and diffuse reflectance spectroscopies, and quantum chemical calculations. It was demonstrated that I2 molecules link Sb2I10 edge-sharing octahedra into zig-zag chains, whereas the organic cations link inorganic anionic chains into a 3D structure featuring a complex pattern of covalent bonds and non-covalent interactions. Overall, these features provide the background for forming the electronic structure with a narrow band gap of 1.41 eV, therefore being a versatile tool for optimizing the band gap of a potential light-harvesting hybrid compound.
Carrier mobility, structural order, and solar cell efficiency of organic heterojunction devices

The loss mechanisms limiting solar cell efficiency in organic heterojunction photodiodes are discussed. We show that the energy loss when the exciton is split, and absence of Langevin recombination at the interface, are related to the magnitude of the electron-phonon interaction and the wavefunction extent of the electron and hole. In contrast to several recent publications, it is argued that well-ordered, high mobility organic materials will provide higher efficiency cells.
Penetration of energetic neutral beams into fusion plasmas

The efficiency of neutral beam heating and current drive depends crucially on the deposition of the energy and momentum of the beam in the plasma. This deposition is determined by the atomic processes involved in the stopping (or effective ionization) of the neutral beam atoms. These processes have been studied in detail for the energy range from 10 keV/u to 10 MeV/u. The processes considered include both the ground state and the excited state of the beam atoms, thus allowing for the multistep ionization of the beam in collisions with the plasma constituents and impurities. The effective beam stopping cross-section has been calculated for a wide variety of beam and plasma parameters. The atomic database necessary for these calculations has been documented using the best data available at present. The stopping cross-section data are also given in terms of a convenient analytic fit, which can be used either in computer calculations or for simpler analytic estimates of neutral beam penetration.
Advanced Color Filter Isolation Technology for Sub-Micron Pixel of CMOS Image Sensor

In this work, novel color filter isolation technology, which adopts air, the lowest refractive index material on the earth, as a major component of an optical grid structure for submicron pixels of a CMOS image sensor, is presented. Metal in a conventional metal grid structure, which has been widely used for mobile CMOS image sensors, was replaced by the air. The image quality improvement was verified through the enhanced optical performance of the air-grid-assisted pixels.
Factors Associated with Spontaneous Clearance of Recently Acquired Hepatitis C Virus among HIV-Positive Men in Brazil

Introduction: The objective of the present study was to describe the clinical and epidemiological aspects of recently acquired hepatitis C virus (HCV) infection and the frequency of its spontaneous clearance in a people living with the human immunodeficiency virus (PLWH) cohort. Methods: We reviewed the medical records from all PLWH at the human immunodeficiency virus (HIV) outpatient reference clinic affiliated with the University of São Paulo, Brazil, and identified, by immunoassays and RNA-PCR individuals who acquired HCV infection between January 2015 and December 2017. The factors associated with subsequent spontaneous clearance of the infection in this group were identified and analyzed. Results: Among 3143 PLWH individuals, 362 (11.5%) were coinfected with HCV. Forty-eight (13.2%) of these subjects first became HCV-positive between January 2015 and December 2017. Spontaneous HCV clearance was documented in 23 individuals (47.9%). The majority of this latter group were male (83.3%), and the median age was 31 years (23–39). The main risk group for HCV acquisition was men who had sex with men (MSM) (89.5%). In a multivariate analysis, only an elevated CD4+ T lymphocyte count at the time of seroconversion was found to be associated with subsequent HCV clearance (p = 0.025). Conclusions: In HIV-infected individuals in Sao Paulo, Brazil, most cases of recent HCV transmission were by sexual exposure. In PLWH, particularly in MSM, the individual’s CD4+ T lymphocyte count is a determinant of whether an acquired HCV infection will be prolonged or will spontaneously clear.
The disintegration of wave trains on deep water Part 1. Theory

The phenomenon in question arises when a periodic progressive wave train with fundamental frequency ω is formed on deep water—say by radiation from an oscillating paddle—and there are also present residual wave motions at adjacent side-band frequencies ω(1 ± δ), such as would be generated if the movement of the paddle suffered a slight modulation at low frequency. In consequence of coupling through the non-linear boundary conditions at the free surface, energy is then transferred from the primary motion to the side bands at a rate that, as will be shown herein, can increase exponentially as the interaction proceeds. The result is that the wave train becomes highly irregular far from its origin, even when the departures from periodicity are scarcely detectable at the start. In this paper a theoretical investigation is made into the stability of periodic wave trains to small disturbances in the form of a pair of side-band modes, and Part 2 which will follow is an account of some experimental observations in accord with the present predictions. The main conclusion of the theory is that infinitesimal disturbances of the type considered will undergo unbounded magnification if \[ 0 < \delta \leqslant (\sqrt{2})ka, \] where k and a are the fundamental wave-number and amplitude of the perturbed wave train. The asymptotic rate of growth is a maximum for δ = ka.
Retention Improvement in Vertical NAND Flash Memory Using 1-bit Soft Erase Scheme and its Effects on Neural Networks

We propose a selective 1-bit soft erase scheme in vertical NAND (V-NAND) flash memory that improves retention characteristics. Selective 1-bit erase using gate-induced drain leakage (GIDL) is applied after program operation to remove shallowly trapped electrons. Compared to conventional methods, the proposed method improves retention characteristics by ~40% and the distribution of $\Delta V_{\text {th}}$ is narrowed to less than 30%. The proposed $V_{\text {th}}$ tuning scheme accurately adjusts $V_{\text {th}}$ to the target $V_{\text {th}}$, thereby improving the error rate of convolutional neural networks (CNNs) for image classification by 17% by adopting the 1-bit soft erase scheme.