Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning algorithms for the problem of optimizing the distribution of parcels in time-dependent networks: the case study

In the paper we present machine learning algorithms for the problem of optimizing the distribution of parcels in sto-chastic time-dependent networks, which have been built as a part of some Distribution Optimization System. The prob-lem solved was a modified VRPTW (Vehicle Routing Problem with Time Windows) with many warehouses, a heteroge-neous fleet, travel times depending on the time of departure (stochastic time-dependent network) and an extensive cost function as an optimization criterion. To solve the problem a modified simulated annealing (SATM) algorithm has been proposed. The paper presents the results of the algorithm learning process: the calibration of input parameters and the study of the impact of parameters on the quality of the solution (calculation time, transport cost function value) de-pending on the type of input data. The idea is to divide the input data into classes according to a proposed classifica-tion rule and to propose several strategies for selecting the optimal set of calibration parameters. These strategies consist in solving some multi-criteria optimization tasks in which four criterion functions are used: the length of the designated routes, the computation time, the number of epochs used in the algorithm, the number of designated routes. The subproblem was building a network model of travel times that is used in constructed SATM algorithm to determine the travel time between recipients, depending on the time of departure from the start location. An attempt has been made to verify the research hypothesis that the time between two points can be estimated with sufficient accuracy depending on their geographical location and the time of departure (without reference to the micro-scale, i.e. the detailed structure of the road network). The research was conducted on two types of data for Warsaw: from transport companies and one of the Internet traffic data providers. Learning the network model of travel times has produced very promising results, which will be described in the paper.
Stage-Specific Effect of Inositol Hexaphosphate on Cancer Stem Cell Pool during Growth and Progression of Prostate Tumorigenesis in TRAMP Model

Simple Summary The stage of a tumor during cancer intervention is the most crucial factor that determines the treatment regimen. Several bioactive natural compounds have shown potential to inhibit prostate cancer growth and progression; however, there is a dearth of studies that explore their efficacy at different stages of tumorigenesis. This knowledge gap prevents researchers from fully exploiting the anti-cancer potential of these beneficial compounds. Accordingly, our present study focused on explicating the ‘stage-specific’ efficacy of the bioactive food component ‘inositol hexaphosphate (IP6, phytic acid)’ against PCa initiation, growth, and progression in the transgenic adenocarcinoma of the mouse prostate TRAMP model. Results indicated that IP6 feeding during initial stages of cancer development prevents progression of prostatic intraepithelial neoplasia lesions to adenocarcinoma, and IP6 feeding during late stage of the disease reduces tumor growth and prevents its progression to advanced stage of the disease. Thus, IP6 intervention is beneficial during all stages of prostate tumorigenesis. Abstract Herein, we assessed the stage-specific efficacy of inositol hexaphosphate (IP6, phytic acid), a bioactive food component, on prostate cancer (PCa) growth and progression in a transgenic mouse model of prostate cancer (TRAMP). Starting at 4, 12, 20, and 30 weeks of age, male TRAMP mice were fed either regular drinking water or 2% IP6 in water for ~8–15 weeks. Pathological assessments at study endpoint indicated that tumor grade is arrested at earlier stages by IP6 treatment; IP6 also prevented progression to more advanced forms of the disease (~55–70% decrease in moderately and poorly differentiated adenocarcinoma incidence was observed in advanced stage TRAMP cohorts). Next, we determined whether the protective effects of IP6 are mediated via its effect on the expansion of the cancer stem cells (CSCs) pool; results indicated that the anti-PCa effects of IP6 are associated with its potential to eradicate the PCa CSC pool in TRAMP prostate tumors. Furthermore, in vitro assays corroborated the above findings as IP6 decreased the % of floating PC-3 prostaspheres (self-renewal of CSCs) by ~90%. Together, these findings suggest the multifaceted chemopreventive-translational potential of IP6 intervention in suppressing the growth and progression of PCa and controlling this malignancy at an early stage.
Handgrip Strength as a Predictor of Successful Rehabilitation After Hip Fracture in Patients 65 Years of Age and Above

Purpose It is important to identify factors associated with the outcome of rehabilitation after hip fracture as an aid to planning the rehabilitation framework and the future discharge of patients. Previous studies have shown that handgrip strength (HGS) is one of the factors associated with the success of rehabilitation. Materials and Methods A retrospective study among patients 65 years of age and above who underwent surgical repair of a hip fracture followed by rehabilitation in the Geriatrics ward between September 2019 and December 2021. Successful rehabilitation was determined as Montebello Rehabilitation Factor Score Revised (MRFS-R) ≥50%. Associations were assessed between various sociodemographic and clinical variables, including HGS, and rehabilitation success. HGS was tested as a continuous and dichotomous variable, in accordance with various definitions of low muscle strength. Results Data were collected for 173 patients. The mean age was 81.2 ± 7.2 years and 68.2% were women. In a logistic regression model only HGS, as a continuous variable, was independently associated with rehabilitation success, with each increase of 1 kg increasing the chance for successful rehabilitation by 6.8%. Conclusion HGS is a simple tool for the planning of the rehabilitation process among patients with hip fracture.
A Context-Aware Method-Based Cattle Vocal Classification for Livestock Monitoring in Smart Farm

: This paper focuses on livestock monitoring in smart farm to improve animal well-being and production. The great potential for increased automation and technological innovation in agriculture help livestock farmers in monitoring the welfare of their animals for precision livestock farming. A new acoustical method exploiting contextual information is introduced for cattle vocal classification. The proposed scheme considers the raw recordings which contain cattle sounds. Then a set of contextual acoustic features is constructed as input to the MSVM classifier to track the types of cattle vocalizations. Categorized noisy cattle calls are finally classified into four types of calls (i.e. cattle food anticipating call, animal estrus call, cough sound, and normal call) with an overall classification accuracy of 84% outperforming the results obtained using conventional MFCC features. We have used an open access dataset consists of 270 cattle classification records acquired using multiple sound sensors. Promising results are obtained by the proposed method for livestock monitoring enabling farm owners to determine the status of their cattle.
Neural-Network-Based Automated Synthesis of Transformer Matching Circuits for RF Amplifier Design

Rich experience and intuition play important roles in designing planar transformers (TFs) for contemporary radio frequency integrated circuits (RFICs). In general, RFIC designers have been heavily relying on multiple iterations of full electromagnetic (EM) simulations, which consumes much time and effort. Here, we propose an automated matching circuit synthesizer (AMCS) using neural networks (NNs). The proposed AMCS directly synthesizes a matching circuit combined with a TF throughout the entire design process, ranging from the desired performance to layout. In the AMCS, which is a “spec-to-layout” synthesizer, one NN returns physical parameters of matching circuits, and another NN estimates the electrical performance in two-port $S$ -parameters from the desired impedances. Before the NNs are trained, input feature design is conducted to avoid the one-to-many problem, which cannot be well characterized with an inverse NN. This significantly reduces the time and effort for iterative circuit and EM simulations. The AMCS generated the matching circuit layouts for simple single-stage amplifiers operating at different frequencies up to 70 GHz or in different bandwidths of up to 32.5%. The estimated $S$ -parameters of the amplifiers show good agreement with the EM simulation results.
Effects of carbon dioxide exposure on human cognitive abilities in an enclosed workplace environment

In this study, fifteen participants were exposed in an enclosed environmental chamber to investigate the effects of elevated carbon dioxide (CO2) concentration on their cognitive abilities. Three CO2 conditions (1500 ppm, 3500 ppm, and 5000 ppm) were achieved by constant air supply and additional ultra-pure CO2. All participants received the same exposure under each condition, during which they performed six cognitive tests evaluating human perception, attention, short-term working memory, risky decision making, and executive ability. During each exposure condition, the reaction time (RT) test, speed perception test, visual search (VS), 2-back test, balloon analogue risk test (BART), and Stroop test were successively conducted with rest intervals of approximately 13min. The results showed that there was no statistically-significant difference in the performance of RT tests, speed perception test, and 2-back test. This indicated that the elevated CO2 concentration below 5000 ppm did not affect the participants’ perception and short-term working memory. However, significant increase of response time was observed in the VS test, BART, and Stroop test at 5000 ppm compared with lower exposure concentrations. The slower reaction reflected the negative effects of elevated CO2 concentration on human visual attention, risky decision making, and executive ability. The findings suggest that work performance could be negatively affected by the exposure to CO2 at the current occupational concentration limit.
Different Phenotypes in Monozygotic Twins, Carriers of the Same Pathogenic Variant for Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a monogenic disease with autosomal dominant inheritance. Genotype–phenotype relationships are complex, with variable penetrance even within the same family. The involvement of other modulating genetic and environmental factors is unknown. We aimed to analyze the HCM in monozygotic twins, carriers of the same founder pathogenic variant MYBPC3 p.G263*. The relationship was verified using the PowerPlex 16 HS System kit. Phenotypic differences and environmental differences (overloading conditions, coexistence and location, lifestyle, sport, and intensity) were analyzed. Three pairs of twins genetically identical for all markers and carriers of MYBPC3 G263* were identified. No environmental differences were identified. One of the 89-year-old twins had symptomatic severe obstructive HCM that required septal ablation, while her twin has remained asymptomatic with mild phenotype >80 years. A 49-year-old twin had a severe phenotype of obstructive HCM and pending myectomy, while his twin had a mild asymptomatic phenotype. In the last pair of twins, one presented a much larger left ventricular hypertrophy than his identical twin. In summary, we present three pairs of HCM twin patients sharing not only the genetic cause of the inherited disease but the entire genetic background. Despite identical genetic information and the absence of other known clinical, environmental, or lifestyle differences, the severity of the HCM phenotype is strikingly different. These unexplained differences should prompt the study of other unknown modulating factors, either epigenetic or environmental.
A novel method to study the fragmentation behavior of marine snow aggregates in controlled shear flow

The sinking of marine aggregates impacts the global carbon cycle as it is one of the primary sources of carbon export from the surface ocean. Aggregation and fragmentation alter the size of aggregates, which governs their sinking speed. Although aggregation theory is well established, aggregate fragmentation strength and breakup characteristics are less well understood. This study developed a cylindrical tank that formed and then exposed diatom aggregates to calibrated laminar shear through a combined rotating and oscillating motion. The shear rate was predictable using a developed analytical solution for the fluid motion within the tank. Under proper operating conditions, this facility provided fluid shear with a similar magnitude to that in multiple ocean environments. We also developed a unique image processing method that enabled continuous tracking of particles' position, size, and morphology as well as determination of the individual aggregate breakup events. The method has great potential to capture breakup events of large marine snow particles, quantify the aggregate morphological changes leading up to and at breakup, and provide data sufficient for statistical analysis of laboratory aggregate populations. We tested the method using laboratory‐cultured Odontella aurita and captured 79 breakup events of the resulting aggregates. These fragmented aggregates ranged from 1 to 5 mm in major axis length and underwent substantial morphological evolutions prior to the fragmentation.
Stable Contrastive Learning for Self-Supervised Sentence Embeddings With Pseudo-Siamese Mutual Learning

Learning semantic sentence embeddings is beneficial to a variety of natural language processing tasks. Recently, methods using the contrastive learning framework to fine-tune pre-trained language models have been proposed and have achieved significant performance on sentence embeddings. However, sentence embeddings are easy to “overfit” to the contrastive learning goal. With the training of contrastive learning, the gap between contrastive learning and test tasks leads to unstable even declining performance on test tasks. For this reason, existing methods rely on the labeled development set to frequently evaluate the performance on test tasks and get the best checkpoints. In such a way, models are limited when the labeled data is unavailable or extremely scarce. To address this problem, we propose Pseudo-Siamese network Mutual Learning (PSML) for self-supervised sentence embeddings to reduce the gap between contrastive learning and test tasks. Consisting of the main encoder and the auxiliary encoder, PSML utilizes mutual learning as the basic framework. Between the two encoders, two mutual learning losses are constructed to share learning signals. The proposed model framework and losses of PSML help the model be optimized more stably and generalize better to test tasks, such as semantic textual similarity. Extensive experiments on seven public semantic textual similarity datasets show that PSML performs better than previous unsupervised contrastive methods for sentence embeddings. Besides, PSML also gives a stable performance curve on test tasks with training and is able to get the comparative performance without frequent evaluation on the labeled development set.
Complete mitochondrial genome of the Japanese Cormorant Phalacrocorax capillatus (Temminck & Schlegel, 1850) (Suliformes: Phalacrocoracidae)

Abstract The complete sequencing of mitochondrial DNA of the Japanese Cormorant Phalacrocorax capillatus was performed using long PCR and primer walking methods. The assembled genome was 19,105 bp in length. It contained 13 protein-coding genes, two ribosomal RNA genes, 22 transfer RNA genes, and two control regions. The phylogenetic analysis using the obtained sequence showed that P. capillatus is closest to P. carbo.
The effect of surface friction on the mesoscale organization of cumulus convection in tropical cyclones

In order to study the effect of surface friction on the properties of mesoscale convection which constitute the rainbands of tropical cyclones, numerical experiments are performed with an axi-symmetric non-hydrostatic model. As discussed by Yamasaki (1983), when the tangential wind velocity is not very strong (10 ms -1 or less), the typical time scale of mesoscale convection is a few hours. When the tangential wind intensifies and frictional inflow becomes very significant, the lifetime of mesoscale convection becomes 11-12 hours. In contrast to the convection with time scale of a few hours, it does not decay owing to an outward shift of low-level convergence associated with the intensification of cold downdraft outflow. Its decay is caused by its inward shift due to strong frictional inflow. In this situation, rainwater falling from the cloud, which tilts outward with height at this stage, acts to prevent the low-level warm moist air from flowing into the cloud. A new mesoscale convection is formed at some distance from the old one. Structure, time evolution and the mechanism of the mesoscale convection with the longer lifetime are discussed in detail.
Wintertime Variation in Carbonaceous Components of PM10 in the High Altitudes of Himalayas

: Carbonaceous aerosols play a significant role in the Earth’s atmospheric system by affect-ing the visibility, hydrological cycle, climate, radiative forcing and human health. The present study analyses the PM 10 samples collected at three distinct urban locations (Mohal-Kullu, Nainital and Darjeeling) over the Himalayan region of India during winter 2019. The mass concentration of PM 10 were recorded as 51 ± 16 μg m −3 , 38 ± 9 μg m −3 and 52 ± 18 μg m −3 for Mohal-Kullu, Nainital and Darjeeling, respectively. Organic carbon (OC) dominated over elemental carbon (EC) and was found to be 50.2%, 42.8 and 47% of total carbon (TC) at Mohal-Kullu, Nainital and Darjeeling, re-spectively. The respective mass concentrations of carbonaceous species were higher at Mohal-Kullu (OC: 11.1 ± 5.3, EC: 4.2 ± 1.9, WSOC: 5.3 ± 1.3 μg m −3 and TCA: 22.1 ± 10.4 μg m −3 ) followed by Darjeeling (OC: 5.4 ± 2.0, EC: 2.7 ± 1.0, WSOC: 3.9 ± 1.3 μg m −3 and TCA: 22.1 ± 10.4 μg m −3 ) and Nainital (OC: 2.9 ± 1.0, EC: 1.3 ± 0.6, WSOC: 2.1 ± 0.6 μg m −3 and TCA: 6.7 ± 2.4 μg m −3 ). The OC/EC and WSOC/OC ratio at Mohal- Kullu (2.6 ± 0.3, 0.6 ± 0.2), Nainital (2.0 ± 0.4, 0.7 ± 0.2) and Darjeeling (2.3 ± 0.5, 0.7 ± 0.2), respectively, indicates the dominance of fossil fuel combustion (coal and vehic-ular exhaust), with signified additional contribution from secondary organic carbon (SOC).
Design and electrical properties’ investigation of (K0.5Na0.5)NbO3–BiMeO3 lead-free piezoelectric ceramics

Based on the discussion on the “origin” of the high piezoelectric properties of Pb-based piezoelectric ceramics, it was predicted that (K0.5Na0.5)NbO3–BiMeO3 solid solutions (where Me3+=Sc, Al, Ga, Y, In, etc.) should possess high piezoelectric properties because of the formation of the morphotropic phase boundary and the hybridization between the Bi 6p and O 2p orbits. (1−x)(K0.5Na0.5)NbO3–xBiScO3 [(1−x)KNN-xBS] ceramics were selected as an example to verify this prediction. (1−x)KNN-xBS ceramics were synthesized by conventional solid-state sintering. The phase structure, microstructure, and dielectric and piezoelectric properties of (1−x)KNN-xBS ceramics were investigated. At room temperature, the polymorphic phase transition (PPT) (from the orthorhombic to the tetragonal phase) in (1−x)KNN-xBS ceramics is identified at x=0.0175 by the analysis of x-ray diffraction patterns and dielectric spectroscopy. The ceramics (x=0.0175) with PPT near room temperature exhibit excellent electrical properties (d33=∼2.
Importance of Integrating Traditional Physical Education into Physical Education Teaching Using Big Data

In order to advance the assessment capability of the significance of integrating old-fashioned physical education into school physical education, an evaluation model of the significance of integrating old-fashioned physical education into school physical education founded on big data analysis of influencing factors of physical education quality is put forward. The big data analysis model of the significance of integrating old-fashioned physical education into school physical education was established, and the constraint parameter model was adopted to evaluate the significance of integrating old-fashioned physical education into school physical education by the method of objective elements collection of physical education teaching effect. Combined with the quantitative statistical analysis method of structural parameters of physical education system, the important parameter analysis model of integrating classical physical education into institution physical education was realized, and the inversion feature quantity of the significance evaluation of integrating classical physical education into institution physical education was extracted. Combining the questionnaire analysis method of influencing factors of institution physical education teaching quality, this paper realizes the piecewise linear estimation of the significance of classical physical education in institution physical education teaching, adopts the joint feature analysis method of quantitative rules, combines the quantitative analysis of indicators and the confidence test results of the significance of classical physical education in institution physical education teaching, and therefore realizes the fuzzy assessment of the significance of classical physical education in institution physical education teaching by fuzzy decision method. We realize the comprehensive evaluation decision of the significance of integrating classical sports into institution physical education. According to the big data mining results of the significance evaluation of integrating classical sports into institution physical education, realize the decision evaluation of the significance of integrating classical sports into institution physical education. The test outcomes express that this approach has good adaptability in evaluating the significance of integrating old-fashioned physical education into school physical education, and the confidence level of the evaluation results is higher than the other approaches.
Systematic Survey on Visually Meaningful Image Encryption Techniques

Due to advancements in technology, digital images are widely used in various applications like medical field, military communication, remote sensing, etc. These images may contain sensitive and confidential information. Therefore, images are required to be protected from unauthorized access. Many image protection techniques have been proposed in past years. The most common technique to protect the images is encryption. In this technique, a secret key and an encryption algorithm are used to change the plain image into an encrypted image. The encrypted image looks like a noisy image and can easily attract the attacker’s attention. If an image gets captured and stacked, sensitive information can be revealed. In this regard, Visually Meaningful Encrypted Image (VMEI) technique is developed, which initially encrypts the original image and then hides it into a reference image. The final encrypted image looks like a normal image. Hence, the VMEI technique provides more security as compared to simple image encryption techniques. Therefore, a systematic survey of existing VMEI techniques is presented in this paper. The VMEI techniques are divided into different categories based on their characteristics. Moreover, this paper elaborates and investigates the improvements and analyses performed on VMEI techniques based on various evaluation parameters. These evaluation parameters are divided into different categories such as security attacks, encryption key attacks, quality analysis, and noise attacks. Finally, this paper discusses the potential applications and future challenges of VMEI techniques.