Miracle-dz/UniFAR-FTUs huggingface dataset:
Female Managers’ Experiences of Work-family Enrichment Realization: A Grounded Theory Study

Background: Challenges created by the simultaneous management of family and work among working women, especially married female managers with children are serious issues affecting all human societies, including Iran. Accordingly, the present study aimed to explore the realization of work-family enrichment based on the experiences of married female managers working in Parsian Bank. Methods: This qualitative study was conducted using the grounded theory approach. The study participants were 22 female managers who were selected using purposive and theoretical sampling methods from the staff of Parsian Bank, in 2019. The necessary data were collected by semi-structured interviews. The collected data were analyzed using the three-step (open, axial, selective) coding procedure (Strauss & Corbin, 1998). The collected data were coded and categorized using the constant comparison method in MAXQDA. Results: The obtained results indicated that female managers employed several strategies, including instrumental strategies, problem-solving management strategies, emotion-related strategies, behavioral measures, and measures to improve personal development and growth for work-family enrichment. The factors facilitating work-family enrichment were self-efficacy, emotional intelligence, support, and environmental network. However, other factors, including socio-cultural characteristics, the challenges of family cultural context, the non-supportive culture of the organization, and the cultural challenges of society were barriers for work-family enrichment. This process led to job satisfaction and satisfaction with marital life among the examined female managers. The core variable manifested as “the multidimensional use of self to enrich work and family”. Conclusion: The insights from this study added to the knowledge of work-family enrichment among female managers. Female managers and officials can use this model to better understand and manage family and work, concurrently.
Distributed Quantized Consensus With Recursive Channel Filters Over Directed Networks

This brief investigates the quantized consensus in discrete-time multi-agent systems over directed networks with limited bandwidths of communication channels and limited capacities of physical memories. A dynamic consensus protocol based on the first-order recursive channel filter is proposed. Several conditions for preserving the asymptotic consensus are obtained, which are dependent on the filter parameters, the sampling period and the topologies of the communication networks. Then, by describing the closed-loop multi-agent system via a higher-order state-space model, the effects of the probabilistic quantization are analyzed. We prove that the quantized consensus is reached under the proposed protocol with quantized communications. Moreover, the distributed PI based protocol is discussed as a special case of the proposed dynamic protocol.
Optimal Design of a Standalone Hybrid Energy System for a Covid-19 Quarantine Center Energization

This work is motivated by the current situation in the world in particular the spread of COVID-19. Building quarantine centers is one of the most important means used in combating the Corona epidemic, but connecting these centers to the electricity distribution network at the appropriate time is not always possible. This paper proposed a Hybrid off-grid Energy System (HES) to effectively energies the quarantine centers economically and environmentally. To achieve this aim, the load profile of the quarantine center is estimated, the system components are modeled and the system design is optimized. In addition, the developed approach was tested using a real case study considering realistic input data. HOMER-pro program is used to simulate and optimize the system design. The results revealed the potential of the HES to provide environment-friendly, cost-effective, and affordable electricity for the studied quarantine center, as compared to only diesel generators system. For the considered case study, it is found that the PV-Wind-Diesel hybrid power system is able to cover the connected load with the lowest cost in comparison to other possible HES structures. It also environmentally friendly as it has the least harmful emissions. Finally, it is proved that the developed approach gives a reasonable solution to the decision-makers to find a fast, economic and reliable solution to energies the quarantine centers.
IoT Malware Classification Based on Lightweight Convolutional Neural Networks

Internet of Things (IoT) is hard to deploy adequate security defenses due to the diversity of architectures as well as the limited computing and storage capabilities, which makes it more vulnerable to malware. With the massive deployment of IoT devices, how to accurately identify and classify the malware variants is crucial to IoT security. However, existing methods of IoT malware classification generally support specific platform or require complex models to achieve higher accuracies. To solve these problems, this article proposes an IoT malware classification method based on lightweight convolutional neural networks (LCNNs). First, the malware binaries are converted into multidimensional Markov images. Then, the LCNN is designed with two new operations, depthwise convolution and channel shuffle, for malware images classification. Compared with other deep learning-based methods such as VGG16, the designed LCNN can greatly reduce trainable parameters while maintaining accuracy. The generated model of LCNN is only about 1 MB, while that of VGG16 is 552.57 MB. The average accuracies of the proposed method are higher than that of gray images on multiple IoT malware data sets, all of which are over 95%. Compared with the state-of-the-art low-level features-based methods, the average accuracy of the proposed method is 99.356% on the Microsoft data set even if the model is tiny. The results show that the proposed method is not only suitable for IoT environments but also has high accuracy.
Hybrid Therapy Versus Total En Bloc Spondyectomy in the Treatment of Solitary Radioresistant Spinal Metastases

Study Design: This was a retrospective study. Objective: To compare the treatment efficacy of hybrid therapy (HT) with that of total en bloc spondylectomy (TES) in patients with solitary radioresistant high-grade epidural spinal cord compression (ESCC) spinal metastases. Summary of Background Data: Both HT and TES can yield good results for solitary radioresistant metastatic spinal tumors with high-grade ESCC. However, there is still a lack of comparative studies on the treatment efficacy of these 2 methods. Materials and Methods: We retrospectively reviewed patients with the above-mentioned tumors between January 2012 and May 2019. A total of 157 patients underwent surgery, 64 of whom received HT and 93 were treated with TES. Propensity score matching (1:1 ratio) allowed the generation of best-matched pairs for the 2 categories. Local control rates and survival rates were estimated using the Kaplan-Meier method. Results: All patients had a minimum 2-year follow-up. The longest follow-up period was 88 months. The survival rates and local progression-free survival rates after HT were comparable with those after TES at 1 year (84.6% vs. 83.1% and 90.2% vs. 90%, respectively), 2 years (60.8% vs. 64.3% and 64.1% vs. 62.1%, respectively), and 5 years (18.8% vs. 24.1% and 24.4% vs. 28.4%, respectively). There were no significant differences in pain control, improvement in neurological status, spine stabilization restoration, incidence of perioperative complications, and improvement in quality of life between the groups. However, HT showed more advantages than TES in that it had a shorter operative time and lower intraoperative blood loss. Conclusions: HT can obtain satisfactory results comparable to TES for solitary radioresistant metastatic spinal tumors with high-grade ESCC. In addition, HT has a shorter operative time and fewer perioperative complications than TES. HT may be a promising treatment for solitary radioresistant metastatic spinal tumors with high-grade ESCC.
A 510-pW 32-kHz Crystal Oscillator With High Energy-to-Noise-Ratio Pulse Injection

This article introduces a 32-kHz crystal oscillator (XO) with high energy-to-noise-ratio pulse injection at subharmonic frequency. A T/4-delay clock slicer is proposed to convert the sinusoidal crystal waveform into an output clock of 32 kHz and to introduce a delay of T/4, providing proper timing for energy injections. The output clock feeds frequency dividers and generates pulses to activate the proposed all-NMOS differential driver at 4 kHz. It enables two injections in eight periods at the peak and valley of the crystal oscillation, with the crystal running freely between injections. This configuration achieves a 2-ppb Allan deviation floor. The less frequent injections reduce the injection overhead, enabling the lowest reported power consumption of published nW XOs (0.51 nW). At 0.45 V, the proposed XO operates across a temperature range of $- 25\,\,^{\circ }\text{C}$ to 125 °C, the widest reported range for nW XOs. This design is fabricated in the 40-nm CMOS and occupies 0.02 mm2.
Provenance classification of nephrite jades using multivariate LIBS: a comparative study

Provenance classification of nephrite jades is important since the unit price of jade changes drastically with its geological origin. In the present work, a detailed comparison between commonly applied multivariate methods is conducted to classify nephrite samples from five different locations via their laser-induced breakdown spectroscopy (LIBS) spectra. Five multivariate methods including principal component analysis (PCA), one-step and pairwise partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM) were applied to provide the classification information on the samples. PCA was used for rough classification while the classification performance of the other four methods was discussed in detail. The results show a training accuracy of 89.0%, 99.2%, 99.8% and 100%, and a testing accuracy of 76.8%, 97.8%, 92.8% and 99.3% for the PLS-DA (one-step), PLS-DA (pairwise), LDA and SVM algorithms respectively. The superior model nature and the selection of suitable characteristic lines for weight differences led to the high performance of SVM, showing an excellent applicability for the provenance classification of nephrite jades using LIBS spectra.
Antioxidant, Anti-urease Activities and Genotoxic Effects of Terfezia Claveryi Methanol Extracts on Human Lymphocytes

Objective: Terfezia claveryi is a dark brown color truffle and has been used extensively in human diet in Turkey. Therefore; it is an important issue to determine their biological activities and toxicity. This study aims to investigate the antioxidant and anti-urease effects of T. claveryi methanol extracts and to evaluate the genotoxicity of each extract. Methods: The antioxidant activities of methanol extracts were examined by 2,2-diphenyl-1-picryl-hydrazyl (DPPH), 2,2’-azino-bis(3-ethylbenzothiazoline-6- sulfonate (ABTS) and ferric reducing/antioxidant power (FRAP) methods. The amount of total phenolic compounds contained in the extracts was determined using the Folin-Ciocalteu reagent (FCR) method. Anti-urease activities of methanol extracts were evaluated by indophenol method. In addition, potential DNA damaging effects of T. claveryi extracts on human lymphocytes were determined by the alkaline comet assay. Results: UM extract exhibited higher DPPH radical scavenging activity than MM extract [the concentration of the substance which provides the inhibition of 50% of the radical (IC 50 ):0.77±0.01 mg/mL, 1.17±0.02 mg/mL, respectively, p <0.0001] however, lower activity than standard ( p <0.0001). Both extracts in different concentrations of T. claveryi caused DNA damage ( p <0.0001) compared with solvent control DMSO. Also, total comet scores was higher in cells which had been incubated with MM extract ( p <0.05). UM extract exhibited higher ferric reducing and anti-urease activity than MM extract but not found statistically different; also activities of both extract were found lower than positive controls of each methods ( p <0.0001). Conclusion: T. claveryi methanol extracts induced genotoxicity on lymphocytes and showed very low antioxidant activity compared to standards. This preliminary study should be confirmed with further studies and in diferent cell lines.
Cross-Platform Event Popularity Analysis via Dynamic Time Warping and Neural Prediction

Nowadays, the primary media for information dissemination is shifting to online platforms. Events usually burst online through multiple modern online media. Therefore, predicting event popularity trends becomes crucial for online platforms to track public concerns and make appropriate decisions. However, little research focuses on event popularity prediction from a cross platform perspective. Challenges stem from the vast diversity of events and media, limited access to aligned datasets across different platforms, and a considerable amount of noise in datasets. In this paper, we solve the cross-platform event popularity prediction problem by proposing a model named <italic>DancingLines</italic>, which is mainly composed of three parts. First, we propose <italic>TF-SW</italic>, a semantic-aware popularity quantification model based on Term Frequency with Semantic Weight. TF-SW obtains the event popularity based on Word2Vec and TextRank, and generates Event Popularity Time Series (EPTS). Then, we propose <inline-formula><tex-math notation="LaTeX">$\omega$</tex-math><alternatives><mml:math><mml:mi>ω</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq1-3090663.gif"/></alternatives></inline-formula><italic>DTW-CD</italic>, a pairwise time series alignment model derived from Dynamic Time Wrapping (DTW) with Compound Distance (CD) for aligning the EPTS on several platforms. Finally, we aggregate two time series and propose a neural-based prediction model implementing Long Short-Term Memory (LSTM) with attention mechanism to obtain accurate event popularity predictions. Evaluation results based on large scale real-world datasets demonstrate that DancingLines can efficiently characterize, align, and predict event popularity in a cross-platform manner.
Bipyridine bisphosphonate-based fluorescent optical sensor and optode for selective detection of Zn2+ ions and its applications

In this study, a bipyridine bisphosphonate-based tetrabutyl 2,2′-bisbipyridine-5,5′-diylbis(methylene) diphosphonate (L) was developed as fluorescence optical sensor for sensitive and selective determination of Zn2+. The sensing ability of L was investigated in solution as well as polymeric membrane phase using absorption and emission spectroscopy. L exhibits weak fluorescence in CH3CN/H2O (9 : 1, v/v). Upon interaction of L with Zn2+ ions, the emission intensity of L significantly increases due to chelation-enhanced fluorescence (CHEF). The Job's plot and Benesi–Hildebrand (B–H) studies revealed the formation of a L–Zn2+ complex in 1 : 1 binding stoichiometry with an estimated association constant of 4.2 × 103 M−1. The complex formation between L and Zn2+ was established from 1H NMR, 31P NMR, mass and theoretical studies. In order to make a low-cost and effective probe for sensing Zn2+ in aqueous media, the optode was prepared by incorporating L as a neutral ligand, dioctyl phthalate (DOP) as plasticizer, sodium tetraphenyl borate (NaTPhB) as an anionic additive and polyvinyl chloride (PVC) as supporting membrane. These components were systematically coated on a quartz plate of dimensions 12 mm × 16 mm with 5 mm thickness. After equilibration with Zn2+, the optode exhibited a selective and strong fluorescence at 350 nm due to CHEF. The best performance of the optode was observed with a membrane having L : PVC : DOP : NaTPhB in proportions of 2 : 62 : 35 : 1 (w/w) in 4.5–8.5 pH range. The probe can detect Zn2+ at concentrations as low as 1.3 nM with fast response time of 15 s and the optical response remains unaltered even after 3 months of use. The fluorescence reversibility of L was studied by sequential addition of Zn2+ and EDTA. The probe was used successfully used for determination of Zn2+ in pharmaceutical and water samples. In addition, L was applied for Zn2+ ions determination in HeLa cells using optical fluoroscence microscope.
Impact of farmland transfer on fertilizer input:Taking Shandong Province as an example

： The transfer of farmland will change the fertilizer input of farmers, and then directly affect the agricultural ecological environment. Based on the data of 892 farmers in 10 counties of Shandong Province, this study estimated the impact of different farmland transfer behaviors on farmers ’ fertilizer input by using the endogenous switching model, and verified the mechanism of the impact of farmland transfer on farmers ’ fertilizer input by using an intermediary effect model. The results show that there are significant differences in the effects of different farmland transfer behaviors on Farmers ’ chemical fertilizer input. Farmer ’ s chemical fertilizer input decreases significantly after the farmland is transferred in, while increases significantly after the farmland is transferred out. From the mechanism of farmland transfer affecting fertilizer input, farmland transfer mainly affects farmers ’ fertilizer input by changing agricultural labor supply; Although the purchase of social services has reduced farmers ’ fertilizer input, it has not completely offset the increase of fertilizer input caused by farmland transfer out; Technical training, business scale, family labor force and the degree of part-time employment can also have a significant impact on Farmers ’ fertilizer input. Therefore, the government should guide the farmers to expand the scale of farmland appropriately, actively regulate their production behavior, and vigorously develop external agricultural services to reduce the input of fertilizers.
Development of an SSR marker-based genetic linkage map and identification of a QTL associated with flowering time in Eustoma

Lisianthus (Eustoma grandiflorum) is an important floricultural crop cultivated worldwide. Despite its commercial importance, few DNA markers are available for molecular genetic research. In this study, we constructed a genetic linkage map and to detect quantitative trait loci (QTLs) for important agronomic traits of lisianthus. To develop simple sequence repeat (SSR) markers, we used 454-pyrosequencing technology to obtain genomic shotgun sequences and subsequently identified 8263 putative SSRs. A total of 3990 primer pairs were designed in silico and 1189 unique primer pairs were extracted through a BLAST search. Amplification was successful for more than 1000 primer pairs, and ultimately 278 SSR markers exhibited polymorphism between the two lisianthus accessions evaluated. Based on these markers, a genetic linkage map was constructed using a breeding population derived from crosses between the two accessions, for which flowering time differed (>140 days when grown under 20°C). We detected one QTL associated with flowering time (phenotypic variance, 27%; LOD value, 3.7). The SSR marker located at this QTL may account for variation in flowering time among accessions (i.e., three accessions whose nodes of the first flower were over 30 had late-flowering alleles of this QTL).
POWER TRANSFORMERS ASSET MANAGEMENT BASED ON MACHINE LEARNING

This paper presents intelligent algorithms applied to power transformer monitoring data. Algorithms illustrate how to use big data obtained during the exploitation of power transformers in order to create optimal maintenance plan. The related database encompasses values obtained by measurements of polarization index, dielectric loss factor, idle current, short-circuit impedance, etc. The first algorithm applies unsupervised machine learning (UML) in order to classify these data and assigns power transformers to the groups with similar properties and probability of failure. The second algorithm uses artificial neural networks (ANN), as a part of supervised machine learning (SML), to assess the exploitation age of the power transformer based on the history of monitoring. The calculated exploitation age is compared then with the actual age and premature aging of the power transformer can be indicated. Both algorithms are trained on the data related to the power transformers installed in the electric power plants and the results are presented in the paper. The results of algorithms application are used to improve the maintenance of power transformers in Public Enterprise Electric Power Industry of Serbia. The current practice of periodical, Time Based Maintenance can be replaced with contemporary approaches, like Condition Based Maintenance (CBM) and Risk Based Maintenance (RBM). The results of engineering application demonstrate the effectiveness and superiority of the machine learning and improve the diagnostics and maintenance plan for the power transformers.
Compact Wide-Angle Scanning Multibeam Antenna Array for V2X Communications

This letter presents a wide-angle scanning multibeam planar inverted-E antenna (PIEA) array for vehicle-to-everything communication. The compact four-element PIEA array is designed with 0. 3λ<sub>o</sub> spacing, which achieves 90<inline-formula><tex-math notation="LaTeX">$^{\circ}$</tex-math></inline-formula> wide-angle scanning when a 135<inline-formula><tex-math notation="LaTeX">$^{\circ}$</tex-math></inline-formula> phase shift is applied obtained using a 4 × 4 Butler matrix (BM). The closeness of the array elements causes high mutual coupling and high cross polarization at wider scan angles. Therefore, three novel techniques are employed to overcome the high cross polarization and mutual coupling between the antenna elements in this array. To feed the antenna array, a low-cost miniaturized beamforming 4 × 4 BM circuit is designed without the use of phase shifters and conventional crossovers. The total dimensions of the compact PIEA array are just 0. 456λ<sub>0</sub> × 1. 12λ<sub>0</sub> × 0. 036λ<sub>0</sub>, while the overall dimensions of the multibeam array, including BM, are 2. 731λ<sub>0</sub> × 2. 2λ<sub>0</sub> × 0.046 λ<sub>0</sub>. This multibeam array achieves for the first time the wide-angle scanning angles of ±87<inline-formula><tex-math notation="LaTeX">$^{\circ}$</tex-math></inline-formula> with a 3 dB beam scanning range of ±120<inline-formula><tex-math notation="LaTeX">$^{\circ}$</tex-math></inline-formula> along with the scanning angles of ±20<inline-formula><tex-math notation="LaTeX">$^{\circ}$</tex-math></inline-formula>. This array design can be scaled to use as an antenna array for millimeter-wave applications.