Miracle-dz/UniFAR-FTUs huggingface dataset:
Quinoa Mitigates Chlorfenapyr-Induced Testicular Toxicity in Rats

Chlorfenapyr (Chf), belongs to a new chemical class called halogenated pyrroles, used extensively in Egypt. Negative effects of pesticides on the male reproductive system have been shown broadly. In recent years, the use of medicinal herbs in reducing pesticide toxicity has increased worldwide; one of these herbals is Quinoa seeds (QS), contains high amount of protein, vitamins, and minerals. Our study evaluated the effects of QS on testicular toxicity in rats exposed to Chf. Twenty rats were divided into four groups; group (I) as control was fed on basal diet only, group (II) QS at 30% from the basal diet, group (III) 180 mg/kg b.w/day of Chf, was fed on basal diet only and group (IV) Chf at 180 mg/kg b.w/day plus QS at 30% from the basal diet, the experimental period was four weeks. Rats were sacrificed and blood serum, sperm, and sexual organs samples were collected for biochemical and histomorphological studies. Results revealed that Chf treated animals showed sagging testicles with increased the testis relative weights. This effect was associated with a decline in count, motility, and viability of sperms, and increased sperm abnormality, and a decrease in sex hormones level (P<0.05). On the other hand, QS showed improvement in almost all estimated parameters and mitigated all the toxic effects of Chf, which was confirmed with the histological examination of the testis. So, we can encourage QS consumption as a clue for the toxicity and infertility problems revealed as a result of chlorfenapyr exposure.
Decision-making technology based on knowledge engineering and experiment on the intelligent water-fertilizer irrigation system

Although the irrigation technologies based on the Decision-making System (DMS) began in the late 1990s, while being merely embryonic from laboratory research into application in the agricultural irrigation areas, DMS based on intelligent algorithms have drawn much attention from the academia over the recent years. In this study, we have provided an overview of the decision-making technology based on knowledge engineering for intelligent irrigation system referred to as Knowledge-based Engineering (KBE). As the modern technical research and scientific theory on agricultural water saving is further developed, the water-fertilizer irrigation is becoming increasingly intelligent. We have put forward the concept of KBE intelligent irrigation system and its support to decision-making in the study, while adopting the techniques and methods of knowledge engineering. In addition, we have combined our research findings with the expert knowledge on the water-fertilizer irrigation in a system integrated with computer network, intelligent reasoning and artificial intelligence (AI), among other modern high-techs. We have set up the decision-making models and analytical methods of irrigation and fertilization for KBE by referring to the expert experience and data of fertilization. Moreover, we have taken into account the web crawler technology in irrigation and fertilization, and we have put forward novel methods of knowledge acquisition based on the web crawler. Correspondingly, we have established the knowledge base for the decision-making support system tailored to irrigation and fertilization. The experiment result shows that the recommended irrigation quota is compared with local cultivation technology experience to obtain a decision accuracy of 81.7%. And the water and fertilizer management plan obtained by the intelligent decision-making system has a thicker stem and higher plant height during the growth period than the crops obtained by local cultivation experience. The output of the decision-making system is 620 kg, which a relative increase of 5.08% is compared with the 590 kg obtained from local cultivation experience.
A computationally efficient non-iterative four-parameter sine fitting method

National Research Development and Innovation Fund Abstract A computationally efficient four‐parameter least squares (LS) sine fitting method in the time domain is presented here. Unlike the most widespread procedure defined in the relevant IEEE standard, the proposed fitting is non‐iterative. This is achieved by the second‐order approximation of the cost function (CF) around the actual frequency of the sinusoidal excitation. The approximation reduces the four‐parameter non‐linear fitting problem to a defined set of three‐parameter linear fitting problems. Therefore, the computational demand can be predicted precisely, which is an essential aspect of real‐ life applications. Furthermore, the proposed method is shown to have increased numerical stability. Finally, measurements and computer simulations are carried out to demonstrate the reduced computational demand, while preserving the accuracy compared with the algorithm proposed in the IEEE standard.
Born to run: where rebel parties participate in post-conflict local elections

ABSTRACT Where do newly formed rebel parties run their candidates for local elections? Most work on rebel parties examines the factors that explain rebel party success on the national level. However, few studies look at where rebel parties run candidates in elections and at the local level. We develop a dataset to analyze the activity of FARC (Fuerzas Alternative Revolucionaria del Común) in the 2019 local elections in Colombia. Using both a Firth Logit regression and Zero-Inflated Negative Binomial model, we found that former rebel parties are more likely to candidates in locations where they have had a historical presence, where ideologically similar parties also ran candidates, where there is a larger field of candidates, and in areas with less institutionalized elections.
Navigating Precarity: Disruption and Decline at the Pittsburgh Post-Gazette

ABSTRACT Since 2017, the Pittsburgh Post-Gazette (PG) has faced multiple disruptions, including a bitter labor conflict and various public controversies, that have threatened the credibility of this long-standing metropolitan newspaper. To understand how PG journalists and its union have navigated such pressures, this study uses the concept of compounding precarity to capture the multiple, overlapping parts of journalistic precarity that together effect the lives and work of journalists. By interviewing current and past PG journalists, the analysis reveals the individual- and collective-level tactics workers used to navigate these conditions. The study finds that, though journalists individualized precarity at times, union logics largely structured how they responded to compounding precarity. While this illustrates the union’s influence at the paper, its inability to sway upper management in numerous disputes reveals the limits of collective organizing at the PG. Given that these issues are not exclusive to the PG, this study highlights the need for cross-coalitional building and structural changes across the news industry in order to spare journalists and journalism from the toxic effects of precarity in late-stage capitalism.
Simultaneous Analysis of Saponins in Ginseng Radix by High Performance Liquid Chromatography

The presence of about 25 ginsenosides in Ginseng Radix and their structures have already been elucidated. Although some important neutral- and acidic-saponins have been detected by high performance liquid chromatography (HPLC), other saponins have not yet been examined by HPLC. Our present improved method for the detection of ginsenosides by HPLC with a C 18 column and a CH 3 CN-H 2 O gradient system enabled the measurement of not only the representative ginsenosides in Red Ginseng [20(R)-ginsenoside-Rg 2 and -Rh 1 , 20(S,R)-ginsenosides-Rg 3 and -Rs 1 ] but also the minor ginsenosides [ginsenosides-Rb 3 , -Rg 2 , -Rh 1 and quinquenoside-R1]. Furthermore, the application of this method after a little modification of the eluent in particular to an extract of Ginseng Radix achieved separation of 22 neutral- and acidic-ginsenosides. The qualitative and quantitative analysis of ginsenosides using the present method offers an efficient means of evaluating Ginseng Radix and commercial products containing it
Use of Interventions to Overcome Medication Non-Adherence

Medication non-adherence is a global problem and has existed for centuries. Patients have paid a very high price for their behaviour of non-adherence, in the form of impaired cost, prolonged diseases, a burden to family or even by their life. In the era of science and technology where there is a solution for every odd problem, the issue of medication non-adherence can also find a remedy. This paper tries to highlight the factors of non-adherence and looks for solutions through various forms of technology. The review of different published literature highlights the findings of researchers and tries to assimilate a solution for addressing the prolonged problem of medication non-adherence.
Virtual work intensity, job satisfaction, and the mediating role of work-family balance: A study of employees in Germany and China

This paper targets virtual work, an increasingly crucial alternative work arrangement in today’s interconnected world. Based on a survey of 308 employees working in Germany and China, we investigate the relationship between virtual work intensity, work-family balance, and job satisfaction through a mediator model. We find empirical evidence for an inverted U-shaped relationship between virtual work intensity and job satisfaction. When virtual work intensity is below a particular level, it is positively related to job satisfaction. However, increasing virtual work intensity begins to decrease job satisfaction when this threshold is exceeded. Our findings suggest this relationship between virtual work intensity and job satisfaction is mediated by work-family balance. Furthermore, empirical evidence demonstrates that the inverted U-shaped relationship has different optimums in different cultures. The research outcome demonstrates that the threshold is lower for Chinese employees than their German colleagues. This paper contributes to literature relating to job satisfaction, work-family balance, and virtual work by focusing on individual virtual work outcomes in a cross-cultural context. It also attempts to provide an alternative explanation for the generative mechanism of the impact of virtual work intensity on job satisfaction.
Deep Neural Network and Time Series Approach for Finance Systems: Predicting the Movement of the Indian Stock Market

The stock market is an aggregation of investor sentiment that affects daily changes in stock prices. Investor sentiment remained a mystery and challenge over time, inviting researchers to comprehend the market trends. The entry of behavioral scientists in and around the 1980s brought in the market trading’s human dimensions. Shortly after that, due to the digitization of exchanges, the mix of traders changed as institutional traders started using algorithmic trading (AT) on computers. Nevertheless, the effects of investor sentiment did not disappear and continued to intrigue market researchers. Though market sentiment plays a significant role in timing investment decisions, classical finance models largely ignored the role of investor sentiment in asset pricing. For knowing if the market price is value-driven, the investor would isolate components of irrationality from the price, as reflected in the sentiment. Investor sentiment is an expression of irrational expectations of a stock’s risk-return profile that is not justified by available information. In this context, the paper aims to predict the next-day trend in the index prices for the centralized Indian National Stock Exchange (NSE) deploying machine learning algorithms like support vector machine, random forest, gradient boosting, and deep neural networks. The training set is historical NSE closing price data from June 1st, 2013-June 30th, 2020. Additionally, the authors factor technical indicators like moving average (MA), moving average convergence-divergence (MACD), K (%) oscillator and corresponding three days moving average D (%), relative strength indicator (RSI) value, and the LW (R%) indicator for the same period. The predictive power of deep neural networks over other machine learning techniques is established in the paper, demonstrating the future scope of deep learning in multi-parameter time series prediction.
Block-based pseudo-relevance feedback for image retrieval

ABSTRACT Pseudo-relevance feedback (PRF) is a relevance feedback (RF) technique for information retrieval that treats the top k retrieved images as relevance feedback. PRF is used to avoid the limitations of the traditional RF approach, which is a human-in-the-loop process. Although the pseudo-relevance feedback set contains noise, PRF can perform retrieval reasonably effectively. For implementing PRF, the Rocchio algorithm has been considered reasonably effective and is a well-established baseline method. However, it simply treats all of the top k feedback images as being equally similar to the query. Therefore, we present a block-based PRF approach for improving image retrieval performance. In this approach, images in the positive and negative feedback sets are further divided into predefined blocks, each of which contains one to several images, and blocks containing higher- or lower-ranked images will be assigned higher or lower weights, respectively. Experiments using the NUS-WIDE-LITE and Caltech 256 datasets and two different feature representations consistently show that the proposed approach using 30 blocks outperforms the baseline PRF in terms of P@10, P@20, and P@50. Furthermore, we show that a system that incorporates the user’s feedback allows the 30-block-based PRF approach to perform even better.
Magnetic resonance imaging study of water absorption in thermally modified pine wood

Abstract Thermal modification is an environmentally friendly process that enhances the lifetime and properties of timber. In this work, the absorption of water in pine wood (Pinus sylvestris) samples, which were modified by the ThermoWood process, was studied by magnetic resonance imaging (MRI) and gravimetric analysis. The modification temperatures were varied between 180°C and 240°C. The data shows that the modification at 240°C and at 230°C decreases the water absorption rate significantly and slightly, respectively, while lower temperatures do not have a noticeable effect. MR images reveal that free water absorption in latewood (LW) is faster than in earlywood (EW), but in the saturated sample, the amount of water is greater in EW. Individual resin channels can be resolved in the high-resolution images, especially in LW regions of the modified samples, and their density was estimated to be (2.7±0.6) mm-2. The T2 relaxation time of water is longer in the modified wood than in the reference samples due to the removal of resin and extractives in the course of the modification process.
Numerical analysis of linear and nonlinear buckling instability of plates made of topologically interlocked materials

Abstract Topologically interlocked materials (TIMs) are an emerging class of architectured materials that each of block is supported by their neighboring blocks without any connector, which is inspired by microstructures observed in nature. Therefore, they showed alluring attributes such as resistance to impact, toughness, high flexibility even when using brittle materials, and high crack propagation resistance. In these structures, the presence of lateral compressive force, applied on peripheral blocks, ensures the integrity and identified properties. Lateral load, however, can adversely affect TIM plates and cause instability, and initiate buckling. In the present study, to focus on the buckling behavior of TIM plates due to the lateral force, a finite element model of TIM plates, made of tetrahedral blocks, was first validated using numerical results available in the literature and then analyzed. In this regard, an appropriate procedure has been presented to simulate the possible instability of these structures. Unlike monolithic plates, geometric parameters of TIM plates are dependent and this makes their parametric study more challenging. Therefore, some case studies have been accomplished to illustrate the effect of diverse parameters. The current study indicates that increasing the assembly plate length and decreasing its thickness causes a reduction of buckling load; nevertheless, it is necessary to mention that the thickness variation is a more impressive factor.
Accreditation and Certification: Do They Improve Hospital Financial and Quality Performance?

The relationship between healthcare organizational accreditation and their leaders’ professional certification in healthcare management is of specific interest to institutions of higher education and individuals in the healthcare management field. Since academic program accreditation is one piece of evidence of high-quality education, and since professional certification is an attestation to the knowledge, skills, and abilities of those who are certified, we expect alumni who graduated from accredited programs and obtained professional certification to have a positive impact on the organizations that they lead, compared with alumni who did not graduate from accredited programs and who did not obtain professional certification. The authors’ analysis examined the impact of hiring graduates from higher education programs that held external accreditation from the Commission on Accreditation of Healthcare Management Education (CAHME). Graduates’ affiliation with the American College of Healthcare Executives (ACHE) professional healthcare leadership organization was also assessed as an independent variable. Study outcomes focused on these graduates’ respective healthcare organization’s performance measures (cost, quality, and access) to assess the researchers’ inquiry into the perceived value of a CAHME-accredited graduate degree in healthcare administration and a professional ACHE affiliation. The results from this study found no effect of CAHME accreditation or ACHE affiliation on healthcare organization performance outcomes. The study findings support the need for future research surrounding healthcare administration professional graduate degree program characteristics and leader development affiliations, as perceived by various industry stakeholders.
Design of 3‐aminophenol‐grafted polymer‐modified zinc sulphide nanoparticles as drug delivery system

Abstract Zinc sulphide (ZnS) nanoparticles were synthesized by the coprecipitation method. The ZnS nanoparticle surface was polymerized with allyl glycidyl ether (AGE), and 3‐aminophenol was then deposited as a ligand on nanosorbent. The modified nanosorbent was investigated with Fourier transform infrared spectroscopy and thermogravimetric analysis. The particle size of the modified nanosorbent was studied with scanning electron microscopy. Some characteristic factors of the adsorption process such as pH and time were investigated for famotidine using the modified nanosorbent. The equilibrium adsorption study of famotidine by 3‐aminophenol‐grafted AGE/ZnS was analysed by adsorption isotherms of the Langmuir, Freundlich, and Temkin models. The famotidine‐releasing process was investigated in simulated biological fluids (intestinal fluid at pH of 7.4 and gastric fluid at pH of 1.2) and demonstrated 65% and 73% famotidine release during periods of 30 h (pH = 7.4) and 60 min (pH = 1.2), respectively. These results reveal the optimal performance of 3‐aminophenol‐grafted AGE/ZnS for sustained drug delivery.
Geometry-Aware Facial Expression Recognition via Attentive Graph Convolutional Networks

Learning discriminative representations with good robustness from facial observations serves as a fundamental step towards intelligent facial expression recognition (FER). In this article, we propose a novel geometry-aware FER framework to boost the FER performance based on both the geometric and appearance knowledge. Specifically, we propose an encoding strategy for facial landmarks, and adopt a graph convolutional network (GCN) to fully explore the structural information of the facial components behind different expressions. A convolutional neural network (CNN) is further applied to the whole facial observation to learn the global characteristics of different expressions. The features from these two networks are fused into a comprehensive high-semantic representation, which promotes the FER reasoning from both visual and structural perspectives. Moreover, to facilitate the networks to concentrate on the most informative facial regions and components, we introduce multi-level attention mechanisms into the proposed framework, which enhance the reliability of the learned representations for effective FER. Experiments on two challenging FER benchmarks demonstrate that the attentive graph-based learning on the facial geometry boosts the FER accuracy. Furthermore, the insensitivity of the geometric information to the appearance variations also improves the generalization of the proposed framework.
Induced interaction in a Fermi gas with a BEC-BCS crossover (5 pages)

We study the effect of the induced interaction on the superfluid transition temperature of a Fermi gas with a Bose-Einstein condensation-Bardeen-Cooper-Schrieffer (BEC-BCS) crossover. The Gorkov-Melik-Barkhudarov theory about the induced interaction is extended from the BCS side to the entire crossover and the pairing fluctuation is treated in the approach by Nozieres and Schmitt-Rink. At unitarity, the induced interaction reduces the transition temperature by about 20%. In the BCS limit, the transition temperature is reduced by a factor of about 2.22, as found by Gorkov and Melik-Barkhudarov. Our result shows that the effect of the induced interaction is important both on the BCS side and in the unitary region.
Rapid shifts in Arctic tundra species' distributions and inter‐specific range overlap under future climate change

The Arctic is one of the planet's most rapidly warming regions, with trends expected to intensify in the future. Projections of shifts in species distributional ranges under future climate change are thus far lacking for most vertebrate species using the Arctic tundra. Our aim was to assess possible climate‐induced changes in distributional ranges and inter‐specific overlap of an Arctic species assemblage within the world's largest land‐based protected area.
The role of immunotherapy for management of advanced thymic epithelial tumors: a narrative review

The emergence of immunotherapy as a modern pillar of cancer treatment has changed the treatment landscape for various cancers. Immune checkpoint inhibitors directed at programed death-1 (PD-1) or its ligand (PD-L1), in particular, have found widespread clinical applications and have resulted in durable responses and an improvement in survival of patients with advanced or metastatic disease. Tumor cell PD-L1 expression and tumor mutation burden (TMB) are biomarkers of response and efforts are underway to identify other biomarkers that might predict benefit with these drugs. Most patients tolerate immunotherapy well, although a subset of patients develop immune-mediated toxicity due to excessive immune stimulation. Thymic epithelial tumors (TETs) have a unique biology which can predispose to development of autoimmune paraneoplastic disease, especially in patients with thymoma. Due to defects in immunological self-tolerance, the use of immunotherapy in TET patients is associated with an increased risk of immune-mediated adverse events, which can be potentially life-threatening. Development of biomarkers of response and toxicity is particularly important for the treatment of TETs since it is important to identify patients who might benefit from treatment and be at low risk for development of severe immune toxicity. The use of immunotherapy in patients with autoimmune disorders and those who have previously experienced immune-mediated toxicity is currently an area of active research. Various risk mitigation strategies are under evaluation in prospective clinical trials, including trials of immune checkpoint inhibitors in patients with thymic cancers.