Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Structural Empowerment and Employee Engagement on Adaptive Performance: The Mediating Role of Knowledge Hoarding

In the most recent times, due to the worldwide pandemic situation, business organizations are confronting with different work environments, and working in the organizations is different from the previous time. Nowadays, the demand for adaptive workers has augmented, and it is compulsory for employees to adapt well to the modifications. In this rapidly changing and volatile work environment, the current research investigates the impact of structural empowerment (SE) and employee engagement (EE) on employee adaptive performance (AP). This study aims to have an empirical investigation of the mediating role of knowledge hoarding on adaptive performance in the dominant context of engaged and empowered employees. The purpose of this research is to test a theoretical model linking employee engagement and workplace empowerment to the employees’ adaptive performance through a mediation mechanism of knowledge hoarding in the growing environment of the services sector of Pakistan. The proposed theoretical model suggests that fear and anxiety mediate the relationship between project uncertainties and the use of improvisation as an adaptive response to uncertainties. Trait positive affect may strengthen this relationship, since individuals high in this trait might manage their emotions in a way that will lead to higher improvisation levels with impact on innovation. This study will also empirically investigate the moderating role of experimental culture, availability of real time information and communication as well as team work quality and expertise in project teams between improvisation and innovation. Descriptive, correlation and regression analyses were run to analyze the data. Tools of ADANCO 2.2. 1 and SPSS 23 were applied to perform data analysis. To measure and explain the proposed model, well-established scales are adopted. The outcomes of this research study show that structural empowerment (SE) and EE statistically impact adaptive performance (AP) and improve it. However, EE and SE incline to undermine KH.
Measuring local employment multipliers and informal employment: a stochastic frontier approach

ABSTRACT This paper offers policymakers a novel tool for calculating employment multipliers. A theoretical model incorporating a non-tradeable employment function is combined with a stochastic frontier methodology to estimate an accurate multiplier. The advantage of this model is that it allows a consideration of unobserved informal employment when estimating the multiplier. We find an employment multiplier effect of 1.2 jobs in the non-tradeable sector for one job in the tradeable sector. Also, the greater the number of skilled consumers, the higher the multiplier indices and the lower the level of informal employment. Moreover, specialised sectors requiring skilled workers also present less informal employment. We use provincial data for Spain over the period 1995–2013.
Surface hazards in North-west Europe following sudden stratospheric warming events

Sudden stratospheric warmings (SSWs) have been linked to surface temperature anomalies, but how these connect to changes in the likelihood of specific weather extremes and their associated weather patterns remains uncertain. While, on average, it is true that cold surface temperatures follow SSW events, particularly in Northern Europe, there is considerable event-to-event variability. Over the British Isles and Central Europe, only around 45% of SSWs are followed by a colder than average period and a negative phase of the North Atlantic Oscillation, cautioning against an over-generalised approach to surface anomalies associated with SSWs. Focussing on more hazardous weather, which in winter is associated with cold extremes, we use reanalysis data to consider how SSWs impact temperature-related hazards; namely the frequency of snowy days, frost days and spells of extreme cold weather in 12 major European cities. In general, SSWs are associated with an increased risk of snow across most of western Europe, and that this is particularly significant in milder, more maritime locations such as London where in reanalysis, snowfall days are 40% more likely after an SSW. However, there is considerable variation in surface temperature anomalies between SSW events; the third of SSWs with the warmest surface anomalies are statistically more likely to have a decreased risk of snow, frost and persistent cold spells compared with non-SSW time periods. These warmer events are associated with a different temperature anomaly pattern, which is consistent in both reanalysis data and large ensemble CMIP6 models. We further show that these warm surface temperature anomaly SSWs are becoming more frequent, a trend which is consistent with background global warming. The varied surface anomalies associated with SSWs highlights the need to study their impacts in a probabilistic sense, and motivates further work to enable better prediction of the impacts of a given event.
Nonlinear dependencies in the Fama and French three-factor model

ABSTRACT This article addresses the topic of nonlinear dependencies in the Fama and French three-factor model. Five time-series models, including nonlinear terms, are assessed using US and European data and compared with a benchmark linear model. The analysis found that nonlinear dependencies in the modified Fama and French three-factor model were statistically significant and provided additional explanatory power for the underlying return-generating process. However, these nonlinear dependencies are of secondary importance in the economic sense.
The Impact of Nordic Walking Pole Length on Gait Kinematic Parameters

Nordic walking (NW) is a popular physical activity used to manage chronic diseases and maintain overall health and fitness status. This study aimed to compare NW to ordinary walking (W) with regard to pole length and to identify kinematic differences associated with different poles’ length (55%, 65% and 75% of the subject’s height, respectively). Twelve male volunteers (21.1 ± 0.7 years; 1.74 ± 0.05 m; 68.9 ± 6.1 kg) were tested in four conditions (W, NW55, NW65 and NW75) at three different speeds (4-5-6 km∗h−1). Each subject performed a total of twelve tests in a random order. Three-dimensional kinematics of upper and lower body were measured for both W and NW, while oxygen consumption levels (VO2) and rating of perceived exertion (RPE) were measured only for NW trials with different poles’ length. NW showed a higher step length, lower elbow motion and higher trunk motion (p < 0.05) compared to W. Additionally, NW65 did not show any kinematic or RPE differences compared to NW55 and NW75. Only NW75 showed a higher elbow joint (p < 0.05) and lower pole (p < 0.05) range of motion compared to NW55 and a higher VO2 (p < 0.05) compared to NW55 and NW65 at 6 km∗h−1. In conclusion, the use of the poles affects the motion of the upper and lower body during gait. Poles with shorter or longer length do not produce particular changes in NW kinematics. However, increasing the length of the pole can be a smart variation in NW to increase exercise metabolic demand without significantly affecting the kinematics and the RPE.
Development of Copper Complexes with Diimines and Dipicolinate as Anticancer Cytotoxic Agents

Coordination complexes may act as anticancer agents. Among others, the formation of the complex may facilitate the ligand uptake by the cell. Searching for new copper compounds with cytotoxic activity, the complex Cu-dipicolinate was studied as a neutral scaffold to form ternary complexes with diimines. A series of [Cu(dipicolinate)(diimine)] complexes (where diimine: Phenanthroline, phen, 5-NO2-phenanthroline, 4-methyl-phenanthroline, neocuproine, 3,4,7,8-tetramethyl-phenanthroline, tmp, bathophenanthroline, bipyridine, dimethyl-bipyridine, as well as the ligand 2,2-dipyridil-amine, bam) were synthesized and characterized both in the solid state, including a new crystal structure of [Cu2(dipicolinate)2(tmp)2]·7H2O. Their chemistry in aqueous solution was explored by UV/vis spectroscopy, conductivity, cyclic voltammetry, and electron paramagnetic resonance studies. Their DNA binding was analyzed by electronic spectroscopy (determining Kb values), circular dichroism, and viscosity methods. The cytotoxicity of the complexes was assessed on human cancer cell lines MDA-MB-231, MCF-7 (breast, the first triple negative), A549 (lung epithelial) and A2780cis (ovarian, Cisplatin-resistant), and non-tumor cell lines MRC-5 (lung) and MCF-10A (breast). The major species are ternary, in solution and solid state. Complexes are highly cytotoxic as compared to Cisplatin. Complexes containing bam and phen are interesting candidates to study their in vivo activity in triple-negative breast cancer treatment.
Protective behaviors during COVID-19 confinement measures in Greece: the role of anxiety, perceived risk and risky-choice framing

Confinement measures at the beginning of the COVID-19 pandemic imposed major changes on the global population. The aim of this study was to explore the level to which the public adhered to protective guidelines by adopting the most appropriate behaviors at that time (such as hand washing with soap and using sanitizer gel) and to identify the determinants of these behaviors. A purposive sample of 1013 individuals was invited and voluntarily participated in the online survey. The questionnaire collected information on demographic data, hand washing, risk perception, anxiety (through the S = Anxiety scale of STAI) and risky-choice framing. Results showed increased levels of anxiety, a moderate perception of the risk of catching coronavirus and increased adoption of protective behaviors, such as handwashing and cleaning surfaces with disinfectant/antiseptic products. Multiple ordinal logistic regression models showed that being female, more educated and cleaning home with disinfectant / antiseptic products predicted handwashing with soap. Additionally, having an increased perception of getting the coronavirus, being older and cleaning the home with disinfectant / antiseptic products predicted handwashing with antiseptics. Public health interventions should take into consideration the unified cleaning pattern and the combined effect of sociodemographic variables and risk perception on the adoption of protective behaviour in the context of a health crisis which is out of people's control.
Chemical and Nutritional Profiling of the Seaweed Dictyota dichotoma and Evaluation of Its Antioxidant, Antimicrobial and Hypoglycemic Potentials

Seaweed has been known to possess beneficial effects forhuman health due to the presence of functional bioactive components. The n-butanol and ethyl acetate extracts of Dictyota dichotoma showed ash (31.78%), crude fat (18.93%), crude protein (14.5%), and carbohydrate (12.35%) contents. About 19 compounds were identified in the n-butanol extract, primarily undecane, cetylic acid, hexadecenoic acid, Z-11-, lageracetal, dodecane, and tridecane, whereas 25 compounds were identified in the ethyl acetate extract, mainly tetradecanoic, hexadecenoic acid, Z-11-, undecane, and myristic acid. FT-IR spectroscopy confirmed the presence of carboxylic acid, phenols, aromatics, ethers, amides, sulfonates, and ketones. Moreover, total phenolic contents (TPC) and total flavonoid contents (TFC) in ethyl acetate extract were 2.56 and 2.51 mg GAE/g and in n-butanol extract were 2.11 and 2.25 mg QE/g, respectively. Ethyl acetate and n-butanol extracts at a high concentration of 100 mg mL−1 showed 66.64 and 56.56 % inhibition of DPPH, respectively. Antimicrobial activity revealed that Candida albicans was the most susceptible microorganism, followed by Bacillus subtilis, Staphylococcus aureus, and Escherichia coli, whereas Pseudomonas aeruginosa showed the least inhibition at all concentrations. The in vivo hypoglycemic study revealed that both extracts exhibited concentration-dependent hypoglycemic activities. In conclusion, this macroalgae exhibited antioxidant, antimicrobial, and hypoglycemic potentials.
Rule induction based on frequencies of attribute values

Rule induction is one of the most significant issues in data mining. This is due to the fact that decision rules induced from the training data are used to classify new objects. The classification is based on matching the object with the decision rules. Specifically, the generated rules are used to resolve whether or not the object satisfies the conditions specified by the subset of attributes belonging to a given decision class. Most of the rule induction methods are insufficient for large databases and hence do not support today's Big Data issues. This is mainly due to the use of so-called discernibility matrices during calculations. The purpose of this paper is the idea of the implementation of a new efficient rule induction algorithm that is based on statistics of attribute values and that avoids building the discernibility matrix explicitly. Tests have shown that the implementation is much more efficient than currently available solutions for large data sets.
Energy-Efficient Audio Processing at the Edge for Biologging Applications

Biologging refers to the use of animal-borne recording devices to study wildlife behavior. In the case of audio recording, such devices generate large amounts of data over several months, and thus require some level of processing automation for the raw data collected. Academics have widely adopted offline deep-learning-classification algorithms to extract meaningful information from large datasets, mainly using time-frequency signal representations such as spectrograms. Because of the high deployment costs of animal-borne devices, the autonomy/weight ratio remains by far the fundamental concern. Basically, power consumption is addressed using onboard mass storage (no wireless transmission), yet the energy cost associated with data storage activity is far from negligible. In this paper, we evaluate various strategies to reduce the amount of stored data, making the fair assumption that audio will be categorized using a deep-learning classifier at some point of the process. This assumption opens up several scenarios, from straightforward raw audio storage paired with further offline classification on one side, to a fully embedded AI engine on the other side, with embedded audio compression or feature extraction in between. This paper investigates three approaches focusing on data-dimension reduction: (i) traditional inline audio compression, namely ADPCM and MP3, (ii) full deep-learning classification at the edge, and (iii) embedded pre-processing that only computes and stores spectrograms for later offline classification. We characterized each approach in terms of total (sensor + CPU + mass-storage) edge power consumption (i.e., recorder autonomy) and classification accuracy. Our results demonstrate that ADPCM encoding brings 17.6% energy savings compared to the baseline system (i.e., uncompressed raw audio samples). Using such compressed data, a state-of-the-art spectrogram-based classification model still achieves 91.25% accuracy on open speech datasets. Performing inline data-preparation can significantly reduce the amount of stored data allowing for a 19.8% energy saving compared to the baseline system, while still achieving 89% accuracy during classification. These results show that while massive data reduction can be achieved through the use of inline computation of spectrograms, it translates to little benefit on device autonomy when compared to ADPCM encoding, with the added downside of losing original audio information.
On the Traceability of Honey by Means of Lanthanide Distribution

Honey is a natural food appreciated all over the world since antiquity due to its well-recognised beneficial properties. However, it is also considered among the most counterfeited foods. Therefore, analytical methods are currently being developed to allow the verifying of its geographic provenance and its botanical origin. Trace- and ultra-trace elements are usually exploited as chemical descriptors in authentication studies, as they allow the properties declared in the label to be verified. A different matter is to trace a food by means of traceability, that is, to find the link between a food and the soil in which this food originates. For traceability, it has been demonstrated in several studies that the lanthanides are particularly useful to find this link. In the present study, the traceability of the honey chain has been studied by means of ICP-MS and ICP-OES analysis, by comparing the lanthanide distributions of 17 different monofloral honey chains, each one composed of honey, flowers and soil in which such flowers grew. The results show that, while the fingerprint of soil, described by the lanthanide distribution, is transmitted unaltered from soil to flowers, a slight fractionation on the heavier lanthanides (from Dy to Lu) occurs in the passage from flowers to honey.
Impact of Oil and Agricultural Shocks on the Financial Markets of Emerging Market Economies

We assess oil and agricultural price shocks in the financial markets of six EMEs (Brazil, Chile, Mexico, India, South Africa, and Turkey) using the GVAR. Positive oil price shocks provoke falls in all stock markets, devaluations in four domestic currencies, and tight monetary policies in two economies. The variance decomposition indicated that this shock is more influential in the stock markets, followed by the exchange rates. Compared to the oil price, shocks in agricultural prices caused appreciations in all domestic currencies. This shock is pervasive in all exchange markets in the first months, losing importance over time. The distinct responses to different commodities shocks point out that the kind of shock is relevant to comprehend its influence on EMEs. We detected relevant heterogeneities concerning the spreading of commodities shocks on domestic financial markets, especially in South Africa.
Analytical and numerical study of the Superfluid : Bose glass transition in two dimensions

The interplay of disorder (i.e. impurities) and interactions is one of the most fundamental questions in Condensed Matter Physics that has received a lot attention in the past couple of decades. The quantum phase transition from Superfluid to Bose glass driven by disorder has puzzled theoreticians and experimentalists alike, leaving unresolved questions despite their best efforts. The work presented in this thesis addresses some of these questions for two models of disordered hard-core bosons in two dimensions. In particular, the values of the critical exponents governing the transition, the inhomogeneous properties of the competing phases, the physical scenario at criticality and the bosonic excitations’ localization properties are investigated. Three different approaches to the transition are used to explore this problem. We first show how Bosecondensate and superfluid fractions are affected by disorder in a Mean-Field approximation, which is unable to capture a transition, but reveals interesting qualitative features. Building on such a Mean-Field solution, quantum fluctuations are then introduced using a linear spin-wave theory in real space which does capture the transition and furthermore unveils a non-trivial behavior for the excitation spectrum. Finally, the quantum criticality is explored in great detail using state-of-theart Quantum Monte Carlo simulations, leading to a precise evaluation of the critical exponents and a surprising absence of self-averaging in the Bose glass regime.
Factors That Impact the Relationship between Perceived Organizational Support and Technostress in Teachers

In the last two years, the obligatory use of technologies due to the COVID-19 pandemic has increased the technostress suffered by education professionals. This study investigates the relationships between technostress and perceived organizational support and the influence of certain socio-demographic variables. An online survey was administered to 771 teachers working in different educational stages in various autonomous communities in Spain. Perceived organizational support was found to be significantly correlated with technostress. Women tend to experience more technostress in general and significant gender differences were also found in the dimension of anxiety. The analyzed data also suggest that perceived organizational support is higher in private schools. In urban centers, teachers’ technostress increases in higher educational stages, such as secondary education and baccalaureate. Further work is needed to develop school policies that address the needs of teachers and provide support for those at risk of technostress. In addition, there is a need to design coping strategies and prioritize the most at-risk sectors to improve their overall health and well-being.
Value of Artificial Intelligence in Evaluating Lymph Node Metastases

Simple Summary In surgical pathology, the assessment of the presence of lymph node metastases is a key aspect in terms of the staging and prognosis of cancer patients. This type of work is time-consuming and prone to error. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic cells, so this task can be automated and standardized, increasing diagnostic quality. This manuscript aims to systematically review the published literature regarding the application of various artificial intelligence systems for the assessment of metastases in lymph nodes in whole slide images. Abstract One of the most relevant prognostic factors in cancer staging is the presence of lymph node (LN) metastasis. Evaluating lymph nodes for the presence of metastatic cancerous cells can be a lengthy, monotonous, and error-prone process. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic tissue. The aim of this study was to review the literature regarding the implementation of AI as a tool for the detection of metastases in LNs in WSIs. A systematic literature search was conducted in PubMed and Embase databases. Studies involving the application of AI techniques to automatically analyze LN status were included. Of 4584 retrieved articles, 23 were included. Relevant articles were labeled into three categories based upon the accuracy of AI in evaluating LNs. Published data overall indicate that the application of AI in detecting LN metastases is promising and can be proficiently employed in daily pathology practice.
Pentraxin-3 and neonatal sepsis: a systematic review and meta-analysis

Abstract Aim The potential bond between pentraxin-3 levels and neonatal sepsis has been the center of research in many primary studies. The aim of the current meta-analysis is to examine whether there are differences among pentraxin-3 levels in septic and in healthy neonates. Materials and Methods Our search strategy included the systematic search of the following databases: MEDLINE, Clinicaltrials.gov, Cochrane Central Register of Controlled Trials (CENTRAL), Google Scholar, using a structured algorithm. Statistical analysis of the overall outcome was done using Revman 5.4 software while leave-one-out and meta-regression analysis were done using the R software. Quality assessment of the included studies was done using the Newcastle-Ottawa scale. Results Pentraxin-3 levels were found to be higher in newborns affected by sepsis than in healthy neonates with an MD = 7.66 [95% CI 0.89, 14.42 (p = .03, I 2 = 99%)]. Subgroup analysis, based on the country of origin of the included study, led to I 2 = 0 with an MD = 1.25 with 95% CI [0.82, 1.69], p < 10−5. Publication bias was assessed using the trim and fill method together with visual inspection of the funnel plots, showcasing no missing studies. Conclusion The results of our study show that pentraxin-3 is elevated in neonates with sepsis making it a potential biomarker that needs to be assessed for its diagnostic accuracy in future cohort studies.