Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Middle Managers in Strategy Execution in two Colleges at a South African Higher Education Institution (HEI)

This research examined the role of middle management in executing strategic plans in two colleges in a South African higher education institution (HEI). Strategy execution is a complex operational process. Flawed sense-making of the strategy could lead to strategy breakdown, and challenges may occur concerning understanding and executing the strategic plans. Therefore, the main aim of this study was to examine how middle management in the two colleges at the HEI engaged in the sense-making of the strategic plan. The study set out to: examine how middle management participated in the sense-making of the two colleges' strategic goals; determine how middle management communicated the strategy to employees at the two colleges, and identify the challenges middle management faced with strategy execution at the two colleges. The research employed a qualitative research approach. Interviews were conducted with a purposive non-probability sample of ten middle managers in the two colleges at the HEI. Primary data were collected through semi-structured interviews. Data were analysed using thematic analysis. The interviews were conducted at the middle management’s offices and lasted between 20-45 minutes. The study findings revealed that each college engaged in the sense-making processes differently and that there are different understandings of the role of middle management in executing strategic plans. The study highlights the need for middle management in the two colleges in the HEI to engage in training in executing strategic plans. The findings may help us understand how middle management in the two colleges executes strategy and how sense-making occurs. In addition, it could assist with role clarification of university service units in implementing the strategic initiatives, as most literature focuses on strategy formulation rather than execution.
An Aggressive Cuckoo Search Algorithm for Optimum Power Allocation in a CDMA-Based Cellular Network

This paper proposes an aggressive cuckoo search algorithm for optimum power allocation in a CDMA-based cellular network. To make the cuckoo search algorithm aggressive, adaptive parameters are used to vary the step size and probability of discovery. Furthermore, the Lévy flight is replaced with the Beta distribution to further improve the performance of the algorithm. To prove that the proposed algorithm is superior, the algorithm is tested on 23 benchmark test functions and its results are compared with those of 10 other standard optimization algorithms and 4 other advanced optimization algorithms. The performance of the proposed algorithm is proved via the statistical analysis of the results using the Wilcoxon rank-sum test. The proposed algorithm is then utilized in determining the optimal uplink power for multiple users in a CDMA-based cellular network in three different scenarios through Rician fading channels. The resultant allocated power should ensure that each mobile station meets its predetermined signal-to-interference-and-noise ratio while utilizing the least amount of power.
Efficacy of Exosome Adipose-Derived Stem Cells (ACSs-Exosome) on Skin Flaps Transplantation in Plastic Surgery: A Systematic Literature Review

Background: Exosomes are a small subset of extracellular vesicles with a size of about 50 nm – 200 nm, which are found in many body fluids. The exosome is rich in various proteins and is a cargo that carries out the process of transporting and transporting between cells in the context of cell communication. This becomes a potential new therapeutic modality in triggering the modulation and activity of target cells so that ACSs-Exosome becomes a new potential to modulate skin flap transplantation repair in plastic surgery. Methods: The literature search process was carried out in various databases (PubMed, Web of Sciences, EMBASE, Cochrane Libraries, and Google Scholar) regarding the use of ASCs-exosome in skin flap transplantation repair in plastic surgery cases. Results: A total of 5 studies were included in this systematic review. In general, ASCs-exosome administration was more effective in the percentage of flap survival area than in the placebo group. There are 3 studies that state that the effectiveness of flap area survival is more than 70 percent compared to the placebo group, which is only in the range of 30-40 percent. Conclusion: ASCs-exosome has the potential to be developed to improve the survival rate of skin flap transplantation in the case of plastic surgery.
Seasonal Net Nitrogen Mineralization in the Topsoil of Subsurface Drip Irrigated Fields

ABSTRACT Nitrogen (N) mineralized from soil organic matter during the growing season can be an important source of crop-available N. However, accurate estimates for subsurface drip irrigated fields are currently not available. The main objective of the study was to determine seasonal net N mineralization rates in subsurface drip irrigated fields in California’s Central Valley. Soil samples were collected regularly from the top 15 cm of eleven commercial fields during the growing season and subsamples were incubated at an optimal moisture content and at field moisture (soil moisture content at the time of sampling). Net N mineralization in field moist soil was on average 45% of the potential N mineralization. When corrected for daily soil temperature at the sites, net N mineralization during a 4-month period from April 15 to August 15 averaged 25.2 kg ha−1 (median = 20.7 kg ha−1), which corresponds to 10 to 20% of the N in the aboveground biomass of the crops grown in these fields. To achieve a high N use efficiency and limit the risk of nitrate leaching to the groundwater, net N mineralization from soil organic matter should be accounted for when planning N fertilizer applications.
Implementation of Lean Management for Covid-19 Patient Services at Hospitals in Deli Serdang

The need to improve the quality of health services in hospitals by eliminating waste in order to achieve an efficient health service process through a Lean Management approach. Methods using descriptive qualitative analysis where data is compiled, analyzed and then explained through data collection, problem solving, planning improvement ideas and by providing design improvement proposals. The application of this lean hospital reduces the service time for Covid-19 patients at Deli Serdang Hospital by 467.4 seconds in the service flow and the application of this lean hospital can reduce the service time for Covid-19 patients at the Haji Hospital by 377 seconds in the service flow. The conclusion of the study is that the application of the Lean Management approach at Deli Serdang Hospital and Haji Hospital is able to increase the effectiveness and efficiency of health services.
The Genetic Characterization of the First Detected Bat Coronaviruses in Poland Revealed SARS-Related Types and Alphacoronaviruses

Bats are a major global reservoir of alphacoronaviruses (alphaCoVs) and betaCoVs. Attempts to discover the causative agents of COVID-19 and SARS have revealed horseshoe bats (Rhinolophidae) to be the most probable source of the virus. We report the first detection of bat coronaviruses (BtCoVs) in insectivorous bats in Poland and highlight SARS-related coronaviruses found in Rhinolophidae bats. The study included 503 (397 oral swabs and 106 fecal) samples collected from 20 bat species. Genetically diverse BtCoVs (n = 20) of the Alpha- and Betacoronavirus genera were found in fecal samples of two bat species. SARS-related CoVs were in 18 out of 58 lesser horseshoe bat (Rhinolophus hipposideros) samples (31%, 95% CI 20.6–43.8), and alphaCoVs were in 2 out of 55 Daubenton’s bat (Myotis daubentonii) samples (3.6%, 95% CI 0.6–12.3). The overall BtCoV prevalence was 4.0% (95% CI 2.6–6.1). High identity was determined for BtCoVs isolated from European M. daubentonii and R. hipposideros bats. The detection of SARS-related and alphaCoVs in Polish bats with high phylogenetic relatedness to reference BtCoVs isolated in different European countries but from the same species confirms their high host restriction. Our data elucidate the molecular epidemiology, prevalence, and geographic distribution of coronaviruses and particularly SARS-related types in the bat population.
Pyroptosis-Related Gene Model Predicts Prognosis and Immune Microenvironment for Non-Small-Cell Lung Cancer

Non-small-cell lung cancer (NSCLC) has a high incidence and mortality worldwide. Moreover, it needs more accurate means for predicting prognosis and treatments. Pyroptosis is a novel form of cell death about inflammation which was highly related to the occurrence and development of tumors. Despite having some studies about pyroptosis-related genes (PRGs) and cancer, the correlation has not been explored enough between PRGs and immune in NSCLC. In this study, we constructed a PRG model by WGCNA to access the prognosis value PRGs have. The testing cohort (n = 464) with four datasets from the GEO database conducted a survival analysis to confirm the stability of the prognostic model. The risk score and age are examined as independent prognostic factors. Based on the PRGs, we found multiple pathways enriched in immune in NSCLC. Separating samples into three subtypes by consensus cluster analysis, Cluster 3 was identified as immune-inflamed phenotype with an optimistic prognostic outcome. A three-gene PRG signature (BNIP3, CASP9, and CAPN1) was identified, and BNIP3 was identified as the core gene. Knockdown of BNIP3 significantly inhibited the growth of H358 cells and induced pyroptosis. In conclusion, the model construction based on PRGs provides novel insights into the prediction of NSCLC prognosis, and BNIP3 can serve as a diagnostic biomarker for NSCLC.
Oxidative torrefaction and torrefaction-based biorefining of biomass: a critical review

Torrefaction is a vital pretreatment technology for thermochemical biorefinery applications like pyrolysis, gasification, and liquefaction. Oxidative torrefaction, an economical version of torrefaction, has recently gained much attention in the renewable energy field. Recent literature on inert and oxidative torrefaction was critically reviewed in this work to provide necessary guidance for future research and commercial implementations. The critical performance parameters of torrefaction for thermochemical biorefinery applications, such as solid yield, energy yield, carbon enhancement, higher heating value (HHV) enhancement, and energy-mass co-benefit index (EMCI), were also analyzed. Agricultural waste, woody biomass, and microalgae were considered. The analysis reveals that woody biomass could equally benefit from oxidative or inert torrefaction. In contrast, inert torrefaction was found more suitable for agricultural wastes and microalgae. Using flue gas as the oxidative torrefaction medium and waste biomass as the feedstock could achieve a circular economy, improving the sustainability of oxidative torrefaction for thermochemical biorefineries. The significant challenges in oxidative torrefaction include high ash content in torrefied agricultural waste, the oxidative thermal runaway of fibrous biomass during torrefaction, temperature control, and scale-up in reactors. Some proposed solutions to address these challenges are combined washing and torrefaction pretreatment, balancing oxygen content, temperature, and residence time, depending on the biomass type, and recirculating torrefaction gases.
Numerical solution and verification of the local equilibrium for the flat interface in the two-phase binary mixture

In this paper we first apply the general analysis described in our first paper to a binary mixture of cyclohexane and $n$-hexane. We use the square gradient model for the continuous description of a non-equilibrium surface and obtain numerical profiles of various thermodynamic quantities in various stationary state conditions. In the second part of this paper we focus on the verification of local equilibrium of the surface as described with excess quantities. We give a definition of the temperature and chemical potential difference for the surface and verify that these quantities are independent of the choice of the dividing surface. We verify that the non-equilibrium surface can be described in terms of Gibbs excess densities which are in good approximation equal to their equilibrium values at the temperature and chemical potential difference of the surface.
MULTIPLE SOLUTIONS FOR SUPERLINEAR p-LAPLACIAN NEUMANN PROBLEMS

Abstract Our main goal is to prove the existence of multiple solutions with precise sign information for a Neumann problem driven by the p-Laplacian differential operator with a (p 1)-superlinear term which does not satisfy the Ambrosetti–Rabinowitz condition. Using minimax methods we show that the problem has five nontrivial smooth solutions, two positive, two negative and the fifth nodal. In the semilinear case (p D 2), using Morse theory, we produce a second nodal solution (for a total of six nontrivial smooth solutions).
A Numerical Study of Evanescent Fields in Backward-Wave Slabs

Numerical study of evanescent fields in an isotropic backward-wave (BW) (double negative or "left-handed") slab is performed with the FDTD method. This system is expected to be able to restore all spatial Fourier components of the spectrum of a planar source, including evanescent fields, realizing a "superlens". The excitation of surface modes on the interfaces of the slab, which is the key process responsible for the sub-wavelength focusing, is numerically confirmed and the time-domain field behavior is studied. In particular, a numerical verification of the amplification of evanescent modes by an isotropic BW slab with epsilon=mu=-1 is provided.
Improving Network Representation Learning via Dynamic Random Walk, Self-Attention and Vertex Attributes-Driven Laplacian Space Optimization

Network data analysis is a crucial method for mining complicated object interactions. In recent years, random walk and neural-language-model-based network representation learning (NRL) approaches have been widely used for network data analysis. However, these NRL approaches suffer from the following deficiencies: firstly, because the random walk procedure is based on symmetric node similarity and fixed probability distribution, the sampled vertices’ sequences may lose local community structure information; secondly, because the feature extraction capacity of the shallow neural language model is limited, they can only extract the local structural features of networks; and thirdly, these approaches require specially designed mechanisms for different downstream tasks to integrate vertex attributes of various types. We conducted an in-depth investigation to address the aforementioned issues and propose a novel general NRL framework called dynamic structure and vertex attribute fusion network embedding, which firstly defines an asymmetric similarity and h-hop dynamic random walk strategy to guide the random walk process to preserve the network’s local community structure in walked vertex sequences. Next, we train a self-attention-based sequence prediction model on the walked vertex sequences to simultaneously learn the vertices’ local and global structural features. Finally, we introduce an attributes-driven Laplacian space optimization to converge the process of structural feature extraction and attribute feature extraction. The proposed approach is exhaustively evaluated by means of node visualization and classification on multiple benchmark datasets, and achieves superior results compared to baseline approaches.
Forest communities in secondary high Andean forests (Azuay, Ecuador)

In the mountains of southern Ecuador there are areas occupied by high Andean secondary forests formed as a result of anthropogenic activities. Here we identified different secondary forest communities located above 2,900 m a.s. l. , based on their floristic similarity. In each community the floristic composition was described by total, exclusive and shared species. Estimation curves were used to provide richness and diversity metrics. Structure was analyzed according to abundance and basal area. In addition, the role of environmental variables in explaining floristic conformation and structure was evaluated through principal component and redundancy analysis. Three forest communities were identified. The highest value in diversity and basal area was for the community located at the highest altitude and lowest temperature. Variation in species composition was explained by climatic and geographic environmental variables, density by edaphic and climatic variables, and basal area by topographic variables. Species richness and basal area did not show a similar altitudinal distribution pattern with other Andean tropical forests. Therefore, it was deduced that floristic variation, species richness and basal area are also explained by the chronological age of secondary succession, as shown by indicator species belonging to different ecological groups. It was concluded that floristic composition, richness and vegetation structure in forest communities of high Andean secondary forests are influenced by climatic, topographic, physiographic and geographic variables linked to the age of succession.
Integrability of type 0A matrix model in the presence of D-brane

We consider type 0A matrix model in the presence of spacelike D-brane which is localized in matter direction at any arbitrary point. In string theory, the boundary state, which in matrix model corresponds to the Laplace transform of the macroscopic loop operator, is known to obey the operator constraints corresponding to open string boundary condition. When we analyze MQM as well as the respective collective field theory and compare it with dual string theory, it appears that consistency of the theory requires a condition equivalent to a constraint on the matter part that needed to be imposed in the matrix model. We identified this condition and observed that this results in constraining the macroscopic loop operator so that it projects the Hilbert space generated by the operator to its physical sector at the point of insertion while keeping the bulk matrix model unaffected, thereby describing a situation parallel to string theory. We analyzed the theory with uncompactified time and have shown explicitly that the matrix model predictions are in good agreement with the relevant string theory. Next, we considered the theory with compactified time, analyzed MQM on a circle in the presence of D-brane. We evaluated the partition function along with the constrained macroscopic loop operator in the grand canonical ensemble and showed the free energy corresponds to that of a deformed Fermi surface. We have also shown that the path integral in the presence of D-brane can be expressed as the Fredholm determinant. We have studied the fermionic scattering in a semiclassical regime. Finally, we considered the compactified theory in the presence of the D-brane with tachyonic background. We evaluated the free energy in the grand canonical ensemble. We have shown the integrable structure of the respective partition function and it corresponds to the tau function of Toda hierarchy. We have also analyzed the dispersionless limit.
Efficient resource allocation for hybrid nonorthogonal multiple access based heterogeneous networks beyond fifth‐generation

Fifth‐generation (5G) and future beyond fifth‐generation (B5G) networks will require immense capacity due to the high rise in the number of multimedia applications and mobile devices. The heterogeneous networks (HetNets) in 5G and B5G can increase the heterogeneity and network throughput because macro base station‐only (MBS‐only) networks cannot satisfy the drastic increase in capacity demands. Hybrid nonorthogonal multiple access (H‐NOMA) can accommodate the increasing number of multimedia applications and mobile devices in 5G and B5G. H‐NOMA in B5G can also improve spectral efficiency. Before this, the researchers had not considered user clustering with H‐NOMA in HetNets. This article investigates the user clustering with downlink H‐NOMA in HetNet to maximize the network throughput in 5G and B5G. The formulated mathematical problem optimizes the considered key performance indicators (KPIs), that is, users admission in clusters, users association with base stations, power allocation to users, and network throughput. At the same time, it also satisfies the minimum transmit power and data rate requirements of users. The formulated problem is a mixed‐integer nonlinear programming (MINLP) problem. We have proposed an ϵ$$ \epsilon $$ ‐optimal algorithm, that is, outer approximation algorithm (OAA) to solve the MINLP problem because the complexity of the optimal exhaustive search algorithm (ESA) increases exponentially with an increase in the number of users. H‐NOMA with user clustering in HetNet is evaluated with extensive simulations to show its effectiveness regarding network throughput in 5G and B5G. We have also considered the proposed framework in the MBS‐only network and compared its performance with HetNet. The results verify that the proposed framework in HetNet performs better than the MBS‐only network. The complexity of the ϵ$$ \epsilon $$ ‐optimal algorithm is calculated that gives ϵ$$ \epsilon $$ ‐optimal results within ϵ=10−3$$ \epsilon =1{0}^{-3} $$ . We have also made a complexity analysis of the proposed OAA and ESA and concluded that the complexity of the proposed OAA algorithm is less than the ESA.
Alpha-Mangostin-Loaded Transferrin-Conjugated Lipid-Polymer Hybrid Nanoparticles: Development and Characterization for Tumor-Targeted Delivery

Alpha-mangostin, a natural xanthone mainly extracted from the pericarp of Garcinia mangostana, has been shown to have promising anticancer properties in many types of cancer. However, the therapeutic potential of α-mangostin has been limited so far due to its poor aqueous solubility and low oral bioavailability, which limited its biopharmaceutical applications. Furthermore, α-mangostin failed to specifically reach tumors at a therapeutic concentration due and rapid elimination in vivo. We hypothesized that this drawback could be overcome by loading the drug within a delivery system conjugated to transferrin (Tf), whose receptors are overexpressed on many cancer cells and would enhance the specific delivery of α-mangostin to cancer cells, thereby enhancing its therapeutic efficacy. The objectives of this study were therefore to prepare and characterize transferrin-conjugated lipid-polymer hybrid nanoparticles (LPHN) entrapping α-mangostin, as well as to evaluate their therapeutic efficacy in vitro. We successfully prepared α-mangostin loaded LPHN using a one-step nanoprecipitation method with high drug entrapment efficiency. The conjugation of Tf to the LPHN was achieved by using the thiol–maleimide “click” reaction, leading to an increase in the particle hydrodynamic size of Tf-LPHN compared to that of unconjugated (control) LPHN (Ctrl-LPHN). Both Tf-LPHN and Ctrl-LPHN were bearing negative surface charges. Tf-LPHN and Ctrl-LPHN exhibited a sustained release of α-mangostin at pH 7.4, following an initial burst release, unlike rapid release of drug solution. The entrapment of α-mangostin in the LPHN led to an increase in α-mangostin uptake by cancer cells, and thus improved its antiproliferative activity compared to that observed with the drug solution. In conclusion, α-mangostin entrapped in the Tf-LPHN is therefore a highly promising therapeutic system that should be further optimized as therapeutic tools for cancer treatment.