Robust Basis Research Articles

Layer-number and strain effects on the structural and electronic properties of PtSe2 material

Bandgap engineering of low-dimensional materials forms a robust basis for advancements in optoelectronic technologies. Platinum diselenide (PtSe2) material exhibits a transition from semi-metal to semiconductor (SM-SC) when going from bulk to monolayer. In this work, density functional theory (DFT) with various van der Waals (vdW) corrections has been tested to study the effect of the layer-number on the structural and electronic properties of the PtSe2material. The considered vdW corrections gave different results regarding the number of layers at which the SM-SC transition occurs. This variation is due to the different interlayer distances found for each correction, revealing the sensitivity of the bandgap to this distance in addition to the layer number. In fact, the bandgap increases with the increasing of the interlayer distance, due to the energy shift of conduction and valence bands dominated by Se-pzorbitals. According to the comparison with the available experimental data, the vdW corrections vdW-DF and rVV10 gave the most accurate results. Moreover, the control of the interlayer distance via vertical compressive strain led to the bandgap tuning of semiconductor PtSe2BL. Indeed, a semi-metal character of PtSe2BL can be obtained under 17% vertical strain. Our work shows a deep understanding of the correlation between the structural and electronic properties, and thus a possibility to tune the bandgap by strain means.

Epidemiological and pathological characterization of acute respiratory infections.

This research comprehensively investigates the epidemiological features and pathogen profile of acute respiratory infections (ARI) in Shihezi City, Xinjiang. A pivotal aspect of this study is the construction of a Bayes discriminant function for principal pathogen infections. This innovative methodology aims to furnish a robust scientific basis for the prevention and clinical management of ARI, potentially guiding more effective strategies in both public health and clinical settings. We compiled and examined data from January 2020 to June 2023, pertaining to patients admitted with acute respiratory infections at the First Affiliated Hospital of Shihezi University. This investigation focused on discerning patterns in epidemiology and pathogen etiology. Among 2110 cases of acute respiratory infections (ARI), 1736 underwent pathogenetic testing. Of these, 595 cases tested positive for at least one pathogen, marking a positivity rate of 34.27%. Viral detections, at a rate of 27.47%, were notably higher than bacterial detections, which stood at 6.51%. The most prevalent viruses identified were Human respiratory syncytial virus (hRSV), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and Human adenovirus (HAdV), while the dominant bacterial pathogens included Klebsiella pneumoniae, Haemophilus influenzae, and Staphylococcus aureus. Co-infections were observed in 76 cases, accounting for 12.77% of positive diagnoses, predominantly involving hRSV in conjunction with other pathogens. In cases of acute bronchiolitis, hRSV was the most frequent pathogen, contributing to 23.10% of such cases. Similarly, in severe pneumonia cases, SARS-CoV-2 was predominant, accounting for 25.4% of these infections. The group with bacterial positivity exhibited elevated levels of C-reactive protein (CRP, 19.17 mg/L) and neutrophilic granulocyte percentage (NE%, 54.7%). The Bayes discriminant function demonstrated an initial validation accuracy of 74.9% and a cross-validation accuracy of 63.7%. The study underscores that hRSV, SARS-CoV-2, and HAdV are the primary pathogens in acute respiratory infections in the Shihezi region. Pathogen susceptibility exhibits variation across different age groups, with a higher pathogen detection rate in children compared to adults. The Bayes discriminant function shows significant promise in the classification and diagnosis of major pathogenic infections.

Genotype by Environment Interaction Using AMMI, GGE Biplot and Multivariate Analysis of Nepalese Aromatic Rice Landraces

Aromatic group of rice comprises fine, medium, long and extra-long grain that has rich in agro-biodiversity, carries historical and cultural significance in Nepalese society. The objective was to identify stable, high-yielding aromatic rice landraces for Nepal Thirty aromatic rice landraces including two released varieties as standard checks were evaluated at three locations during 2020 and 2021. The experiment was laid out in a Randomized Complete Block Design with three replications. The combined analysis of variance indicated differences among genotypes and genotype-environment interactions for grain yield. The additive main effects and multiplicative interaction (AMMI) analysis revealed that genotypic effects for 69%, genotype × environment interaction effects for 28% and environmental effects accounted for 3% of the total sum of squares. Similarly, GGE biplot analysis showed that the principal components (PC1 and PC2) accounted for 53% and 47% of the GGE sum of squares, respectively. Based on AMMI1, AMMI2 biplots, and GGE biplots, the genotypes Samba Masuli Sub-1 and Sugandhit Dhan-1 were identified as the high yielding and stable fine and aromatic rice landraces. Other promising genotypes are Damari Dhan, Tilki, Hiupuri, Kalo Basmati, Gouriya, and Brahmamusi Dhan. Specifically, Samba Masuli Sub-1 demonstrated the highest grain yield performance at the NRRP, Hardinath, while Sunaulo Sugandha and Balamsari Dhan excelled in the Tarahara and Khajura environments, respectively. In PCA analysis, four PCs showed significant eigen value >1.0 contributing more than 84% of total variance. Biplot analysis reflected that 1000 grain weight, panicle length, heading and maturity days, and grain yield showed greater variation among the landraces. The results provide a robust basis for the further evaluation and verification of this findings in advance stage trials. The superior and outstanding landraces with higher yields and aroma are potential future varieties that can be either use directly as varieties after selection or utilize in future breeding programs for the development of new aromatic rice varieties.

The Global Trend of Drug Resistant Sites in Influenza A Virus Neuraminidase Protein from 2011 to 2020.

Influenza A virus (IAV) causes highly contagious respiratory disease worldwide, so prevention and control of IAV is extremely important. However, overuse of neuraminidase inhibitor (NAI) drugs leads to drug resistance. To explore the up-to-date geographical distribution and evolution of drug-resistant mutations (DRMs) in the NA protein of IAV, 81,492 near full-length NA sequences downloaded from NCBI and GISAID databases, including 34,481 H1N1 and 46,622 H3N2, were processed and analyzed. Our results showed the annual number of NA sequences from 2011 to 2019 continuously increased. Meanwhile, almost 85% of sequences were from developed countries in North America, Europe and Asia. Clustering analysis demonstrated H3N2 varied more than H1N1. Notably, H3N2 exhibited a higher frequency of DRMs than H1N1, with prevailing DRMs mainly located at non-active sites within the NA protein. Phylogenetic analyses showed NA harboring DRMs collected in the same year and from the same location clustered together, which may be related to the local economic level, clinical monitoring of DRMs and research level. Consequently, it is imperative to enhance global surveillance targeting drug resistance in IAV infections which can mitigate the transmission of drug-resistant strains. In summary, our research provides valuable insights for clinical medication while establishing a robust scientific basis for IAV prevention and treatment strategies to improve overall efficacy.

Key environmental parameters and numerical prediction model of jellyfish bloom in Qingchuan Bay Nuclear Power Plant, China

In recent years, there has been a notable increase in cooling water intake blockage caused by marine organism blooms at coastal nuclear power plants worldwide, resulting in shutdowns of nuclear power plants and large economic losses. A sizable portion of these incidents were caused by blooms from jellyfish, a planktonic invertebrate with a unique growth pattern. Suitable external conditions are conducive to the rapid growth of jellyfish, and blooms can occur within a few days. In order to better predict jellyfish bloom and enable nuclear power plants to prepare for it in advance, this study explores the numerical relationship between jellyfish biomass and environmental parameters. A series of time windows (evaluation intervals) were defined and constructed by a time-series recursive approach, which solved the problem of poor correlation between jellyfish biomass and environmental parameters at non-bloom points. The optimal time window length D = 10 was obtained, and the key environmental parameters affecting jellyfish biomass were screened as sea surface temperature, salinity, voltage value, dissolved oxygen, and chlorophyll. According to the ADF and KPSS tests, the key parameters have no significant time dependence, which ensures the stability and reliability of the subsequent predictions. The jellyfish bloom prediction model was derived by calculating the score F through the recursive Principal Component Analysis of the key environmental parameter in the time interval preceding the prediction point. The sudden change moments of score F correspond well to the jellyfish bloom moments, and the sudden change moments are all advanced for a period of time compared to the bloom time, which can provide valuable time for the nuclear power plant to organize manpower to deal with the blockage. Finally, a maximum score F threshold model was proposed to be coupled with the jellyfish bloom prediction model to provide a more robust basis for early warning of jellyfish at nuclear power plants.

Pre-clinical development of AP4B1 gene replacement therapy for hereditary spastic paraplegia type 47.

Spastic paraplegia 47 (SPG47) is a neurological disorder caused by mutations in the adaptor protein complex 4 β1 subunit (AP4B1) gene leading to AP-4 complex deficiency. SPG47 is characterised by progressive spastic paraplegia, global developmental delay, intellectual disability and epilepsy. Gene therapy aimed at restoring functional AP4B1 protein levels is a rational therapeutic strategy to ameliorate the disease phenotype. Here we report that a single delivery of adeno-associated virus serotype 9 expressing hAP4B1 (AAV9/hAP4B1) into the cisterna magna leads to widespread gene transfer and restoration of various hallmarks of disease, including AP-4 cargo (ATG9A) mislocalisation, calbindin-positive spheroids in the deep cerebellar nuclei, anatomical brain defects and motor dysfunction, in an SPG47 mouse model. Furthermore, AAV9/hAP4B1-based gene therapy demonstrated a restoration of plasma neurofilament light (NfL) levels of treated mice. Encouraged by these preclinical proof-of-concept data, we conducted IND-enabling studies, including immunogenicity and GLP non-human primate (NHP) toxicology studies. Importantly, NHP safety and biodistribution study revealed no significant adverse events associated with the therapeutic intervention. These findings provide evidence of both therapeutic efficacy and safety, establishing a robust basis for the pursuit of an IND application for clinical trials targeting SPG47 patients.

A Shared Economy Data Prediction Model Based on Deep Learning

In the dynamic and intricate shared economy, efficient resource management and forecasting are still crucial. In this research, a new prediction model is presented that aims to improve the operational efficiency of several shared economy services. Our method creatively combines Long Short-Term Memory (LSTM) networks with Genetic Algorithms (GA) to assess user demand and optimize resource deployment. We apply this methodology especially to e-scooter sharing services, but the underlying ideas and methods can be applied to other shared economy platforms as well, like peer-to-peer lending services, car sharing, and vacation rentals. The model starts with a GA to adjust the hyperparameters of the LSTM network, making the network better suited to handle specific characteristics of common economic data. Capturing the complex temporal and spatial patterns of user behavior and demand on these platforms requires this optimization. The LSTM element then predicts changes in service demand due to its capacity to analyze sequential records. Further to being useful for analyzing sequential data and predicting destiny wishes, this predictive functionality is important for a shared economy platform to correctly manage stock, allocate assets, and predict personal wishes. We use a large dataset to check our technique, demonstrating the predictive accuracy of the model and demand and its potential to aid strategic choice-making. Compared to traditional fashions, the consequences show a large development in forecast accuracy and resource allocation efficiency. Our methodology creates a robust basis for statistics-driven insights that decorate customer happiness and decorate the long-term increase of the shared financial system. This work highlights the blended ability of GA and LSTM inside the shared economy and paves the manner for future enhancements in using modern-day gadget mastering techniques to optimize and alter various shared services. In quick, effective useful resource control and forecasting in the shared economic system is tough, however our specific forecasting model combining GA-LSTM gives a manner ahead. Our technique, as it should be, predicts fluctuations in provider demand; it was first refined the usage of GA to regulate the LSTM hyperparameters. The consequences show how correct and powerful our version is and spotlight how it can enhance customer pleasure and operational performance. This research paves the manner for future trends within the software of gadgets, gaining knowledge of methods and supports the continuing enlargement and development of shared economic system offerings.

Explore the mechanism of mulberry leaf combined with gut microbiota to improve hyperlipidemia based on network pharmacology

PurposeTo elucidate the underlying mechanisms by which mulberry leaves, in conjunction with gut microbiota, ameliorate hyperlipidemia and identify key gut microbial taxa involved, a mulberry leaf/gut microbiota-active components-metabolites-target-pathway network was constructed.MethodologyInformation on active constituents of mulberry leaves, metabolites generated by gut microbiota, and target proteins associated with hyperlipidemia were retrieved from multiple databases. Employing bioinformatics tools, relevant pathways and targets involved in the synergistic effect of mulberry leaves and gut microbiota on hyperlipidemia improvement were systematically analyzed. Through multi-level data analysis and integration of existing knowledge on the interplay between mulberry leaves, gut microbiota, and hyperlipidemia, the atherosclerosis signaling pathway was identified as the central pathway, with AKT1 as the core target protein. Based on metabolites produced by gut microbiota that act on AKT1, 29 specific gut microbial strains were found to exhibit synergistic action within the context of mulberry leaf-mediated hyperlipidemia alleviation. This culminated in the establishment of a comprehensive mulberry leaf/gut microbiota-active components-metabolites-target-pathway network. Moreover, molecular docking and visualization analyses demonstrated stable interactions between the selected gut microbiota-derived metabolites and the central target AKT1.ConclusionThese findings provide a robust scientific basis for the application of mulberry leaf-based formulations combined with probiotics in the development of specialized medical foods targeting hyperlipidemia, grounded in the elucidated molecular mechanisms and identified key gut microbial players.

Rainwater harvesting through roofs and stormwater harvesting through pervious pavements: A Life Cycle Assessment and decision-making comparison

Water consumption and the building sector are among the top contributors to environmental impacts worldwide. Also, sustainable practices have been constantly encouraged in both sectors to mitigate water scarcity and provide resilience to cities. Thus, studies that provide an environmental assessment and discuss the decision-making process are essential for enabling the augmentation of sustainable practices. This research aims to contribute to the discussion on sustainability by conducting a Life Cycle Assessment (LCA) of Sustainable Urban Drainage Systems (SUDS) examples. LCA is the main methodology applied to perform environmental comparisons. First, a thorough inventory was obtained from previous works with different alternatives for water management within a university building, including drainage, paving and water supply functions. The inventory contemplates alternatives using rainwater and stormwater harvesting for non-potable water supply, including pervious pavements. Such alternatives were compared with Brazil's usual water supply systems, aiming at diminishing the total environmental burden. The methodology aspects of LCA were also regarded, such as an uncertainty assessment and a quality matrix of the processes used. Midpoint and endpoint categories were also regarded with their decision-making and uncertainty analysis. The main result is the understanding of the trade-offs between the proposed systems, with a significant reduction in water stress obtained in the alternative scenarios. However, such a decrease is accompanied by a slight increase in some impact categories. Rainwater harvesting through roofs is the most environmentally friendly alternative, although the Cultural Theory parameter definition impacts on the sustainability ranking. Alternative sustainable systems for water management still need validation in terms of LCA, while decision-making for sustainable and resilient cities also requires a robust scientific basis. Thus, in conclusion, alternative systems such as SUDS were obtained to be more sustainable than conventional paving, drainage and water supply in the case study, and the understanding of different benefits found in the LCA is important to the decision-making process.

Tailoring thermal transport and confinement effect of GaN/Si3N4 nanowires utilizing core–shell architecture

The thermal transport properties of nanowires (NWs) can be significantly influenced by the implementation of a core-shell structure, which introduces interface scattering and phonon localization effects, opening avenues for novel applications. In this paper, we use the method of non-equilibrium molecular dynamics to simulate the effects of system temperature, cross-sectional width, and nanopillar interface on the thermal transport of GaN/Si3N4core-shell NWs. The thermal transport process of phonons in core-shell NWs is studied by calculating the vibrational density of states, phonon participation rate, and dispersion curve. The results show that the core-shell NWs characterized by smooth interfaces exhibit a 17.4% decrease in thermal conductivity (TC) at room temperature when contrasted with pristine GaN NWs. Furthermore, the TC of GaN/Si3N4core-shell NWs can be further reduced by adding nanopillars at the interface. Due to resonance effect, thus effectively regulating the thermal transport. The presence of nanopillars increases phonon-surface scattering intensity at low-frequency and modifies phonon dispersion to decrease the group velocity. In addition, the hybridization of phonon modes between those of the nanopillars and the Si3N4shell gives rise to numerous dispersionless resonance phonon modes that span the entire phonon spectrum. This research delves into the effects of nanopillars and interfaces on thermal transport, providing important guidance for understanding confinement effects and establishing a robust theoretical basis for the regulation of thermal transport.

Essential Steps and Strategic Considerations for Successfully Launching a Small-Scale Business

To achieve a successful launch of a small-scale firm, one must carefully navigate through many crucial procedures and strategic considerations. This review essay thoroughly analysed the fundamental activities and techniques required for a successful company’s commencement. Essential elements include completing comprehensive market research to find possibilities and comprehend competition, formulating a comprehensive business plan that delineates objectives, strategies, financial forecasts, and operational plans, and obtaining sufficient finance. The research highlighted the significance of robust financial management methods, including budgeting, cash flow management, and financial forecasting, to guarantee stability and facilitate development. Efficient allocation of resources, adaptation to market changes, and harnessing technology were important strategic considerations for overcoming common difficulties and attaining lasting success. The essay consolidated the latest research and optimal methodologies, offering practical insights for entrepreneurs to improve their company performance and overcome frequent challenges. Suggestions included consulting with specialists, being attentive to market fluctuations, and enhancing the availability of financial resources and support networks. This exhaustive analysis provided a beneficial structure for small-scale enterprises seeking to develop a robust basis for expansion and long-term sustainability. Keywords: Small-scale business, Business launch, Market research, Business plan, Financial management.

Improving efficiency in the emergency department Monte Carlo simulation for determining the optimal number of staff

This paper examines the application of Monte Carlo simulation to determine optimal staffing levels at reception desks in the Emergency Department. The study utilizes data collected from a hospital in Bogotá, with all data anonymized to maintain the confidentiality of both the institution and its patients. By leveraging programming tools, the study randomizes the data and models various scenarios to assess the staffing requirements accurately. The primary goal is to enhance the efficiency and quality of service by aligning staffing levels with patient demand. The use of historical data, combined with the simulation of hypothetical scenarios, provides a robust basis for predicting future needs and making informed staffing decisions. The study's findings offer valuable insights into human resources management, enabling the Emergency Department to strategically allocate personnel, minimize wait times, and improve overall patient care. This approach demonstrates the potential for simulation-based models to optimize resource allocation in critical healthcare environments.

Spectral methods utilizing generalized Bernstein‐like basis functions for time‐fractional advection–diffusion equations

This paper presents two methods for solving two‐dimensional linear and nonlinear time‐fractional advection–diffusion equations with Caputo fractional derivatives. To effectively manage endpoint singularities, we propose an advanced space‐time Galerkin technique and a collocation spectral method, both employing generalized Bernstein‐like basis functions (GBFs). The properties and behaviors of these functions are examined, highlighting their practical applications. The space‐time spectral methods incorporate GBFs in the temporal domain and classical Bernstein polynomials in the spatial domain. Fractional equations frequently produce irregular solutions despite smooth input data due to their singular kernel. To address this, GBFs are applied to the time derivative and classical Bernstein polynomials to the spatial derivative. A thorough error analysis confirms the technique's accuracy and convergence, offering a robust theoretical basis. Numerical experiments validate the method, demonstrating its effectiveness in solving both linear and nonlinear time‐fractional advection–diffusion equations.

Laue-DIALS: Open-source software for polychromatic x-ray diffraction data.

Most x-ray sources are inherently polychromatic. Polychromatic ("pink") x-rays provide an efficient way to conduct diffraction experiments as many more photons can be used and large regions of reciprocal space can be probed without sample rotation during exposure-ideal conditions for time-resolved applications. Analysis of such data is complicated, however, causing most x-ray facilities to discard >99% of x-ray photons to obtain monochromatic data. Key challenges in analyzing polychromatic diffraction data include lattice searching, indexing and wavelength assignment, correction of measured intensities for wavelength-dependent effects, and deconvolution of harmonics. We recently described an algorithm, Careless, that can perform harmonic deconvolution and correct measured intensities for variation in wavelength when presented with integrated diffraction intensities and assigned wavelengths. Here, we present Laue-DIALS, an open-source software pipeline that indexes and integrates polychromatic diffraction data. Laue-DIALS is based on the dxtbx toolbox, which supports the DIALS software commonly used to process monochromatic data. As such, Laue-DIALS provides many of the same advantages: an open-source, modular, and extensible architecture, providing a robust basis for future development. We present benchmark results showing that Laue-DIALS, together with Careless, provides a suitable approach to the analysis of polychromatic diffraction data, including for time-resolved applications.

Image encryption algorithm based on multiple chaotic systems and improved Joseph block scrambling

Abstract With the rapid development of digital information technology, images are increasingly used in various fields. To ensure the security of image data, prevent unauthorized tampering and leakage, maintain personal privacy, and protect intellectual property rights, this study proposes an innovative color image encryption algorithm. Initially, the Mersenne Twister algorithm is utilized to generate high-quality pseudo-random numbers, establishing a robust basis for subsequent operations. Subsequently, two distinct chaotic systems, the autonomous non-Hamiltonian chaotic system and the tent-logistic-cosine chaotic mapping, are employed to produce chaotic random sequences. These chaotic sequences are used to control the encoding and decoding process of the DNA, effectively scrambling the image pixels. Furthermore, the complexity of the encryption process is enhanced through improved Joseph block scrambling. Thorough experimental verification, research, and analysis, the average value of the information entropy test data reaches as high as 7.999. Additionally, the average value of the number of pixels change rate (NPCR) test data is 99.6101%, which closely approaches the ideal value of 99.6094%. This algorithm not only guarantees image quality but also substantially raises the difficulty of decryption.

S. glabra exerts anti-lung cancer effects by inducing ferroptosis and anticancer immunity

BackgroundSarcandra glabra (S. glabra), a traditional Chinese medicine (TCM), has demonstrated significant anticancer activity; however, the underlying mechanisms have not yet been fully elucidated. PurposeThis study aimed to investigate the effects of S. glabra on lung cancer and to explore its underlying mechanisms. MethodsThe chemical profile of S. glabra was analyzed via ultrahigh-performance liquid chromatography coupled with mass spectrometry (UPLC-MS). The effects of S. glabra on the viability, proliferation, apoptosis, migration, and invasion of lung cancer cells were assessed via CCK8, colony formation, flow cytometry, scratch, and Transwell assays. In vivo anticancer activity was evaluated in an LLC mouse model. Proteomic analysis was performed to identify key molecules and pathways in S. glabra-treated LLC cells. The expression of ferroptotic proteins and associated cellular events were examined via western blotting, ROS production, iron accumulation, and lipid peroxidation assays. Immune modulation in tumor-bearing mice was evaluated by detecting immune cells and cytokines in the peripheral blood and tumor tissue. ResultsOur analysis quantified 1997 chemical markers in S. glabra aqueous extracts. S. glabra inhibited the viability and proliferation of lung cancer cells and induced cell cycle arrest and apoptosis. Scratch and Transwell assays demonstrated that S. glabra suppressed the migration and invasion of lung cancer cells. Oral administration of S. glabra significantly inhibited tumor growth in LLC tumor-bearing mice. Proteomic analysis revealed that S. glabra upregulated the expression of the HMOX1 protein and activated the ferroptosis pathway. Consistent with these findings, we found that S. glabra triggered ferroptosis in lung cancer cells, as evidenced by the upregulation of HMOX1, downregulation of GPX4 and ferritin light chain proteins, iron accumulation, increased ROS production, and lipid peroxidation. Furthermore, S. glabra demonstrated immunostimulatory properties in LLC tumor-bearing mice, leading to increased populations of immune cells (NK cells) and elevated cytokine levels (IL-2). ConclusionThis study is the first to demonstrate that S. glabra induces ferroptosis in lung cancer cells by regulating HMOX1, GPX4, and FTL. These findings provide a robust scientific basis for the clinical application of S. glabra in lung cancer treatment.

Legal regulation of rehabilitation assistance to military personnel during martial law

The scientific article delves into the legal regulation of rehabilitation assistance to military personnel during martial law. The research, aimed at establishing the legal basis for providing rehabilitation assistance to military personnel during martial law, is underpinned by a robust methodological basis. The research methods employed are not only general scientific but also special legal, including theoretical-legal, formal-dogmatic, and comparative-legal methods. This thorough approach instills confidence in the validity of the research. The results of the research consist in determining the legal basis for providing rehabilitation assistance to military personnel. It has been established that the legal regulation of providing rehabilitation assistance to military personnel is carried out by such Laws of Ukraine as "Basics of Ukrainian legislation on health care," "On rehabilitation in the field of health care," "On social and legal protection of military personnel and their family members." The specifics of providing rehabilitation assistance to military personnel during martial law are determined. It has been established that the Ministry of Defense of Ukraine’s structure includes a network of centers for medical rehabilitation and sanatorium-resort treatment of military personnel. It has been found that rehabilitation assistance for military personnel can occur in rehabilitation institutions and healthcare institutions with rehabilitation departments, subordinated to the Ministry of Health of Ukraine or other ministries. The legal grounds for sending military personnel to provide rehabilitation in foreign healthcare institutions have been analyzed. It is indicated that the decision to send military personnel to a rehabilitation institution for rehabilitation assistance is made based on the conclusion of the military medical commission. The importance of the existing state target program for providing rehabilitation assistance to defenders of Ukraine is emphasized. A conclusion was drawn on the necessity of enshrining a separate section in the Law of Ukraine, "On Rehabilitation in the Field of Health Care," dedicated to the specifics of the rehabilitation of military, injured in hostilities.

Effect of NaCl on heat-denatured wheat gluten in freeze-thaw cycles: Physicochemical properties and molecular structure

Heat-denatured wheat gluten (HDWG) degradation during freeze-thaw (FT) cycling is a key factor in the quality deterioration of pre-cooked noodles (PCNs). This study investigated the effects and underlying mechanisms of sodium chloride (NaCl) on HDWG. Adding NaCl enhanced the luminance of PCNs and reduced chromatic aberration. Scanning electron microscopy revealed that NaCl facilitated a more continuous microstructure, resulting in a reduction the average hole size of HDWG from 84.44 μm to 56.59 μm after 10 FT cycles. With the FT cycling, gluten macropolymer (GMP) depolymerization occurs, causing the collapse of HDWG network structure. However, NaCl contributed to the formation of β-sheet and gauche-gauche-gauche (g-g-g) conformations, resulting in a stronger three-dimensional structure of HDWG, thus alleviating GMP depolymerization. The analysis of intermolecular interactions shows that NaCl primarily affects hydrogen bonds, disulfide bonds, and hydrophobic interactions, and stabilizes the structural integrity of HDWG by balancing these forces. NaCl altered the three-dimensional structural composition of HDWG by influencing the intermolecular interactions, thereby fortifying the PCNs against FT cycling. This study provides a robust theoretical basis for using NaCl to regulate the quality of thermally precooked flour products.

Forecasting international tourist arrivals in South Korea: a deep learning approach

Purpose This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism. Design/methodology/approach This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism. Findings This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management. Originality/value This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

The Assessment of Precipitation and Droughts in the Aegean Region Using Stochastic Time Series and Standardized Precipitation Index

This study analyzes drought conditions in the Aegean region using monthly precipitation data from nine stations between 1972 and 2020. The Standardized Precipitation Index (SPI) was calculated for 1-, 3-, 6-, 9-, and 12-month periods to evaluate drought conditions at different timescales and station-specific conditions. The results indicate that short-term droughts are more frequent but shorter in duration, while longer periods exhibit fewer but more prolonged droughts. The relative frequency of drought across all periods ranges between 9% and 27%. The İzmir and Denizli stations were highlighted due to their representation of coastal and inner regions, respectively. The findings show that coastal stations, like İzmir, experience more frequent wet years compared to inner stations like Denizli, which have more dry years. Time series linear autoregressive (AR) models, using SPI-12 data, were developed to represent long-term drought trends and forecasts. The best-fitting models were determined using AIC, AICC, FPE, and Var(e) criteria, with AR(2) generally being the most suitable, except for Denizli. This integrated analysis of SPI and AR models provides a robust basis for understanding regional precipitation regimes and predicting future droughts, aiding in the development of effective drought mitigation strategies and water resource management. Future research is anticipated to extend this analysis to encompass all of Turkey and explore various time series models’ applicability.