Nano-drugs and nano-delivery systems are rapidly evolving, with new strategies emerging in the current practices. The evolution of these technologies began with modifying the chemical structure, progressing to supramolecular ionic complexes, and culminating in elegant ad hoc delivery systems. Nanoparticles have numerous benefits as a carrier system for delivering therapeutic agents to intra-arterial sites. These benefits include their subcellular size, targeted surfaces, good suspensibility, and uniform dispersity, making them an ideal choice for catheter-based delivery. Despite the advancements made in the field of nano-drugs and nano-delivery systems, there are still some hurdles to overcome in terms of their commercial availability.The current review presents an updated summary of recent advancements in nano-drugs and nano-delivery systems, including their commercial availability. We aim to discuss the present challenges and prospects of commercially available nano-drugs and nano-delivery systems. Here, we provide a precise and informative overview of the current state of these technologies and underscore the potential they hold for future developments. Further, we have categorized commercially available modifications, name, parent company and their main applications in nano-drugs.

Walls-like structured monoclinic WO3 thin films were obtained by spray deposition method for further integration in gas sensors. For sensing applications, it is very important to achieve a high surface-to-volume ratio surface together with a proper thin films structure. The present communication reports on the effect of the sprayed solution volume variation (as a thickness variation element) on the detailed Raman spectroscopy for WO3 thin films with different thicknesses grown from precursor solutions with two different concentrations. Detailed analysis of the two series of samples shows that the growing thickness strongly affects the films morphology while their crystalline structure is only slightly affected. The Raman analysis contribute to refining the structural features clarifications. It was observed that, for both concentrations, the increase in thickness seems to slightly affect the stoichiometry of material and, in association with surface morphology variations, provide new possibilities for fine tuning of films properties towards enhanced as sensing applications. Further studies are ongoing.

The intranasal route of drug administration is characterized by high bioavailability and is considered promising for rapid delivery of drugs with systemic action to the central nervous system, bypassing the blood-brain barrier. This is particularly important for the use of neuroprotective drugs in the treatment of brain tissue damage in infants caused by the effects of intrauterine hypoxia. Creation of new dosage forms for neonatology with the use of software and mathematical technologies in pharmaceutical development allows to create cerebroprotective drugs with controlled pharmaco-technological properties, thus reducing time and resources for necessary research. We developed a formulation of a new nasal gel formulation with Angiolin using Box-Benken experiment design for the therapy of prenatal CNS damage. It was found that the consistency properties of the nasal gel were significantly influenced by the gelling agent and mucoadhesive component - sodium salt of carboxymethylcellulose. We have optimized the composition of nasal gel formulation with Angiolin using the formed models and relationships between the factors. The optimized composition of the obtained nasal gel has satisfactory parameters of thixotropic properties. The developed 1% gel for neuroprotection with Angiolin meets all the requirements for harmlessness and safety to drug forms of this group - low toxicity, absence of local irritant and allergic effect.

In this paper, based on our previous expertise on ceramic artifacts, several archaeometric methods applied to some samples collected from the Dudești archaeological site (Oltenia region, Romania) are reported for the first time in the literature. The chemical composition and microstructural and morphological characterization of these samples offer important conclusions about the processing conditions. Some specific techniques such as X-ray diffraction (XRD), wave-length-dispersive X-ray fluorescence (WDXRF), Optical microscopy (OM), stereomicroscopy, scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy, provide composition information about pigments and their decay processes. Additionally, the Brunauer-Emmett-Teller (BET) method helps to estimate pore sizes and specific surface areas. Thermal analysis (TGA) was used to establish details regarding the production technology and the raw materials source used to make the ceramics, too. The obtained results indicated that the ceramics are based on a paste of muscovite, feldspar, with high plasticity, quartz, melanterite, and calcite, the latter in very low concentrations. According to the obtained results, we could assume that non-calcareous clays are present in the investigated samples because calcium exists in low concentrations. Gypsum is present, too, in very low concentration, identified by the presence of sulfate group in WDXRF. In the same context, iron oxides significantly impact the firing atmosphere of iron-rich clay, resulting in blackening under reducing conditions and reddish coloration under oxidative conditions. Using hematite and gypsum as pigments further contributes to the color variations in the pottery. The consistent firing temperature range of 200-600 °C in Dudești pottery implies a standardized production process, the variation in color being dependent on the specific reducing/oxidative regime conditions. This relationship between clay composition and local sources suggests a connection to Neolithic pottery production in the region and their color depending on the reducing/oxidative regime conditions.

Abstract: Short-chain fatty acids (SCFAs) are the major microbial metabolites produced in the gut through the fermentation of non-digestible fibers. SCFAs act through free fatty acid receptor 2 and 3 (FFAR2 and 3), which are known to be expressed on glucagon-like peptide-1 (GLP-1)-secreting L-cells. This pathway is suggested to protect the intestinal mucosa. We aimed to investigate how lack of fiber affects the production of SCFAs and the vulnerability of the co-lon. Additionally, the involvement of FFAR2 and 3 in GLP-1 secretion and intestinal protection. SCFA concentrations in luminal contents from chow- or fiber-free diet-fed mice were analysed by LC‒MS. SCFAs were applied to isolated perfused colons from FFAR2/3 double KO and WT mice, and the secretion of GLP-1 was measured. Dextran sulfate sodium (DSS) colitis was induced in chow- or fiber-free diet-fed mice as well as in FFAR2/3 KO and WT mice. The removal of die-tary fiber significantly decreased total colonic SCFA concentrations, with specific decreases in acetate, propionate, and butyrate. SCFAs significantly increased colonic GLP-1 secretion but not in FFAR2/3 KO mice. In the DSS model, the removal of dietary fiber increased the severity and delayed recovery. Colitis severity was independent of FFAR2 and FFAR3 signaling.

Hybrid reinforcement in a composite material (CM), in many situations, allows better govern material mechanical properties. Polymer matrix CM plate is reinforced with small particles and macro-fibers. Particles are oil shale ash (OSA) powder, macro-fibers – basalt fiber threads im-pregnated by matrix material. With the goal to find out elastic properties of the matrix- polymer with OSA, samples with its different concentrations were experimentally fabricated and their elastic properties were experimentally measured. Obtained data were used in numerical model-ling. Was observed polymer matrix composite slab, reinforced with OSA and three-dimensional (3D) basalt fiber fabric. Fabric are forming threads impregnated by matrix (polymer resin mixed with OSA particles). Damage accumulation in the stretched slab was modelled. Impregnated thread, in the modelling, is observed as a macro-fiber (MF) with averaged elastic properties. With the load increase MF in the material began to break. At first, appeared isolated single breaks. A probabilistic sequential MF rupture accumulation model was accepted and numerical-ly realized. The geometry of the reinforcing knitted fabric was modeled using the Leaf and Glaskin approach. In the knitted fabric highly, stressed MF cross-sections are observed as po-tential location where thread breakage can occur. If one thread breaks, the cross-sections of the adjacent threads experience the overload caused by this rupture. More overloaded crossection in the adjacent overloaded thread breaks and we obtain two adjacent broken MF designated as a defect with the size j=2. The probabilities of obtaining defect consisting of at least 2, 3, … j adjacent broken MF in the plate were calculated. The calculations continued until the rupture process becomes unstable (j→∞). The overstresses on adjacent threads in the CM were calculat-ed using the FEM three-dimensional numerical approach. Analytical and stochastic methods were used for the load-carrying capacity determination and different size defects accumulation in the hybrid composite material (HCM) plate. Also, was calculated the probability of the plate rupture.

The application of multiple criteria decision-making (MCDM) techniques in real-life problems has increased in recent years. The need to build advanced decision models with higher capabilities that can support decision making in a broad spectrum of applications, promotes the integration of MCDM techniques with applicable systems, including artificial intelligence, and Geographic Information Systems (GIS). The Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are amongst the most widely adopted MCDM techniques capable of resolving water resources challenges. A critical problem associated with water resource management is dam site selection. This paper presents a comparative analysis of TOPSIS and AHP in the context of decision making using GIS for dam site selection. The comparison was made based on geographic and water quality criteria. A ratio estimation procedure was used to determine the weights of these criteria. Both methods were applied for selection of optimal sites for dams in the Sistan and Baluchestan province, Iran. The results show that the TOPSIS method is better suited to the problem of dam site selection for this case study. Actual locations of dams constructed in the area were used to verify the results of both methods.

In this study, functionalized-MWCNTs with 3-aminopropyltriethoxysilane (APTES) loaded titania nanoparticles (MWCNTs-APTES-TiO₂) were prepared to investigate their physicochemical properties and photocatalytic activity for methyl orange (MO) degradation. The TiO₂ nanoparticles, functionalized-MNCNT and composite powders were characterized by XRD, raman, and TEM. The results obtained proved that titania (TiO₂) nanoparticles was successfully loaded on APTES-MWCNTs. The best photocatalytic degradation of methyl orange solution under UV light irradiation was recorded over MWCNTs-APTES-TiO₂ (1:2), with 87% after 180 min. Kinetic analysis indicated that photocatalytic degradation of MO solution by MWCNTs-APTES-TiO₂ obeyed second-order kinetic model (R² > 0.95), supported by half-life equations and graph. The presence of carbon nanotubes accelerated the degradation of methyl orange due to inhibition of electron-hole recombination, the formation of additional hydroxyl radicals and functional groups of the latter had an inhibitory effect on the degradation of methyl orange. This study shows that the MWCNTs-APTES-TiO₂ nanocomposites exhibit promising photocatalytic degradation against methyl orange.

Keywords: Forest ecosystem; Biodiversity; Ecosystem multifunctionality

Background. Chronic kidney disease (CKD) is a progressive condition with limited treatment options that often lead to significant morbidity. Recent studies suggest that oxyhydrogen nanobubbles (HHOnb) may offer therapeutic benefits due to their antioxidative and anti-inflammatory properties. This case series examines the effects of intravenous HHOnb therapy on three patients with varying stages of CKD.Methods. Three patients with CKD underwent a series of ten HHOnb infusions over a period of two to three months. The patients, aged 43 to 70, presented with additional comorbidities including hypertension, diabetes mellitus, and polycystic kidney disease. Clinical parameters, including glomerular filtration rate (GFR), serum creatinine, blood pressure, and uric acid levels, were monitored alongside subjective health improvements.Results. Patient A. A 70-year-old woman with CKD grade 3a experienced a reduction in serum creatinine from 0.98 mg/dL to 0.82 mg/dL and an improvement in GFR from 59 mL/min/1.73 m² to 73.2 mL/min/1.73 m². She reported reduced leg swelling and increased vitality. Patient B. A 52-year-old man with CKD grade 4 and class III obesity showed a decrease in serum creatinine from 3.0 mg/dL to 2.86 mg/dL, an increase in GFR from 23 mL/min/1.73 m² to 24.4 mL/min/1.73 m², and improved sleep quality. Blood pressure reduced from 167/82 mmHg to 145/85 mmHg. Patient C. A 43-year-old man with CKD grade 4 and massive polycystic kidney disease reported reduced fatigue and normalized urination. His serum creatinine decreased from 3.26 mg/dL to 3.09 mg/dL, and his GFR improved from 22 mL/min/1.73 m² to 23.6 mL/min/1.73 m².Conclusion. Intravenous HHOnb therapy demonstrated potential benefits in improving renal function and overall health in CKD patients. These findings suggest that HHOnb could be a promising complementary treatment for CKD, though larger, controlled studies are needed to confirm its efficacy and safety.

Every month teachers face the dilemma of what exercises should their students practice, and what their consequences are on long-term learning. Since teachers prefer to pose their own exercises, this generates a large number of questions, each one attempted by a small number of students. Thus, we couldn’t use models based on big data such as deep learning. Instead, we developed a simple to understand state-space model that predicts end-of-year national test scores. We used 2,386 online fourth-grade mathematic questions designed by teachers and each attempted by some of the 500 students in 24 low socioeconomic schools. We found that the state-space model predictions improved month-by-month and that in most months it outperformed linear regression models. Moreover, the state-space estimator provides for each month a direct mechanism to simulate different practice strategies and compute their impact on the end-of-year standardized national test. We built iso-impact curves based on two critical variables: the number of questions solved correctly in the first attempt and the total number of exercises attempted. This allows the teacher to visualize the trade-off between asking students to do exercises more carefully or doing more exercises. To the best of our knowledge, this model is the first of its kind in education. It is a novel tool that supports teachers drive whole classes to achieve long-term learning targets.

Acellular bacterial ghosts (BGs) are empty non-living bacterial cell envelopes, commonly generated by controlled expression of the cloned lysis gene E of bacteriophage PhiX174. In this study, Vibrio parahaemolyticus ghosts (VPGs) were generated by chemically induced lysis and the method is based on minimum inhibitory concentration (MIC) of sodium hydroxide (NaOH), acetic acid, boric acid, citric acid, maleic acid, hydrochloric acid and sulfuric acid. The MIC values of the respective chemicals were 3.125, 6.25, < 50.0, 25.0, 6.25, 1.56 and 0.781 mg/ml. Except boric acid, the lysis efficiency was reached more than 99.99% at 5 min after treatment of all chemicals. Among those chemicals, NaOH-induced VPGs showed completely DNA-free that was confirmed by quantitative real-time PCR. Besides, lipopolysaccharides (LPS) extracted from the NaOH-induced VPGs showed no distinctive band on SDS-PAGE gel after silver staining. On the other hand, LPS extracted from wild-type bacterial cells as well as the organic acids-induced VPGs showed triple major bands and LPS extracted from the inorganic acids-induced VPGs showed double bands. It suggests that some surface structures in LPS of the NaOH-induced VPGs may be lost, weakened or modified by the MIC of NaOH. Nevertheless, Limulus amoebocyte lysate assay revealed that there is no significant difference in endotoxic activity between the NaOH-induced VPGs and wild-type bacterial cells. Macrophages exposed to the NaOH-induced VPGs at 0.5 × 106 CFU/mL showed cell viability of 97.9%, however the MIC of NaOH did not reduce the cytotoxic effect of wild-type bacterial cells. Like Escherichia coli LPS, the NaOH-induced VPGs are an excellent activator of pro-inflammatory cytokines (IL-1β and iNOS), anti-inflammatory cytokine (IL-10) and dual activities (IL-6) in the stimulated macrophage cells. On the other hand, the induction of TNF-α mRNA was remarkable in the macrophages exposed with wild-type cells. Scanning electron microscopy showed the formation of trans-membrane lysis tunnel structures in the NaOH-induced VPGs. SDS-PAGE and agarose gel electrophoresis also confirmed that cytoplasmic proteins and genomic DNA released from the VPGs to culture medium through the lysis tunnel structures. Taken together, all these results indicated that the NaOH-induced VPGs show the potency of safe, economical and effective inactivated bacterial vaccine candidate.

In view of the problem that the output thrust of solenoid actuator is affected by various factors and is difficult to calculate in actual working conditions, this paper proposes a semi-analytical model constructed by magnetic field subdomain method with internal and external boundary conditions in cylindrical coordinate system for calculation, and the general solution equations of magnetic vector potential for each subdomain are derived and solved by MATLAB. Taking a push-pull electromagnet as an example, the finite element simulation and experimental comparative analysis are carried out. The correctness and applicable conditions of the subdomain method are illustrated by comparing the gradient plot of magnetic vector potential, inductance curve and electromagnetic force. By calculating the output thrust after considering the core gravity, spring force and electromagnetic force, it is shown that this method has the advantage of computational flexibility compared with the finite element method, and it is easier to write special algorithms according to various working conditions to calculate the important parameters in engineering applications.

Purpose: Post-pandemic management of COVID-19 infections and any emergent outbreaks is as an endemic disease remains a public health concern. Vaccine hesitancy may continue to hamper efforts to respond any new disease outbreaks and future epidemics. This qualitative study aimed to explore factors influencing covid-19 vaccine acceptance and hesitancy in Kenya too gain deeper insights on this issue. Methods: The study was implemented in Western Kenya using key informant interviews. Fourteen (14) key informants were purposively selected for the study. All interviews were transcribed and analyzed using thematic analysis. Interpretation of findings was done within the framework of the Health Belief Model. Key findings: Knowledge was a critical factor in combatting misinformation and fostering vaccine acceptance among participants in this study. Misinformation included rumors that the vaccine lowers immunity and was intended for population control. Cues to action included influence from political and opinion leaders and observing the loss of life among unvaccinated individuals. Perceived barriers to vaccine uptake included fear of vaccine safety, side effects, long waiting times at that time of our study, fear of contracting the COVID-19 at vaccination sites, family/spousal influence on vaccine uptake and fear of the unknown with the vaccine. Conclusion: Findings from this study provides insight into areas for targeted strategies for managing Covid-19 vaccinations and future pandemics. Within the framework of the Health Belief Model, this study identified salient barriers and facilitators of COVID-19 vaccine hesitancy that may be helpful to inform future pandemic response.

The present study analyzed the effect of temperature, pH, retention time, pretreatment and mixing ratio on the anerobic digestion process. The parameters during the anaerobic co digestion of cow manure and food waste were then optimized using the Taguchi experimental design method. ANOVA is carried out to find the significant parameters which influence the biogas production. Experimental tests were carried out at laboratory scale reactors kept at different temperatures (28 °C, 35 °C, and 50 °C). The specific methanogenic Performance (SMP) during anerobic digestion at higher temperatures was characterized with the analysis of acetate, propionate, butyrate, hydrogen, glucose, and formate. The improvement of biogas production with different pre-treatment i.e., ultrasonic, autoclave, and microwave techniques also analysed. The results showed that reactor that maintained at 35 °C showed the highest biogas production. While the reactor that maintained at lower temperature (28 °C) produced the lower volume of biogas. As the retention time increases, the amount of biogas produced increases. Methanogenic activities of microorganisms reduced at higher temperature conditions (65 °C). Biogas production increased by 28.1%, 20.23%, and 13.27 % when the of substrates treated with ultrasonic, autoclave, and microwave, respectively, compared to the untreated substrate. The optimized condition for the highest biogas production during anaerobic co digestion of food waste and cow manure is temperature of 35 °C, pH of 7 and mixing ratio (CM: FW=1.5:0.5). ANOVA showed that temperature is the most important input parameter affecting biogas production, followed by mixing ratio.

Through the integration of high-resolution hydrodynamic modeling and comprehensive field measurements, this study elucidates the intricate three-dimensional hydrographic characteristics of the eastern Gulf of Thailand (eGOT). In addition to the prevalent tidal currents dictating alternating flow along the northwestern and southeastern axes, our investigation reveals pronounced seasonal variations in mean currents, water temperature, and salinity within the eGOT, closely linked to the dynamics of the Asian-Australian monsoon system. During the southwest monsoon, mean currents exhibit a southeasterly trend, contrasting with a northwesterly pattern during the northeast monsoon. Lowest water temperatures occur during the latter, while the highest levels are observed during the 1st monsoon transition (April-March). Notably, salinity levels reach their lowest levels during the southwest monsoon and the 2nd monsoon transition (October), coinciding with the seasonal stratification of the water column and the emergence of a distinct stable along-the-shore northwesterly current with the average speed of 15 cm/s, defined here as the “Chathaburi Coastal Current (CCC)”. Model experiments attribute the formation of the CCC to decreased salinity induced by direct rainfall, highlighting the significance of rainfall as a key factor influencing coastal water dynamics in tropical regions or areas experiencing high precipitation.

Chipman contends, in stark contrast to the conventional view, that utility is not a real number but a vector and is inherently lexicographic in nature. According to these views, it will be proved that, for every preordered topological space (X,≾,t), the assumption t to be finer than the order topology t≾ on (X,t), i.e. t⊇t≾, and the assumption the quotient topology t∣∼≾ that is defined on the preordered set (X∣∼,≾∣∼) of indifference classes of ≾ to be Hausdorff imply that a cardinal number κ and a (complete) preorder ≲ on {0,1}κ that is coarser than the lexicographical ordering ≤lex on {0,1}κ, i.e. ≤lex⊂≲, can be chosen in such a way that there exists a continuous order-embedding ϑ:(X,≾,t)⟶({0,1}κ,≲,t≤lex). This theorem will be compared with a theorem that, in particular, describes necessary and sufficient conditions for ≾ to have a continuous multi-utility representation.

Background: This study aimed to assess the impact of a nutrition-care bundle on growth and neurodevelopmental outcomes of micro-preterm infants born in a level III neonatal intensive care unit (NICU) by two years corrected age. Methods: A nutrition-care bundle emphasizing the prompt initiation of parenteral nutrition at birth, initiation of enteral feeds within 6 hours after birth, and early addition of human milk fortifiers was implemented in 2015 for infants born &lt;26 weeks gestation. This before-and-after study evaluated growth and neurodevelopmental outcomes in infants born between 2012-2013 (before-nutrition-bundle BNB) and 2016-2017 (after-nutrition-bundle ANB). Results: A total of 145 infants were included in the study. Infants in the ANB group (n=73) were smaller (birthweight and gestational age), and there were more male infants and multiples included compared to the BNB group (n=72). Enteral feeds and fortifiers started earlier in the ANB group. Growth velocity and weight-z-score changes were similar in both groups during NICU stay and post-discharge. Systemic steroid use, but not cohort, was linked to lower Bayley scores across all domains. Conclusions: Implementing a nutrition-care bundle was not consistently associated with improved weight-gain and neurodevelopmental outcomes in the micro-preterm infant population, likely due to ongoing high-quality nutritional care by the clinical team.

Vascular cell adhesion molecule-1 (VCAM-1) is closely associated with tumor progression and metastasis. However, the relevance and role of VCAM-1 in lung cancer have not been clearly elucidated. In this study, we found that VCAM-1 was highly overexpressed in lung cancer tissue compared with that of normal lung, and high VCAM-1 expression correlated with poor survival of lung cancer patients. VCAM-1 knockdown reduced invasion in A549 human lung cancer cells, and competitive blocking experiments targeting the Ig-like domain 6 of VCAM-1 (VCAM-1-D6) demonstrated that the VCAM-1-D6 domain was critical for VCAM-1-mediated A549 cell invasion. Next, we developed a human monoclonal antibody specific to human and mouse VCAM-1-D6 (VCAM-1-D6 huMab), which was isolated from a human synthetic antibody library using phage display technology. Finally, we showed that VCAM-1-D6 huMab had a nanomolar affinity for VCAM-1-D6 and that it potently suppressed invasion in A549 and NCI-H1299 lung cancer cell lines. Taken together, these results suggest that VCAM-1-D6 is a novel therapeutic target in VCAM-1-mediated lung cancer invasion and that our newly developed VCAM-1-D6 huMab will be a useful tool for inhibiting VCAM-1-expressing lung cancer cell invasion.

The pointing accuracy of large radio telescopes is affected by antenna structure errors, installation errors, servo errors, gravity, and varying environmental factors. It is hard to estimate the pointing error of radio telescopes under solar radiation via the traditional multi-parameter static pointing models, due to the time-varying characteristics of the dynamic pointing error. There is still no effective way to estimate the dynamic pointing error of large radio telescopes under solar radiation up to now. To estimate the dynamic pointing error of large radio telescopes under solar radiation, a new way based on digital twin(DT) is tried in this paper. The operational framework of the DT system of large radio telescopes is introduced first. Then, the Wuqing 70-m radio telescope(WRT70) is taken as the research object, the digital models of which are created. Finally, the reliability of the WRT70's DT system is verified and the dynamic pointing error of WRT70(staying at the position of the elevation angle of 90° and the azimuth angle of 180°) on two sunny days in different seasons is estimated by the created DT system. The research results show that the pointing error of large radio telescopes under solar radiation fluctuates greatly and the seasonal differences are very striking. The maximum elevation pointing error of WRT70 under solar radiation around the winter solstice is over 40 arcsec, which should never be ignored. The way is feasible to estimate the dynamic pointing error of large radio telescopes staying at a specific position under solar radiation via constructing a DT system in this paper, but it is not yet able to estimate effectively the dynamic pointing error of large radio telescopes being in action. It is expected to establish a dynamic pointing error model and calibrate the dynamic pointing errors of large radio telescopes being in action by DT technology in the future.

Background: Colorectal cancer (CRC) patients experienced multiple chemotoxicity affecting treatment compliance, survival, and quality of life (QOL). Prior research shows clinician-reported chemotoxicity (i.e., grading scales or diagnostic codes) predicts rehospitalization and cancer survival. However, a comprehensive synthesis of clinician-reported chemotoxicity is still lacking. Objectives: We conducted a systematic review and meta-analysis to determine the chemotoxicity's prevalence and risk factors in CRC. Methods: A systematic search from 2009 to 2024 yielded 30 studies for review, with 25 included in the meta-analysis. Results: Pooled prevalences of overall, non-hematological, and hematological moderate-to-severe toxicities were 45.7%, 39.2%, and 25.3%, respectively. The most common clinician-reported chemotoxicities were gastrointestinal (GI) toxicity (22.9%) and neuropathy or neutropenia (17.9%). Significant risk factors at baseline were malnutritional status, frailty, impaired immune or hepato-renal functions, short telomere lengths, low gut lactobacillus levels, age, female sex, aggressive chemotherapy, and low QOL. Age was associated with neutropenia (β: -1.44) and GI toxicity (β:1.85) (Ps 65 y.o.) had higher prevalences of overall (OR: 1.14) and GI (OR: 1.65) toxicities, but a lower prevalence of neutropenia (OR: 0.65) than younger adults (Ps

Software-Defined Networking (SDN) has revolutionized network management by providing unprecedented flexibility, control, and efficiency. However, its centralized architecture introduces critical security vulnerabilities. This paper presents an innovative approach to securing SDN environments using IOTA 2.0 smart contracts. The proposed system leverages the IOTA Tangle, a directed acyclic graph (DAG) structure, to enhance scalability and efficiency while eliminating transaction fees and reducing energy consumption. We introduce three smart contracts—Authority, Access Control, and DoS Detector—to ensure secure network operations, prevent unauthorized access, and mitigate denial-of-service attacks. Through comprehensive simulations using Mininet and the ShimmerEVM IOTA Test Network, we demonstrate the efficacy of our approach in enhancing SDN security. Our findings highlight the potential of IOTA 2.0 smart contracts to provide a robust, decentralized solution for securing SDN environments, paving the way for further integration of blockchain technologies in network management.

This study endeavors to elucidate the intricate interplay between gastronomic involvement, gastronomic knowledge, past gastronomic experiences, gastronomic attractiveness, destination attractiveness, tourist satisfaction, and the intention to revisit. In the context of this investigation, the research population comprises 300 international culinary tourists who have ventured to the Turkish Republic of Northern Cyprus (TRNC). Employing SPSS software, a confirmatory factor analysis (CFA) was conducted to rigorously assess construct validity, reliability, and the discriminant and convergent validity of the research model. The findings of this research study underscore the positive relationships that exist between various dimensions of gastronomy, namely involvement in gastronomy, knowledge of gastronomy, and previous gastronomic experiences, and the perceived attractiveness of gastronomy. These findings enhance our understanding of how culinary elements affect tourists' perceptions and experiences, highlighting the crucial role of gastronomy in destination appeal and visitor satisfaction. This research is a significant step towards enhancing our comprehension of the dynamic nexus between gastronomy and the tourism industry, ultimately benefiting destination management and strategy development.

The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data–driven grey–box identification approach provided the physically meaningful nonlinear continuous–time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte–Carlo analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model–based and data–driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.

Background: Studies have tried to identify predictors of functional markers of keratoconus progression, whereas structural outcomes of collagen cross-linking have not been properly investigated. Objective: find and set up a combination of diagnostic modalities and measurements to reliably reflect CXL efficiency. Material and methods: We retrospectively reviewed the medical histories of 107 KC patients (131 eyes) who underwent CXL in a medical center from January 2018 to December 2022. The dataset included preoperative examinations and follow-up results, which provided a total of 796 observations. The cases of other corneal diseases, repeated CXL, pregnancy, and observations without follow-up examinations were excluded. Results: The study findings revealed a significant drop in the central and corneal thickness during 14.01±9.98 months of observations. Linear and polynomial equations suggested different trends in pachymetry change after CXL. The linear model showed corneal thinning, and according to the polynomials, pachymetry findings return to baseline values two years after CXL. Conclusion: The most reliable prognosis of postoperative corneal thickness is achieved with the models trained on keratometry readings and topography indices. BAD indices are also reliable predictors of the corneal thickness after CXL. A combination of the aforementioned structural parameters and their derivatives can reliably predict CXL efficiency.

The existing collaborative channel perception suffered from unreasonable data fusion weight allocation, which mismatched the channel perception capability of node devices. This often led to significant deviations between the channel perception results and the actual channel state. To solve this issue, this paper integrated the data fusion algorithm from evidence fusion theory with data link channel state perception. It applied the data fusion advantages of evidence fusion theory to evaluate the traffic pulse statistical capability of network node devices. Specifically, the typical characteristic parameters describing the channel perception capability of node devices were regarded as evidence parameter sets under the recognition framework. By calculating the credibility and falsity of the characteristic parameters, the differences and conflicts between nodes were measured to achieve a comprehensive evaluation of the traffic pulse statistical capabilities of node devices. Based on this evaluation, the geometric mean method was adopted to calculate channel state perception weights for each node within a single-hop range, and a weight allocation strategy was formulated to improve the accuracy of channel state perception.

Social media data is widely used to gain insights about social incidents, whether on a local or global scale. Within the process of analyzing and evaluating the data, it is common practice to download and store it locally. Considerations about privacy protection of social media users are often neglected thereby. However, protecting privacy when dealing with personal data is demanded by laws and ethics. In this paper we introduce a method to store social media data using the cardinality estimator HyperLogLog. Based on an exemplary disaster management scenario, we show that social media data can be analyzed by counting occurrences of posts, without becoming in possession of the actual raw data. For social media data analyses like these, that are based on counting occurrences, cardinality estimation suffices the task. Thus, the risk of abuse, loss or public exposure of the data can be mitigated and privacy of social media users can be preserved. The ability to do unions and intersections on multiple data sets further encourages the use of this technology. We provide a proof-of-concept implementation for our introduced method, using data provided by the Twitter API.

The development and generalization of Digital Volume Correlation (DVC) on X-ray computed tomography data highlight the issue of long term storage. The present paper proposes a new model-free method for pruning the DVC data. The size of the remaining sampled data can be user-defined, depending on the needs concerning storage space. The data pruning procedure is deeply linked to hyper-reduction techniques. The DVC data of a resin-bonded sand tested in uniaxial compression is used as an illustrating example. The relevance of the pruned data is tested afterwards for model calibration. A new Finite Element Model Updating (FEMU) technique coupled with an hybrid hyper-reduction method is used to successfully calibrate a constitutive model of the resin bonded sand with the pruned data only.

In this research paper, we address the critical role of data security in the rapidly evolving FinTech sector. We explore the challenges and strategies for establishing robust data security systems, emphasizing the consequences of data breaches and common vulnerabilities. The paper highlights the efficacy of technologies like encryption, blockchain, and cloud security, supported by real-world case studies from leading FinTech companies. Additionally, we examine regulatory frameworks and standards essential for maintaining data security. Concluding with the proposal of an integrated security system model, the paper underscores the synergy between technological innovation, regulatory compliance, and proactive risk management in the FinTech industry

With the emergence of blockchain and smart contracts, traditional digital applications 1 such as identity management, supply chain management, banking and finance etc. are expected 2 to be transformed into Decentralized Applications (DApps). Blockchain interoperability is a new 3 and exciting aspect of blockchain technology that is quickly gaining popularity in many industries. 4 However, the widespread use of blockchains has not yet been achieved because DApps running on the 5 blockchain using smart contracts require access to authentic off-chain data. Users are more conscious 6 of their personal data privacy and consequently are more reluctant to share their data, posing a 7 challenge in the adoption of DApps. To solve this challenge, we present a novel quantum-resistant, 8 data authentication and zero-knowledge proof scheme named zk-DASTARK. Based on our proposed 9 scheme, we present a novel framework which can be used to feed authenticated off-chain data to 10 DApps without compromising user data privacy. Our proposed framework is quite efficient against 11 well-known off-chain data authentication frameworks. The proposed framework is implemented on 12 state-of-the-art quantum-resistant IOTA Blockchain and is highly efficient as it takes approximately 13 60 ms to generate a proof and approximately 10 ms to verify a proof.

Ajuga arabica is an essential medicinal plant found naturally in Saudi Arabia, recognized for its therapeutic characteristics. Nevertheless, research on the genetic diversity and population arrangement of this species in Saudi Arabia is deficient. In this study, we conducted a comparative examination of DNA and gene sequences to evaluate the genetic diversity and population configuration of Ajuga arabica in Saudi Arabia. Our findings demonstrate that there are high levels of genetic diversity within populations, but low levels of genetic differentiation among populations. Moreover, we discovered proof of gene flow among populations, suggesting that there are no significant genetic barriers to gene flow. Our results offer valuable insights into the genetic diversity and population structure of Ajuga arabica in Saudi Arabia, which can support conservation efforts and promote the sustainable use of this valuable medicinal plant.

By installing FBG sensors on the geogrids, smart geogrids can both reinforce and monitor the stability for geogrid-reinforced slopes. In this paper, a geogrid-reinforced sand slope model test was conducted in the laboratory and fiber Bragg grating (FBG) sensing technology is used to measure the strain distribution of the geogrid. Based on the model test, the performance of the reinforced soil slope is simulated by finite element software Midas-GTS, and the stability of the reinforced soil slope is analyzed by strength reduction method. The relationship between the geogrid strain and safety factor is set up. The results indicate that the measured strain and calculated results agree very well. The geogrid strain measured by FBG sensor can be applied to evaluate the stability of geogrid-reinforced sand slopes.

Most of the mid-high permeability sandstone oilfields in China have entered the ultra-high water cut stage, accompanied with the highly-dispersed residual oil, and the control effect of mi-cro-heterogeneity on the residual oil is increasingly prominent. Understanding the microscopic characteristics of pore structural and its evolution during the long-term waterflooding is critical for enhancing the accuracy and efficiency of oil field exploitation. While the previous insights paid more attention to the geometrical and morphological features of pore structure and ignores the relation of pore structure with the fluid flow capacity. In this study, we propose a microscopic pore structure classification method considering pore size and local permeable capacity, which realizes the physical classification and quantitative evaluation of pore structure characteristics for different permeability rocks. The micro-pore structure is classified into 6 types based on the key evaluation parameters of pore diameter and flow flux area. By taking six mid-high permea-bility sandstone core samples from Daqing oilfield in China, a series of long-term waterflooding experiments are carried out and the interior pore structure was captured using high precision CT scanning technique. The results reveals that high-permeability reservoirs exhibit concentrated pores diameters and finer throats, whereas mid-permeability reservoirs exhibit smaller pores and throats in greater quantity. With the increase of water injection volumes, the permeable ca-pacity and connectivity of the high-connectivity pore space in ultra-high- and high-permeability reservoirs are enhanced, and in mid-permeability reservoirs, the micro-heterogeneity intensifies the increase in low-connectivity small pores. These findings provide new insights for the micro-scopic heterogeneity and a solid theoretical basis for investigating the occurrence of microscopic residual oil and optimising enhanced oil recovery strategies at the late stage of ultra-high water cut.

The increased prevalence of obesity worldwide has been implicated for the alarming rise of the incidence of gestational diabetes and preeclampsia as well, both considered as threatening conditions for both mother and fetus. We studied gene polymorphisms of the proinflammatory cytokine Interleukin 6 (IL-6) and the gene expression levels of VEGF and VEGF-receptor all known to be involved in pregnancy complications aiming to identify possible predisposing risk factors in pregnancies with obesity. G allele of IL-6 was found to correspond with an increased risk for gestational diabetes and preeclampsia occurrence. Furthermore, in obese pregnant mothers with either gestational diabetes or pre-existing type 2 diabetes and to those developed preeclampsia it was confirmed that gene expression levels of VEGF were reduced while they were increased for VEGF receptor. We conclude that the genetic profile of the obese pregnant woman shares a common background with that of a pre-existing type 2 diabetes mellitus and therefore predisposes to complications in pregnancy.

Achieving the best aesthetic result is a key objective of implant-prosthetic therapy. To maximize the aesthetic outcome, ensuring that implant-supported restorations are in harmony with the peri-implant soft tissues and bone is essential. A comparative analysis of zirconia and titanium dental implants revealed that both materials offer high success rates; however, they present distinct advantages and limitations in clinical practice. Titanium implants, with surface modifications but without chemical composition changes, boast remarkable long-term survival. In contrast, zirconia implants, while presenting high aesthetic and biocompatibility qualities, have a relatively high incidence of material-specific failure, ranging from 4% to 2% after 5-10 years. While titanium implants generally show superior long-term performance, zirconia implants benefit from ongoing advancements aimed at improving their osseointegration and longevity. This study emphasizes the need for short-term and long-term studies, complemented by randomized controlled trials (RCTs), which are essential for optimizing implant materials and ensuring patient satisfaction. Continued innovation and research are crucial for advancing dental implant technology and enhancing the quality of life for patients requiring dental restorations. Finally, it is important to understand that all materials have both advantages and disadvantages, so the choice of the optimal material should be based on the specific purpose.

Garcinia indica (Thouars) Choisy (commonly known as Kokum) is an important medicinal plant in Ayurvedic medicine and belonging to the Clusiaceae family. Kokum is a highly traded medicinal plant species of India, dried rind of fruits and butter from seeds are traded in the markets. This study aimed to assemble the complete chloroplast (cp) genome of G. indica and compare it with the previously published cp genome within the Garcinia taxa to identify the potential species-specific barcode marker for Kokum authentication. The assembled cp genome, with a size of 1,56,891 bp, exhibits a typical quadripartite structure. The large single-copy (LSC) region spans 85,580 bp, and the small single-copy (SSC) region spans 17,181 bp, together comprising 64.5% of the genome. The pair of inverted repeats (IRA and IRB) are each 27,065 bp, covering the remaining 35.5% of the genome. A total of 126 unique genes were identified, including 86 protein-coding genes, 32 tRNA genes, and 8 rRNA genes. Phylogenetic analysis using complete cp genomes from 12 species in the Clusiaceae family indicated that 10 Garcinia species form a sister clade. Notably, the ycf1 gene in the LSC region was more divergent within the Garcinia species. This study presents the first report on the chloroplast genome of G. indica. This chloroplast genome resource provides a basis for identifying new DNA barcode marker as well as species-specific marker for herbal drug authentication of Kokum, and species delineation within the Garcinia genus.

Additionally, the characteristics of inhaled corticosteroids (inhaled particle sizes) may influence the risk of sleep apnea. In children, the severity of asthma plays a crucial role in influencing the prevalence of sleep apnea, while using inhaled corticosteroids seems to be a less relevant risk factor compared to adults. The overall risk of bias was categorized as high or with some concerns in 100% of the studies analyzed. Each study identified at least one form of bias that raised significant concerns. Studies showed a complex relationship between inhaled corticosteroids use, asthma severity, and the onset of sleep apnea. Further studies are needed.

An analysis of the processes of plastic deformation and acoustic relaxation in a high-entropy alloy Al0.5CoCrCuFeNi was carried out. The following have been established: dominant dislocation defects; types of barriers that prevent the movement of dislocations; mechanisms of thermally activated movement of various elements of dislocation lines through barriers at room and low temperatures. Based on modern dislocation theory, quantitative estimates have been obtained for the most important characteristics of dislocations and their interaction with barriers.

The main purpose of this work was to tests of form and size of fatigue wear of the top layer of oil gear pump wheel (3PW-BPF-24). The object of tests was operated during a determined number of hours at strictly controlled operational and environmental parameters. A microscope of focal differentiation by Alicona InfiniteFocus G4 (with Focus-Variation optical measuring technology) was used for an identification, measurement and description of noticed superficial defects, which resulted from the fatigue process of the top layer and an earlier thermochemical treatment. The work was finished with an analysis of obtained results (based on the analysis of 3D scans) and conclusions concerning about optimization of the thermal treatment. Taking into consideration the presented results (suggestions) in the technology of improving strength properties of gear wheels (of this type oil pumps) may render it possible to obtain an appropriate fatigue endurance of the top layer, i.e. a minimization of observed superficial defects.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

Motivated by the PEACOC project on metal recovery from solid wastes, an innovative magnetic density separation (MDS) process has been developed for granular material sorting. It has intrinsic advantages over the existing industrialized MDS by eliminating fluid turbulence and particle jamming problems. The new MDS applies an inclined planar magnet and a horizontal basin containing a static magnetic fluid as the separation medium. A particle sliding phenomenon is identified as a feature of the process which could help the separation. The MDS successfully sorted shredded PCBAs to concentrate valuable metals and sorted shredded wires to reduce metallic contaminants in the plastic fractions. A pilot scale facility is introduced to show the design to achieve continuous production and to reduce the consumption of ferrofluid.

The interest in the Spark Plasma Sintering (SPS) technique has continuously increased over the last few years. This article shows the possibility of the development of a SPS device used for material processing and synthesis both in both scientific and industrial applications. This work presents an example of processing Arc-Meleted (half-Heusler, cobalt triantimonide) and SHS-synthesized semiconductors (bismuth telluride) materials with SPS device. The system functionalities expansion is presented, showing the possible way of increasing the information amount obtained about SPS processes and using the SPS apparatus for conducting synthesis of materials and increasing reproducibility and accuracy of process parameters control.

Introduction: The Pandemic of COVID-19, an infectious disease caused by SARS-CoV-2 motivated the scientific community to work together in order to gather, organize, process and distribute data on the novel biomedical hazard. Here, we analyzed how the scientific community responded to this challenge by quantifying distribution and availability patterns of the academic information related to COVID-19. The aim of our study was to assess the quality of the information flow and scientific collaboration, two factors we believe to be critical for finding new solutions for the ongoing pandemic. Materials and methods: The RISmed R package, and a custom Python script were used to fetch metadata on articles indexed in PubMed and published on Rxiv preprint server. Scopus was manually searched and the metadata was exported in BibTex file. Publication rate and publication status, affiliation and author count per article, and submission-to-publication time were analysed in R. Biblioshiny application was used to create a world collaboration map. Results: Our preliminary data suggest that COVID-19 pandemic resulted in generation of a large amount of scientific data, and demonstrates potential problems regarding the information velocity, availability, and scientific collaboration in the early stages of the pandemic. More specifically, our results indicate precarious overload of the standard publication systems, significant problems with data availability and apparent deficient collaboration. Conclusion: In conclusion, we believe the scientific community could have used the data more efficiently in order to create proper foundations for finding new solutions for the COVID-19 pandemic. Moreover, we believe we can learn from this on the go and adopt open science principles and a more mindful approach to COVID-19-related data to accelerate the discovery of more efficient solutions. We take this opportunity to invite our colleagues to contribute to this global scientific collaboration by publishing their findings with maximal transparency.

An ambitious citizen-science effort in the city of Indianapolis (Indiana, USA) led to the collection and analysis of a large number of samples at the property scale, facilitating the analysis of differences in soil metal concentrations as a function of property location (i.e., dripline, yard, and street) and location within the city. This effort indicated that dripline soils had substantially higher values of lead and zinc than other soil locations on a given property, and this pattern was heightened in properties nearer the urban core. Soil lead values typically exceeded the levels deemed safe for children’s play areas in the US (<400 ppm), and almost always exceeded safe gardening guidelines (<200 ppm). As a whole, this study identified locations within properties, and cities, that exhibited the highest exposure risk to children, and also exhibited the power of citizen science to produce data at a spatial scale (i.e., within a property boundary) that is usually impossible to feasibly collect in a typical research study.

Additive manufacturing has already been established as a highly versatile manufacturing technique with demonstrated potential to completely transform conventional manufacturing in the future. The objective of this paper is to review the latest progress and challenges associated with the fabrication of multi-material parts using additive manufacturing technologies. Various manufacturing processes and materials used to produce functional components were investigated and summarized. The latest applications of multi-material additive manufacturing (MMAM) in automotive, aerospace, biomedical and dentistry field were demonstrated. Investigation on the current challenges were also carried out to predict the future direction of MMAM processes. It is concluded that the further research and development needed in the design of multi-material interfaces, manufacturing processes and material compatibility of MMAM parts are necessary.

The world faces two seemingly unrelated challenges—a shortfall in the STEM workforce and increasing antibiotic resistance among bacterial pathogens. We address these two challenges with Tiny Earth, an undergraduate research course that excites students about science and creates a pipeline for antibiotic discovery.

Many research work has demonstrated that taken the Combined Cooling Heating and Power system (CCHP) as the core equipment, the integrated energy system (IES) can bring obvious benefit to energy efficiency, CO2 emission reduction and operation economy in urban areas. Compared with isolated IES, integrated energy micro-grid (IEMG) which is formed by connecting multiple regions IES together, through distribution and thermal network, can further improve the reliability, flexibility, cleanliness and economy of regional energy supply. Based on the existing IES model, this paper describes the basic structure of IEMG and built a IEMG planning model. The planning based on the mixed integer linear programming, and economically construction planning scheme are calculated by using known electricity, heating and cooling loads information and the given multiple equipment selection schemes. At last, the model is validated by a case study. The results show that the application of IEMG can effectively improve the economy of regional energy supply.

Klebsiella pneumoniae (KP) is an important gram-negative zoonotic conditional pathogen. It can infect humans and a variety of farm animals, leading to infections such as pneumonia, meningitis and even sepsis. thereby posing a significant threat to public health. OmpX acts as a virulence protein that can participate in bacterial pathogenesis, mediates bacterial adhesion to and invasion of epithelial cells, and is involved in bacterial adaptation to the osmotic pressure of the external environment. In this study, MBs-SELEX was used as the main strategy for screening aptamers. Omp X was used as the target protein and Omp A as the counter-screening target. qPCR and flow cytometry were used to determine the nature and specificity of the obtained aptamers for the Omp X protein. The analysis showed that among the obtained aptamers, three aptamers, Apt-1, Apt-2 and Apt-3, were selected with Kd values of 62.50 nM, 22.05 nM and 7.9 nM, respectively. In addition, total internal reflection fluorescence microscopy (TIRFM) was used for the characterization of KP in complex with specific FITC-labeled aptamers (FITC-Apt-3) and AuNCs. The complexes were characterized by observing blue fluorescent dots from AuNCs and green fluorescent dots from FITC-labeled aptamers overlapping with bacterial presence. These results suggest that AuNCs and aptamers can specifically bind to target bacteria, forming complexes. Furthermore, in vitro results demonstrated that Apt-1, Apt-2 and Apt-3 inhibited bacterial growth, with Apt-3 having the most significant inhibitory effect. The results suggest that aptamers can selectively recognize membrane proteins, thereby disrupting bacterial cell membrane formation and effectively inhibiting bacterial growth. Aptamers have a potential mechanism to inhibit bacterial growth.

Soil water balance (SWB) in woody crops is sometimes difficult to estimate with one-dimensional models because these crops do not completely cover the soil and usually have a deep root system, particularly when cropped under rainfed conditions in a Mediterranean climate. In this study, the actual crop evapotranspiration (ETc act) is estimated with the soil water balance model SIMDualKc which uses the dual-Kc approach (relating the fraction of soil cover with the crop coefficients) to improve the estimation of the water requirements of a rainfed vineyard, using data from a deep soil profile. The actual basal crop coefficient (Kcb act) obtained using the SIMDualKc model was compared with the Kcb act estimated using the A&amp;P approach, which is a simplified approach based on measurements of the fraction of ground cover and crop height. Spectral vegetation indices derived from Landsat-5 satellite data were used to determine the fraction of ground cover (fc VI) and thus, the density coefficient (Kd). The SIMDualKc model was calibrated using soil water content (SWC) measurements down to a depth of 1.85 meters, which significantly improved the conditions for using a SWB estimation model. The test of the model was performed using a different SWC dataset. A good agreement between simulated and field measured SWC was observed for both data sets along the crop season, with RMSE&lt;12.0 mm, NRMSE &lt;13%. The calibrated Kcb values were 0.15, 0.60 and 0.52 for the initial, mid-season, and end season, respectively. The ratio between actual (ETc act) and crop evapotranspiration (ETc) was quite low between veraison and maturity (mid-season), corresponding to 36%, indicating that the rainfall was not sufficient to satisfy the vineyards water requirements. Spectral vegetation indices used to compute fc VI were unable to fully track the plants’ conditions when water stress. However, ingestion of data from remote sensing showed promising results that could be used to support decisions making in irrigation scheduling. Further studies on the use of the A&amp;P approach using remote sensing data are required.

A novel series of thiazole based-1,3,4-thiadiazoles were designed and prepared via the reaction of the 2-(4-methyl-2-phenylthiazole-5-carbonyl)-N-phenylhydrazinecarbothioamide with the appropriate hydrazonoyl chlorides. The structures of the newly synthesized compounds were established based on spectroscopic evidences and their alternative syntheses. Thirteen new 1,3,4-thiadiazoles have been evaluated for their anticancer activity against liver carcinoma cell line (HepG2). Also, their structure activity relationship (SAR) was studied. The 1,3,4-thiadiazoles 12d, 12c, 6g,18b, 6c, and 6f(IC₅₀ = 0.82, 0.91, 1.06, 1.25, 1.29 and 1.88 µM, respectively) have promising antitumor activity against liver carcinoma cell line (HepG2).

The present study aimed to date the death of Korean red pine (Pinus densiflora Siebold &amp; Zucc.) at Sogwang-ri, where is famous for the Korean red pine forests in Korea, using a dendrochronological method, and to investigate the effect of climatic factors on the death and growth decline of the pine trees. For the study, 51 Korean red pines were selected from three stands (Jungmi-gol A, Jungmi-gol B, and Neonam-gol) in the study area, and for the ring-width measurement the increment cores were extracted from the selected trees. The stand chronologies for the Jungmi-gol and Neonam-gol were established using the mean values of individual time series of the living trees from each site, and the chronologies were further used for cross-dating with individual time series of the dead trees to date their death. The abrupt growth reduction was also investigated based on the consecutive ring-width growth decline trend to know the vitality during the living. Results revealed that the red pines at Jungmi-gol A were dead in 2020, at Jungmi-gol B in 2012 or 2013, and at Neonam-gol in 2019. So, it was verified that the death of the red pines at the study sites occurred from high to low elevations. In the analysis of the role of climate in the annual ring growth, the trees at Jungmi-gol A and B located on the southwest slope showed more ring-width growth when precipitation in April, July, and August were increasing, at Neoman-gol located on the north slope when precipitation only in July was increasing. Although the correlations of the ring-width growths at the sites with temperature and precipitation were somehow different from each other, Drought Stress Index (DSI) from May to September at all sites were all negative. Therefore, water stress caused by increased temperature and decreased precipitation is the most significant climatic parameter for the death or growth decline of the pine trees in the study area.

Gestational diabetes mellitus (GDM) triggers alterations in the maternal gut microbiome. Alongside metabolic shifts, microbial products may impact clinical factors and influence pregnancy outcomes. Here, we performed two separate analyses to integrate the maternal microbiome-metabolomic differences from a subset of the “Choosing Healthy Options in Carbohydrate Energy” (CHOICE) study. Women diagnosed with GDM were randomized to a diet higher in complex carbohydrates (CHOICE, n=18, 60% complex carbohydrate/25% fat/15% protein) or a conventional GDM diet (CONV, n=16, 40% carbohydrate/45% fat/15% protein). All meals were provided, diets were eucaloric, and fiber content was similar. CHOICE diet was associated with increases in TMAO, lysophosphotidylcholines, indoxyl sulfate, and several triglyceride (TG) species, while CONV diet was associated with higher plasma hippuric acid, aspartic acid, betaine, glutamic acid, indole propionic acid, and indoleacetic acid, suggesting that differences in lipid metabolism and tryptophan utilization pathways are linked to specific maternal diets. We also identified two latent metabolic groups not associated with either diet treatment: one metabolome associated with higher fasting glucose, and the other associated with higher fasting TGs after 7 weeks on either diet. Our study underscores the importance of utilizing dietary approaches to optimize maternal-fetal health outcomes in GDM treatment.

Insulation systems for the floor, roof and external walls play a prominent role in providing a thermal barrier for the building envelope. Design decisions made for the insulation material type and thickness can alleviate potential impacts on the embodied energy and improve the building thermal performance. This design problem is often addressed using a BIM-integrated optimisation approach. However, one major weakness lies in the current studies is that BIM is merely used as the source for design parameters input. This study proposes a BIM-based envelope insulation optimisation design framework using a common software Revit to find the trade-off between the total embodied energy of the insulation system and the thermal performance of the envelope by considering the material type and thickness. In addition, the framework also permits data visualisation in a BIM environment, and subsequent material library mapping together with instantiating the optimal insulation designs. The framework is tested on a case study based in Sydney, Australia. By analysing sample designs from the Pareto front, it is found that slight improvement in the thermal performance (1.3399 to 1.2112 GJ/m2) would cause the embodied energy to increase by more than 50 times.

This paper presents a novel integration of Systems Engineering (SE) methodologies and Industry 4.0 (I4.0) technologies in the design of robotic systems, with a focus on enhancing underwater robotic missions. Using the conceptual design of an underwater exploration vehicle as a case study, we demonstrate how SE can systematically incorporate I4.0 tools to improve mission performance and meet stakeholder expectations. The study begins with an overview of the SE approach, emphasizing the conceptual design stage, aligning with the application and case study of design theories. We then explore various I4.0 technologies, highlighting their functional benefits rather than technical specifics, addressing design methods for Industry 4.0. Remotely Operated Vehicles (ROVs) are examined in terms of classification, components, and tasks, showcasing their evolution driven by technological advancements, thus tackling the complexity and design of complex systems. The core of our study involves defining stakeholder expectations, using quality function deployment for requirements definition, and performing a functional and logical decomposition of the ROV system. Our approach underscores the importance of understanding and integrating new technologies functionally, aligning with the transition towards Industry/Society 5.0. This work not only illustrates the synergy between SE and I4.0 but also offers a structured methodology for advancing the design and functionality of complex systems, setting a blueprint for future developments in this field.

Chagas disease (CD) is a neglected tropical parasitic disease caused by the protozoan Trypanosoma cruzi. Unfortunately, the current etiological treatment for CD is unsatisfactory, due to the high toxicity and low cure efficacy of compounds, mainly during the chronic phase of this disease. Thus, the discovery of new drugs for the treatment of CD is urgent. In the present study, we report the synthesis of a series of guanidines and its evaluation in in vitro assays to determine the trypanocidal effects on the Tulahuen T. cruzi strain, transfected with a β-galactosidase reporter gene, in comparison to the cytotoxic effects on vertebrate cells. The most potent and selective compounds LQOF-G1 and LQOF-G29 were considered good drug prototypes and candidates for in vivo tests using mice. Both compounds possess an electron withdrawing group on the aniline moiety, namely a NO2 and Br group, respectively, which effectively decrease the electron density. LQOF-G29 stands out among the investigated compounds due to the replacement of the benzyl group by methyladamantane.

Axial resolution is one of the most important characteristics of a microscope. In all microscopes, a high axial resolution is desired in order to discriminate information efficiently along the longitudinal direction. However, when studying thick samples that do not contain laterally overlapping information, a low axial resolution is desirable as information from multiple planes can be recorded simultaneously from a single camera shot instead of plane-by-plane mechanical refocusing. In this study, we increased the focal depth of an infrared microscope non-invasively by introducing a binary axicon fabricated on barium fluoride substrate close to the sample. Preliminary results of imaging thick and sparse silk fibers showed an improved focal depth with a slight decrease in lateral resolution and increase in background noise.

Tadpole shrimp (Triops longicaudatus) has become a major pest for California rice farmers. Currently, management relies solely on the insecticide lambda-cyhalothrin. However, resistance to this pyrethroid was confirmed in 2016, thus identifying an effective and practical biological control method for TPS is a priority. Field trials were conducted from 2017 to 2018 to: (1) evaluate the efficacy of the predatory fish Gambusia affinis and the predatory beetles, Laccophilus maculosus (Say) and Tropisternus lateralis (Fabricius), in controlling TPS, (2) test the efficacy of several inoculation rates of Gambusia affinis at controlling TPS and (3) to explore early indicators of TPS activity and damage as monitoring tools. Both Gambusia affinis and the predatory beetle treatments were not significantly different from the commercial standard (lambda-cyhalothrin). Both four and five Gambusia per 1 m2 controlled TPS as well as lambda-cyhalothrin, and we observed that Gambusia affinis was able to reproduce in the field. Water turbidity was significantly correlated with TPS counts (R = 0.85, N = 20, P &lt; 0.0001 (2017); R = 0.58, N = 30, P = 0.0007 (2018)). The number of dislodged seedlings was less reliably correlated with TPS count; in 2017 correlations were significant (R = 0.84, N = 20, P &lt; 0.0001), however, in 2018 correlations were not significant (R = 0.18, N = 30, P = 0.35). With further refinement water turbidity could play a valuable role in monitoring TPS populations.

The importance of predicting the exposure to environmental hazards is highlighted by issues like global climate change, public health problems caused by environment stresses, and property damages and depreciations. Several approaches have been used to assess potential exposure and achieve optimal results under various conditions, for example, for different scales, groups of people, or certain points in time. Micro-simulation tools are becoming increasingly important in human exposure assessment, where each person is simulated individually and continuously. This paper describes an agent-based model (ABM) framework that can dynamically simulate human exposure levels, along with their daily activities, in urban areas that are characterized by environmental stresses such as air pollution and heat stress. Within the framework, decision making processes can be included for each individual based on rule-based behavior to achieve goals under changing environmental conditions. The ideas described in this paper are implemented in a free and open source NetLogo platform. A simplified modeling scenario of the ABM framework in Hamburg, Germany, further demonstrates its utility in various urban environments and individual activity patterns, and portability to other models, programs and frameworks. The prototype model can potentially be extended to support environmental incidence management by exploring the daily routines of different groups of citizens and compare the effectiveness of different strategies. Further research is needed to fully develop an operational version of the model.

Background: The incidence of various types of cancer, for example papillary thyroid carcinoma (PTC), is on the rise. Since therapeutic success depends greatly on early diagnosis, reliable diag-nostic biomarkers must be identified and easy-to-apply tools for detecting them must urgently be standardized. Here, we contribute to solving this medical challenge by assessing miRNAs sus-pected of promoting carcinogenesis in extracellular vesicles (EVs) that can be routinely obtained via liquid biopsy. We profit from current progress in cancerology that provides innovations in liquid biopsy and EVs analysis along with identification of miRNAs and chaperone system (CS) components implicated in carcinogenesis. Methods: We measured in EVs obtained from circulat-ing blood plasma from PTC patients the levels of three miRNAs implicated in thyroid cancer, miR-1, miR-206, and miR-221, and most likely involved in the regulation of two members of the CS, Hsp60 and CCT. EVs were isolated from the plasma of patients with PTC and controls with benign goiter (BG), and from the culture medium of a PTC cell line (MDAT32) and were duly characterized. Results: The levels of miRNAs determined by RT-qPCR were consistently higher in PTC patients and decreased down to control levels after thyroidectomy. Bioinformatics showed that the miRNAs target genes were associated to the molecular pathogenesis of PTC. Conclusions: Thus, our exploratory study reaffirms the potential in clinics of the selected miRNAs in EVs as useful biomarkers of PTC easily accessible via liquid biopsy, which is minimally invasive and amenable to periodic repetition, an improvement compared to the established fine-needle aspirate biopsy.

The Asiatic apple leafminer, Phyllonorycter ringoniella (Matsumura), is a significant secondary pest of apple trees in Northeast Asia. To better understand its population dynamics, a population model based on temperature-developmental relationships was constructed. This model includes three sub-models: spring emergence, immature stage transition, and adult oviposition. Field data were collected from sex-pheromone baited traps in apple orchards in Andong, Korea, dur-ing 2015 and 2016 to validate the model. Simulations under six pesticide-natural enemy scenari-os showed that the population size of each generation was best simulated when weighted mor-tality factors for pesticides and natural enemies were applied. Using daily temperature inputs, the model demonstrated that P. ringoniella typically undergoes five generations per year, with peak times predicted within a seven-day margin of field data. Sensitivity analyses revealed that population size was influenced by total fecundity and the larval stage model, but peak times remained consistent despite parameter changes. Higher temperatures led to earlier adult peak dates, especially in summer generations. This model serves as a fundamental tool for estimating population dynamics and abundance changes of P. ringoniella and can guide the timings of pesti-cide application. Further validation is necessary to test the model's efficacy in controlling pests in apple orchards.

(1) Background: The study of microbiome is being crucial for his role in major systemic diseases, in particular the oral and gut microbiota. In the last years the study of microorganism correlated for example with neurodegenerative disease is increasing the prospective of a possible link between gut microbiota and brain. Here we report a new case report about a patient evaluated genetically firstly for dementia and late-onset dyskinesia, and then tested with 16S metagenomics sequencing. (2) Methods: Starting from buccal swab, we extracted bacterial DNA and then we performed a NGS metagenomics sequencing based on the amplification of hypervariable regions of the 16S in bacteria. (3) Results: The sequencing revealed the presence of Spirochaetes Phylum, a pathogenic bacterium generally known to be able to migrate to the Central Nervous System and not only. (4) Conclusions: Oral infections as our results shows could be possible contributing factors to various neurodegenerative conditions.

Natural risks encompass a range of disasters and dangers, requiring comprehensive management through assessments, anticipations, and advanced alert systems. Our specific focus is on landslides in challenging terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum approach, integrating qualitative parameters. Despite the challenges of the rugged terrain in Northern Algeria, it paradoxically hosts a dense population attracted by valuable hydro-agricultural resources. Our research is dedicated to studying landslide risks in these areas, particularly in the Mila region, with the aim of constructing a statistical model integrating considerations of both danger and vulnerability. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. The result is a valuable tool for effective risk management, strengthening safety and resilience against natural disasters. By understanding the intricate interplay of geological, environmental, and social elements, our research can contribute to the development of targeted interventions, fostering a more resilient community in the face of natural adversities.

This study investigated the aerodynamic performances of various trains with different nose shapes, using as design variables two angles α,β for the heads shape and the bluntness angle γ, without crosswind. The effect on aerodynamic performances, such as the train drag coefficient, pressure distribution along the train surface, flow structures around the train and the wake, head pressure pulse, were analyzed. The results indicate that the increase of the train nose length for flat shapes decreases the CD values by 21.47% and 19.11%, decreasing the high-pressure region in the leading head. The duck-nose configuration emerges as a compromise between drag reduction and nose length. Increasing the angle γ a further drag reduction of 8.5% is featured. Drag formation along the train is also analyzed. The steeper the variation of the geometry, the higher the peak intensity and the slope of the curve. Regarding the flow features around the train, two-main counter-rotating vortices are captured in the wake. Moreover, the higher the nose length and the higher the bluntness angle γ, the weaker and narrower the wake. Again, a longer nose shape yields to a softer jump in terms of pressure difference, crucial for train homologation and safety.

UV-A (315–400 nm) is a component of solar radiation impacting plant physiology. Consequently, the impact was investigated of UV-A supplementation on various aspects of kale cultivation in a plant factory with artificial lighting. Three UV-A intensity treatments (5, 10, or 15 W/m2) were applied to kale plants for 5 days before harvesting. The results revealed that 5 or 10 W/m2 UV-A supplementation provided significant advantages for kale growth. The fresh weight of the kale shoots increased by over 35–50% after UV-A exposure. In particular, the 10 W/m2 UV-A exposure significantly improved the photosynthetic rate, water use efficiency and quantum efficiency of photosystem II (Y(II)). In addition, the treatment with UV-A positively impacted changes in leaf spectral indices, resulting in decreases in the simple ratio index (SR), the nor-malized phaeophytinization index (NPQI), and the normalized difference red edge index (NDRE). Furthermore, the phytonutrients in kale (vitamin C, soluble protein, and total phenolics) were enhanced using the 10 W/m2 UV-A treatment. One notable outcome was the consistent reduction in nitrate contents across all UV-A treatment intensities. Therefore, supplementing kale cultiva-tion with UV-A radiation was a valuable strategy for enhancing kale yield and quality in plant factory production.

In a hyperconnected society, computer networks play a pivotal role in all activities that permeate our daily lives. In the context of smart cities, theses networks play a pivotal role in enhancing urban efficiency and sustainability. The continuous flow of packets in these networks poses security challenges, from personal data leakage to service unavailability. Anomaly-based methods are commonly used to detect network attacks. In this context, Software-Defined Networking (SDN) emerges as a solution for anomaly detection in network traffic, facilitating comprehensive, real-time analyses and dynamic adaptation to changes. This study aims to present a systematic review of feature selection techniques and evaluate the effectiveness of attribute selection with a multi-class classifier in network anomaly detection. The objective of this paper is to inspire future research and identify trade-offs among these techniques for detecting Denial of Service (DoS) attacks. Based on the findings, the analysis of the entire dataset yields superior results in terms of precision (99%), but using the output of OneR, although resulting in a slight loss of precision compared to the complete dataset, presents the highest precision among other studied techniques, indicating a trade-off between precision and processing time efficiency.

This study was to identify clinical cancer biomarkers for recurrent gastric cancer survivors using artificial intel-ligence. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random forest with MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive ex-Planations (SHAP) for feature importance analysis in this study. Our proposed Random forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86% and F1 of 88.2% on re-current category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI(body mass index), and gender, which significantly affect the prediction model's output, are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well.

3D printing is a non-traditional additive manufacturing process. It is different from the traditional subtractive manufacturing process. It offers exceptional rapid prototyping capabilities and results that traditional subtractive manufacturing methods cannot attain, especially in applications involving curved or intricately shaped components. Despite its advantages, metal 3D printing will face porosity, warpage, and surface roughness issues. These issues will affect the future practical application of the parts indirectly, for example, the structural strength and the parts assembly ca-pability. Therefore, this study compares the qualities of the warpage, weight, and surface rough-ness after milling and grinding processes under the same materials (316L stainless steel) between general rolled steel and 3D additive steel. Experimental results show that 3D printing parts are approximately 13% to 14% lighter than the general rolled steel. The surface roughness performance of 3D printing steel is better than general rolled steel under the same material after milling or grinding processing. The hardness of the 3D printing steel is better than that of the gen-eral rolled steel. The research verifies that 3D additive manufacturing can use surface processing to optimize surface performance and achieve the functions of lightness and hardness.

Green spaces take part a vital role in reducing the harmful effects occurred through the process of rapid urbanization. This study focuses on evaluating the change of green space and per capita green space in Colombo District, Sri Lanka in between 2008 and 2015 using Landsat images. The NDVI differencing method and classification method were used to detect the change of land cover. According to the results, a gradual decline of green space from 629.1km2 to 591.16km2 with a rate of 6.03% can be depicted. The change is considerable in Kaduwela, Moratuwa and Maharagama DSDs and quite better in the areas such as Avissawella, Homagama and Padukka. The per capita green space has been decreased from 272.361 m2 to 248.811 m2. Though these rates are very well higher than the UN, EU and WHO standards for the district, Colombo DSD do not comply with UN and EU standards. The case would turn into worst in 2025, as the predictions with current rates. Therefore, the study recommends taking immediate actions to make a greener city. Further, a tax system is proposed to get the attraction of the general public.

This study involves the development of a hybrid forecasting framework that integrates two different models in a framework to improve prediction capability. Although the concept of hybridization is not a new issue in forecasting, our approach presents a new structure that combines two standard simple forecasting models uniquely for superior performance. Hybridization is significant for complex data sets with multiple patterns. Such data sets do not respond well to simple models, and hybrid models based on the integration of various forecasting tools often lead to better forecasting performance. The proposed architecture includes serially connected ARIMA and ANN models. The original data set is first processed by ARIMA. The error (i.e., residuals) of the ARIMA is sent to the ANN for secondary processing. Between these two models, there is a classification mechanism where the raw output of the ARIMA is categorized into three groups before they are sent to the secondary model. The algorithm is tested on well-known forecasting cases from the literature. The proposed model performs better than existing methods in most cases.

Multi-cameras system is widely applied in 3D computer vision especially when multiple cameras are distributed on both sides of the measured object. The calibration methods of multi-cameras system are critical to the accuracy of vision measurement and the key is to find an appropriate calibration target. In this paper, a high-precision camera calibration method for multi-cameras system based on transparent glass checkerboard and ray tracing is described, which is used to calibrate multiple cameras distributed on both sides of the glass checkerboard. Firstly, the intrinsic parameters of each camera is obtained by Zhang’s calibration method. Then, multiple cameras capture several images from the front and back of the glass checkerboard with different orientations, and all images contain distinct grid corners. As the cameras on one side are not affected by the refraction of glass checkerboard, extrinsic parameters can be directly calculated. However, the cameras on another side are influenced by the refraction of glass checkerboard, and the direct use of projection model will produce calibration error. A multi-cameras calibration method using refractive projection model and ray tracing is developed to eliminate this error. Furthermore, both synthetic and real data are employed to validate the proposed approach. The experimental results of refractive calibration show that the error of the 3D reconstruction is smaller than 0.2 mm, the relative errors of both rotation and translation are less than 0.014%, and the mean and standard deviation of reprojection error of 4-cameras system are 0.00007 and 0.4543 pixel. The proposed method is flexible, high accurate, and simple to carry out.

This study utilized electrical resistivity imaging (ERI) to investigate subsurface characteristics near Nicolaus Copernicus University Polar Station on the western Spitsbergen-Kaffiøyra Plain, island in the Svalbard archipelago. Surveys along two lines, LN (148 meters) collected in 2022 and 2023 and ST (40 meters) collected in 2023, were conducted to assess resistivity and its correlation with ground temperatures. The LN line revealed a 1 to 2 meters thick resistive unsaturated outwash sediment layer, potentially indicative of permafrost. Comparing LN resistivity result between 2022 and 2023, a 600 Ohm-m decrease in the unsaturated active layer in 2023 was observed, attributed to a 5.8℃ temperature increase, suggesting a link to global warming. ERI along the ST line depicted resistivity reaching its minimum at approximately 1.6 meters, rising to over 200 Ohm-m at 4 meters, and slightly decreasing to around 150 Ohm-m at 7 meters. Temperature measurements from the ST line's monitoring well confirmed permafrost at a depth of 1.6 to 2 meters. Additionally, water content distribution in the ST line was estimated after temperature correction, revealing a groundwater table depth of approximately 1.06 meters, consistent with measurements from the S4 borehole on the ST line. This study provides valuable insights into Arctic subsurface dynamics, emphasizing the sensitivity of resistivity patterns to climate change and offering a comprehensive understanding of permafrost behavior in the region.

The methods of Artificial Intelligence (AI) have been used in the planning and operation of power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI including the use of Machine Learning (ML) to the power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way considering the supervised and unsupervised learning methods. Based on this, three complex application examples: wind power generation forecasting, smart grid security as-sessment (using two methods), and automatic system fault detection are presented and discussed in detail. Summary and outlook conclude the paper.

We review our theory of interdependence applied to autonomous human-machine teams, with which we have made several generalizations based on theory that have led to new discoveries, compared to social science which is struggling to break free of its validation crisis, but also has no generalizations to new discoveries with human teams, let alone to human-machine teams. In this invited article, based around the stability of a team, of our discoveries, we review the uncertainty between a team's structure and its performance, our first finding. We generalize that finding to deception, vulnerability, innovation and fittedness. From there, our next generalization is to the quantum likeness of interdependence and quantum mechanics; we move on to Shannon holes, knowledge, and conclude with the value of debate and the need for boundaries to contain debate, to reduce interference, and to protect states of interdependence. After the introduction, we extend our model in Section 2 to energy and time, and we close with a brief review of innovation and future plans.

Advances in biomimetic triboelectric nanogenerators (TENGs) have significant implications for electronic skin (e-skin) and human-machine interaction (HMI). Emphasizing the need to mimic complex functionalities of natural systems, particularly human skin, TENGs leverage triboelectricity and electrostatic induction to bridge the gap in traditional electronic devices' responsiveness and adaptability. The exploration begins with an overview of TENGs' operational principles and modes, transitioning into structural and material biomimicry inspired by plant and animal models, proteins, fibers, and hydrogels. Key applications in tactile sensing, motion sensing, and intelligent control within e-skins and HMI systems are highlighted, showcasing TENGs' potential in revolutionizing wearable technologies and robotic systems. This review also addresses the challenges in performance enhancement, scalability, and system integration of TENGs. It points to future research directions, including optimizing energy conversion efficiency, discovering new materials, and employing micro-nanostructuring techniques for enhanced triboelectric charges and energy conversion. The scalability and cost-effectiveness of TENG production, pivotal for mainstream application, are discussed along with the need for versatile integration with various electronic systems. The review underlines the significance of making bioinspired TENGs more accessible and applicable in everyday technology, focusing on compatibility, user comfort, and durability. Conclusively, it underscores the role of bioinspired TENGs in advancing wearable technology and interactive systems, indicating a bright future for these innovations in practical applications.

Hybrid machine tools combining additive and subtractive processes have arisen as a solution to the increasing manufacture requirements, boosting the potentials of both technologies, while compensating and minimizing their limitations. Nevertheless, the idea of hybrid machines is relatively new and there is a notable lack of knowledge in the field. Therefore, in the present paper, an insight into the advancements of hybrid machines is given, identifying their real capabilities, together with the latest developments from an industrial context. In addition, the current situation and future perspectives of hybrid machines from the point of view of process planning, monitoring and inspection are discussed. Finally, the challenges that must be overcome and the opportunities that the hybrid machines will provide in the forthcoming years are presented.

In the paper is described the control electronics for an industrial pneumatic – hydraulic system based on a low-cost PLC. The developed system is a hydraulic pulse system and it generates series of high pressure hydraulic pulses (max. 200 bar). We describe requirements, an overall concept of the embedded control system, user interface, security features and network connectivity. In the description of the software solution we describe implementation of hierarchical ordered program threads (multithreaded program) and main control state machine. At the conclusion, we describe the calibration method of the system and calibration curves and we present the schematic diagram and a photo of a functional prototype of the system.

This paper presents a state machine based architecture which enhances flexibility and reusability of industrial robots, more concretely dual-arm multisensor robots. The proposed architecture, in addition to allowing absolute control of the execution, eases the programming of new applications by increasing the reusability of the developed modules. Through an easy-to-use graphical user interface, operators are able to create, modify, reuse and maintain industrial processes increasing the flexibility of the cell. Moreover, the proposed approach is applied in a real use case in order to demonstrate its capabilities and feasibility in industrial environments. A comparative analysis is presented for evaluating presented approach versus traditional robot programming techniques.

Osteoarthritis and chronic pancreatitis comorbidity are insufficiently studied, and the reciprocal impact of these conditions remains poorly understood. This study aimed to investigate potential predictors for the development of osteoarthritis and chronic pancreatitis comorbidity, as well as complications associated with this comorbidity. A cohort of 150 patients was categorized into three groups: a control group (n=30), patients with osteoarthritis (n=68), and patients with comorbid osteoarthritis and chronic pancreatitis (n=52). Potential predictors for comorbidity development and associated complications were explored. The study revealed that concurrent osteoarthritis and chronic pancreatitis exacerbate each other's progression and contribute to malnutrition. Body Mass Index (BMI) emerged as a potential predictor for chronic pancreatitis development in osteoarthritis patients. In osteoarthritis patients, fecal elastase-1 levels were identified as potential predictors for exocrine pancreatic insufficiency. Factors such as WOMAC total score, fecal elastase-1, C-reactive protein (CRP), ferritin, retinol, tocopherol, 25-hydroxy vitamin D3, and BMI were found to influence comorbidity development between osteoarthritis and chronic pancreatitis. Additionally, Gastrointestinal Symptom Rating Scale-Diarrhea Syndrome (GSRS-DS), Gastrointestinal Symptom Rating Scale-Constipation syndrome (GSRS-CS), Qualitative Assessment of the Symptoms and Impact of Pancreatic Exocrine Insufficiency domain A (PEI-Q-A), retinol, tocopherol, and iron were identified as potential predictors for exocrine pancreatic insufficiency in osteoarthritis patients. In conclusion, the comorbidity of osteoarthritis and chronic pancreatitis exacerbates disease progression and complications, necessitating further investigation.

Background: to analyze the impact of COVID-19 pandemic on characteristics and management of uveal melanoma (UM) in National Referral Center in Poland. Materials and Methods: the retrospective analysis of 1336 patients who were newly diagnosed with UM at Department of Ophthalmology and Ophthalmic Oncology, Jagiellonian University Collegium Medicum Krakow, Poland between January 1, 2018 and December 31, 2021. The demographic and clinical data were compiled including localization, size and treatment methods of tumors. Results: In total, 728 patients with UM were included before COVID-19 pandemic in years 2018-2019 and 608 were included during COVID-19 pandemic in years 2020-2021. Fixed-base dynamics indicators for the incidence of uveal melanoma (base year 2018) in the national referral center in Poland were 80.22% and 86.81% in year 2020 and 2021, respectively. UMs were statistically significantly larger and more frequently localized anterior to the equator of the eye globe in year 2021 than in year 2018 (Chi^2 Pearson test p= 0.0001 and p= 0.0077, respectively). The rate of patients treated with enucleation increased from 15.94% in year 2018 to 26.90% in year 2021 (Chi^2 Pearson test p=0.0005). Conclusions: statistically significant differences were found in the management of uveal melanoma in National Referral Center in Poland during COVID-19 pandemic with tumors being larger, more frequently localized anterior to the equator of the eye globe and more often enucleated.

Semantic segmentation of high-resolution remote sensing images provides detailed and precise feature information, playing a pivotal role in decision-making and analysis in sectors such as water management, agriculture, military, and environmental protection. However, most methods merely combine features from various branches directly, without a mechanism for spatial location feature screening, and treat all extracted features as equally important. To overcome these limitations, we introduce a novel spatially adaptive interaction network (SAINet) for dynamic interaction across different features in remote sensing semantic segmentation task. Specifically, we propose a spatial refined module that leverages local context information to filter spatial locations and extract salient regions. Following this, we introduce an innovative spatial interaction module that uses a spatial adaptive modulation mechanism to dynamically select and allocate spatial position weights. This facilitates interaction between local salient areas and global information, allowing the network to focus more on relevant regions. This flexible approach enables the network to capture more informative features, thereby enhancing segmentation accuracy. Experiments conducted on the DeepGlobe, Vaihingen, and Potsdam public datasets confirm the effectiveness and capability of SAINet. Our code and models will be publicly available

Methods for cost calculation of Laser-based Additive Manufacturing (LAM) have evolved over the last two decades. Starting from a rapid prototyping manufacturing method, cost calculation over time included more than single part production for small series or mixed batches. New machine developments nowadays aim at large scale production and introduce modular machine designs that give LAM factory operators more decision freedom on machine configurations and expansibility. To leverage this new possibilities, LAM costing methods must be adapted to calculate and visualize the economic consequences of different module configurations.In this work a modelling architecture of L-PBF machines using virtual modules is presented. Using principles of the industry 4.0 communication standard OPC UA, a virtual machine is set up, parametrized and connected to the AM digital process chain. Two different views on the cost information are used, optimized for different use cases.First, the product view allows detailed analysis of cost allocation to parts in mixed batches and the layered geometry of the part. Second, the machine view tracks utilization of all machine modules, divided into the states idle, active, off and maintenance. The theoretical Overall Equipment Effectiveness (OEE) is calculated on a module basis, enabling economic analysis and optimization of module composition as well as module improvements and new modular machine constellations. This allows focusing research and development on most valuable improvements. By calculating different scenarios like spare part production, mass production or rapid prototyping, the part portfolio to be manufactured can be considered.

Machine reading comprehension aims to teach machines to understand a text like a human, and is a new challenging direction in Artificial Intelligence. Datasets play an important role while describing or building an algorithm for machine reading comprehension. The type of answers we required from developed algorithm depends on datasets.The datasets are classified into two types, namely datasets with extractive answers and datasets with descriptive answers. This article summarize both datasets with an example of each type to get better insight of datasets in machine reading comprehension and which datasets to use depending the requirements.

With the widespread use of electric machines there is a growing need to extract information from the machines to optimize their control systems and maintenance management. The present work shows the development of an embedded system to perform the monitoring of an squirrel cage induction motor rotor physical variables. The system comprises: circuit to acquire desirable rotor variable(s) value(s) and send it to the computer; a rectifier and power storage circuit that besides converting an alternating current in continuous also store energy for a certain amount of time to wait the motor engine shutdown; and magnetic generator that harvest energy from rotating field to power the circuits mentioned above. The embedded system is set on the rotor, making it difficult to power the system because it is rotating, problem solved with the construction of the magnetic generator eliminating the need of using batteries or collector rings and send data to the computer using a wireless NRF24L01 module. For the proposed system validation a temperature sensor (DS18b20) was used, variable known as the most important when identifying the need for maintenance and control systems. Tests were made getting satisfactory results proving the viability of using sensors on the rotor.

Background: Active case finding for TB was implemented in selected sixty high TB burden wards of Kolkata, India. Community volunteers called TOUCH agents (TAs) identified and referred presumptive TB patients (PTBPs) to health facilities for TB diagnosis and treatment. We aimed to describe the ‘care cascade’ of PTBPs identified during July to December, 2018 and to explore the reasons for attrition as perceived by TAs and PTBPs. Methods: An explanatory mixed methods study with quantitative phase of cohort study using routinely collected data followed by descriptive qualitative study with in-depth interviews was conducted. Results: Of the 3, 86,242 individuals enumerated, 1132 (0.3%) PTBPs were identified. Only 713 (63.0%) PTBPs visited referred facility for TB diagnosis. TB was diagnosed in 177 (24.8%) and the number needed to screen for one TB was 2,183 individuals. The potential reasons for low yield were stigma and apprehension about TB, distrust about TA, wage loss for attending health facilities and substance abuse among PTBPs. Conclusion: The yield of ACF was suboptimal with low PTBP identification rate and high attrition rate. Interviewing each individual for symptoms of TB and supporting PTBPs for diagnosis through sputum collection and transport can be adopted to improve the yield.

Statistical machine translation (SMT) which was the dominant paradigm in machine translation (MT) research for nearly three decades has recently been superseded by the end-to-end deep learning approaches to MT. Although deep neural models produce state-of-the-art results in many translation tasks, they are found to under-perform on resource-poor scenarios. Despite some success, none of the present-day benchmarks that have tried to overcome this problem can be regarded as a universal solution to the problem of translation of many low-resource languages. In this work, we investigate the performance of phrase-based SMT (PB-SMT) and NMT on two rarely-tested low-resource language-pairs, English-to-Tamil and Hindi-to-Tamil, taking a specialised data domain (software localisation) into consideration. This paper demonstrates our findings including the identification of several issues of the current neural approaches to low-resource domain-specific text translation and rankings of our MT systems via a social media platform-based human evaluation scheme.

The study investigated farmers’ perception on pests and diseases management strategies, agricultural practices, production constraints and selection criteria of elite varieties using a survey research design. A total of 700 questionnaires were administered to smallholder farmers who were farming for household consumption, those producing for sale and household consumption and those who were mainly producing for sale because their primary goal was to produce for the market. Findings revealed that the most prevalent pests identified were whiteflies (Bemisia tabaci), grasshoppers (Zonocerus variegatus), aphids (Aphis gossypii), mealybugs (Phenacoccus manihoti), termites (Isoptera), and grasscutters (Thryonomys swinderianus). The three most common diseases of cassava limiting growth and yield with mixed infection include viral, bacterial, and fungal. The study also revealed that only 12.5% of farmers cultivated improved cassava varieties. The management practices implemented encompassed field sanitation and the application of pesticides. Lack of capital and no availability of market were the most important production and marketing constraints faced by farmers in the study area, whereas poundability and high storage root yield are the most preferred traits farmers consider in selecting a new variety. Results suggest that smallholder farmers should endeavor to undergo various developmental programs to develop themselves to increase their competitive advantage in their locality and in the local and global agricultural sectors. In addition, governments and other policy makers should strategize plans and programs that would contribute to smallholder farmers’ agriculture, community and national development.

Some glucagon-like peptide-1 receptor agonists (GLP-1 RAs), first used in the treat-ment of type 2 diabetes mellitus (T2DM), have been approved for the treatment of obe-sity in patients with or without T2DM (liraglutide – LIR, semaglutide - SEM, and tir-zepatide - TIR). Social media had an important influence on the off-label use of GLP-1 RAs for obesity, especially for SEM. We analysed the Google queries related to SEM to assess people's interest in this drug. We also investigated the occurrence of adverse drug reactions (ADRs) by searching the EudraVigilance database (EV) for Individual Case Safety Reports (ICSRs) that reported SEM as the suspected drug and performed a descriptive and a disproportionality analysis. Data obtained for SEM were compared to other GLP-1 RAs. SEM had the highest proportions of searches on Google associated with the term “weight loss” and presented the lowest number of severe ADRs, but it also had the highest number of ICSRs reported in EV. Even though no unexpected safety issues have been reported for it until now, SEM has a high tendency for overdose reports. The most frequent off-label use was reported for SEM and TIR. In order to lower the ADRs’ risks, the off-label use should be reduced and carefully monitored.

DNA barcoding has emerged as a powerful tool in the field of taxonomy, transforming the way we identify and classify species. This review article aims to provide an up-to-date overview of the advancements in DNA barcoding techniques, their applications, and their impact on taxonomy and biodiversity studies. We will explore the evolution of DNA barcoding, the development of standardized barcode regions, and the challenges and limitations associated with this methodology. Additionally, we will delve into the diverse range of taxonomic groups for which DNA barcoding has been applied, highlighting key case studies and their contributions to our understanding of biodiversity. Furthermore, we will discuss the integration of DNA barcoding with other molecular and bioinformatic approaches, such as metabarcoding and machine learning, to enhance species identification and biodiversity assessment. This review aims to provide a comprehensive perspective on how DNA barcoding is reshaping the field of taxonomy and its implications for conservation, ecology, and evolutionary biology.

We investigate regular damped oscillatory structures appearing in the "effective" form factor of various hadrons. The behavior of this form factor can be extracted from the experimental information on total cross sections, the processes $\sigma_{tot}(e^+e^- \to h \bar h)$, $h=\pi^{\pm}, K^{\pm}, K^0, p, n $ are examined. The apparent oscillations have been observed for the first time for the proton and we show, taking also other hadrons into the consideration, that they are an arbitrary artifact resulting from a very simplistic theoretical description based on an elementary three-parametric model. If the data are described by a more appropriate and well physically funded U\&A model, then the oscillations disappear. In addition, if the simple model is used to describe the data, one observes that the oscillations are opposite for particles which form an isospin dublet. We show, by using the U\&A model, that this feature is explained by the transformation properties of the corresponding states in the isotopic space.

SOC stock (SOCS) changes caused by land use changes are still unclear, and understanding this response is essential for many environmental policies and land management practices. In this study, we investigated the temporal-spatial and vertical distribution characteristics of SOCS on the Western Sichuan Plateau (WSP) using the sparrow search algorithm-random forest regression (SSA-RFR) models with excellent model applicability and accuracy. The temporal-spatial varia-tions in the SOCS were modeled using 1080 soil samples and a set of nine environmental covariates. We analyzed the effect of land use changes on the SOCS on the WSP. The total SOCS increased by 18.03 Tg C from 1990 to 2020. The results of this study confirmed that the SOCS in the study area has increased significantly since 2010, with an increase of 27.88 Tg C compared to the total SOCS in 2010. We found that the spatial distribution of the SOCS increased from southeast to northwest, and the vertical distribution of the SOCS in the study area decreased with increasing soil depth. Forest and grassland are the main sources of SOCS, the total SOCS in the forest and grassland accounted for 37.53 and 59.39% of the total SOC pool in 2020, respectively. Expansion of the wetlands, forest, and grassland areas could increase SOCS in the study area.

The task of human action recognition (HAR) based on skeleton data is a challenging yet crucial technique owing to its wide-ranging applications in numerous domains, including patient monitoring, security surveillance, and observation of human-machine interactions. While numerous algorithms have been proposed in an attempt to distinguish between a myriad of activities, most practical applications necessitate highly accurate detection of specific activity types. This study proposes a novel and highly accurate spatiotemporal graph autoencoder network for HAR based on skeleton data. Furthermore, an extensive investigation was conducted employing diverse modalities. To this end, a spatiotemporal graph autoencoder was constructed to automatically learn both spatial and temporal patterns from human skeleton datasets. The powerful graph convolutional network, designated as GA-GCN, developed in this study, notably outperforms the majority of existing state-of-the-art methods when evaluated on two common datasets, namely NTU RGB+D and NTU RGB+D 120. On the first dataset, the proposed approach achieved accuracies of 92.3\% and 96.8\% for the cross-subject and cross-view evaluations, respectively. On the more challenging NTU RGB+D 120 dataset, GA-GCN attained accuracies of 88.8\% and 90.4\% for the cross-subject and cross-set evaluations, respectively.

This study investigated the magnitude and direction of inter-limb asymmetry in 21 professional female sepak takraw players across several task-specific tests. Five inter-limb asymmetry assessments were employed: unilateral countermovement jump (Uni-CMJ), bilateral countermovement jump (Bi-CMJ), single leg hop (SLH), triple hop test (THOP), and isokinetic concentric peak torque of the knee flexors and extensors at 60 deg/sec-1, 120 deg/sec-1 and 180 deg/sec-1 angular velocities. A “true” inter-limb asymmetry was only observed for Uni-CMJ jump height (16.62%) and THOP distance (6.09%). Kappa coefficients demonstrated fair agreement in the direction of asymmetry between the Uni-CMJ and Bi-CMJ tests for jump height (Kappa = 26.67), but only slight agreement for peak force (Kappa = 0.11), propulsive impulse (Kappa = -0.12) and eccentric impulse (Kappa = -0.14), respectively. Fair agreement was observed between the SLH and THOP (Kappa = 0.32). Slight to moderate agreement was found for concentric peak torque across angular velocities for the knee extensors (Kappa = 0.08 to 0.48) while fair to nearly perfect agreement was noted for the knee flexors (Kappa = 0.31 to 1). The Uni-CMJ and THOP are most sensitive to detect between-limb asymmetries in female sepak takraw players. However, given the inconsistency in asymmetry direction across tests, monitoring asymmetry direction is crucial for conditioning.

Abstract: This study aimed to evaluate the long-term impact of paediatric dentistry programmes on children and adolescents to reduce oral health inequalities. It measures and assesses the improvement effects of paediatric dentistry programmes on the oral health of children and adolescents as part of the efforts to decrease oral health disparities in this age group. It included 406 individuals who participated in student and paediatric dentistry programmes between 2013 and 2019 at screening facilities in Gwangjin-gu, Seoul. Frequency analysis was conducted for demographic characteristics, and binary logistic regression analysis was performed to identify factors influencing the prevalence of dental caries as the dependent variable. Data were analysed using PASW Statistics with the statistical significance level set at α=0.05. Regarding oral health indicators based on the frequency of participation in paediatric dentistry programmes for children and adolescents, participants with seven or more sessions had lower prevalence rates of dental caries, malocclusion, and periodontal disease than those with only one session. Second, when comparing oral health indicators in paediatric dentistry programmes between primary and adolescent age groups, individuals under continuous management showed a decrease in permanent teeth affected by dental caries, dental caries prevalence, and malocclusion prevalence (excluding primary school age). Third, binary logistic regression analysis revealed significant influences (p

Alzheimer's disease (AD) is a stealthy and progressive neurological disorder that is a leading cause of dementia in the global elderly population, imposing a significant burden on both the elderly and society. Currently, the condition is treated with medications that alleviate symptoms. Nonetheless, these drugs may not consistently produce the desired results and can cause serious side effects. Hence, there is a vigorous pursuit of alternative options to enhance the quality of life for patients. Ginkgo biloba (GB), an herb with historical use in traditional medicine, contains bioactive compounds such as terpenoids (Ginkgolides A, B, and C), polyphenols, organic acids, and flavonoids (quercetin, kaempferol, and isorhamnetin). These compounds are associated with anti-inflammatory, antioxidant, and neuroprotective properties, making them valuable for cognitive health. A systematic search across three databases using specific keywords GB in AD and dementia yielded 1702 documents, leading to the selection of 15 clinical trials for synthesis. In eleven studies, GB extract/EGb 761® was shown to improve cognitive function, neuropsychiatric symptoms, and functional abilities in both dementia types. In four studies, however, there were no significant differences between the GB-treated and placebo groups. Significant improvements were observed in scores obtained from the Mini-Mental State Examination (MMSE), Short Cognitive Performance Test (SKT), and Neuropsychiatric Inventory (NPI). While the majority of synthesized clinical trials show Ginkgo biloba has promising potential in these conditions, more research is needed to determine optimal dosages, effective delivery methods, and appropriate pharmaceutical formulations. Furthermore, a thorough assessment of adverse effects, exploration of long-term use implications, and investigation into potential drug interactions are critical aspects that must be carefully evaluated in future studies.

Graphene-based materials (GBMs) are a prospective material of choice for rechargeable battery electrodes because of their unique set of qualities, which include tunable interlayer channels, high specific surface area, and strong electrical conductivity characteristics. The market for commercial rechargeable batteries is now dominated by lithium-ion batteries (LIBs), however issues with organic electrolyte safety, high lithium pricing, and limited lithium resources have hindered LIB development. Zinc-based rechargeable batteries have emerged as a viable substitute for rechargeable batteries due to their affordability, safety, and improved performance. This review article explores recent developments in the synthesis and advancement of GBMs for rechargeable zinc‐air batteries (ZABs), and common graphene-based electrocatalyst types. An outlook on the difficulties and probable future paths of this extremely promising field of study is provided at the end.

Glial cell line-derived neurotrophic factor GDNF is among the strongest dopamine neuron function and survival promoting factors known. Due to this reason, it has clinical relevance in dopamine disorders such as Parkinson’s disease and Schizophrenia. In the striatum, GDNF is exclusively expressed in interneurons which make up only about 0.6% of striatal cells. Despite clinical significance, histological analysis of striatal GDNF system arborization and relevance to incoming dopamine axons, which bear its receptor RET, has remained enigmatic. This is mainly due to the lack of antibodies able to visualize GDNF and RET positive cellular processes; here we overcome this problem by using knock-in marker alleles. We find that GDNF neurons chemoattract RET+ axons at least 7 times longer in distance than medium spiny neurons (MSNs), which make up 95% of striatal neurons. Furthermore, we provide evidence that tyrosine hydroxylase, the rate limiting enzyme in dopamine synthesis, is enriched towards GDNF neurons in the dopamine axons. Finally, we find that GDNF neuron arborizations occupy approximately twelve times less striatal volume than 135 times more abundant MSNs. Collectively, our results improve our understanding of how endogenous GDNF affects striatal dopamine system function.

It is important to consider both requirements and limitations for the generation and sharing of genomic data. For example, human genomic sequence data should typically not be deposited publicly without the appropriate "explicit consent" to do so. This includes genetic identifying data that may exist within raw reads for bulk RNA Sequencing (RNA-Seq) data and genomic data from cell lines that are available to purchase. At the same time, data sharing is important for re-analysis and reproducibility/replication in the scientific community. A review of known rules and guidelines for genomic data sharing is provided. This includes specific rules for NIH-funded data for controlled access deposit of HeLa cell line data, including bulk RNA-Seq data. The ability to create “partial” Gene Expression Omnibus (GEO) public deposits with only processed data is described, with search criteria that can identify many “partial” GEO deposits for a variety of data types where reads were not made public for patient privacy concerns. However, it is the opinion of the author that this should be considered a short-term solution, and additional considerations and action should be carried out for genomic data generated in future experiments. Information about how to learn more about what is known for consent for cell line samples is also briefly provided, along with search results for genomic data from widely available cell lines that is deposited in controlled access databases. Finally, it should be made clear that this article presents some amount of opinion. Additionally, open feedback for this preprint is encouraged to further enhance knowledge and communication.

Neural-objective and image optimization approaches for drilling fluid rheology automation are crucial for drilling engineering optimization. A myriad of intelligent computational models are employed to predict and monitor the parameters of mud rheology and filter cake permeability posture using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and a model optimization tool. 498 datasets of synthetic-based mud (SBM) flat rheology from a drilling mud laboratory at Nazarbayev University were imported into a user-friendly MATLAB application using image processing and nftool. Apart from the Google TensorFlow Sequential API DNN architecture used, a Levenberg-Marquardt training algorithm with input sigmoid hidden neurons 10, 12, and 18 coupled with a linear output layer was also used to predict the SBM flow index. OBM and SBM filter cakes were processed for void spaces; however, a model study was extrapolated to maximize filtration volume Vf. The study’s findings show that the ANN-FF model employed for rheological property monitoring and prediction had higher steady and exponential levels of accuracy and a correlation coefficient of 0.96-0.99. More so, SBM and OBM image processing presented an area of void spaces of 1790M2 and 1739M2, respectively. The porosity and permeability postures of both SBM and OBM resulted in a significant void space capable of maximizing the flow index. In addition, the single-objective modelling based on a genetic algorithm did validate the experimental rheological data for flow index or filtration volume Vf optimization; the study resulted in the finding that the constrained physics-informed objective function hindered maximizing oil recovery and instead predicted possible formation damage. It is empirical to note that automating flow predictions with neural, objective, and image functions has presented an alternative novel method for non-programmers using MATLAB and Google Colabs that is capable of enhancing mud rheological parameters, drilling efficiency, and hydrocarbon recovery.

We explore several aspects of replica synchronization with the goas of retrieving the value of parameters for the Lorenz system. The idea is that of having a computer replica (slave) of a natural system (master, simulated in this paper), and exploit the fact that slave synchronizes with the master only if they evolve with the same parameters. As a byproduct, in the synchronized phase the state variables of the slave and that of the master are the same, thus allowing to perform measurements impossible on the real system. We review some aspects of master-slave synchronization using a subset of variables, with intermittent coupling. We show how synchronization can be achieved when some of the state variables are available for direct measurement using a simulated annealing approach, and also when they are accessible only through a scalar function, using a pruned-enriching ensemble approach, similar to genetic algorithms without cross-over.

IoT is a computational perception that defines a situation, where smart objects are interconnected to the internet/ network. In the last few years, IoT objects have increased in all sectors/ fields and use in all domains of our lives. As the number of devices increases, the privacy of data becomes an important issue. To deal with this issue different techniques have been used by researchers in this field. Unfortunately, these methods have less traceability, privacy of data, and accountability. This paper aims to design a blockchain-based network architecture for Traceability, Data privacy, and Accountability (TDA). Technologies of blockchain are known as a distributed ledger of transactions record, which record the life span information with a timestamp. The major features of blockchain are persistency, decentralization, and audibility. These features help to decrease the budget and increase efficiency. Moreover, to strengthen the TDA architecture, this paper also discusses the performance of the proposed architecture.