The purpose of this project is to develop and build an extensible fixture. This fixture is ideal to milling, drilling, and shaping operations. Jigs and fittings are often the most cost-effective methods of mass fabricating a component. As a result, jigs and fittings are used and play a vital part in the mass production system. These are specialist work holding and tool-guiding devices. The quality of jigs and fixtures used in a process has a substantial impact on its performance. A fixture is distinctive because it is meant to fit a given area or shape. The basic purpose of a fixture is to find and, in certain instances, hold a workpiece throughout an operation. A jig differs from a fixture in that it leads the tool to the right position or movement during an operation, in addition to locating and supporting the workpiece. When a key is reproduced, the original key serves as a base for the path reader, which regulates the tool's movement to construct the duplicate key. In this situation, a CWC machine's route reader works as a jig, with the original being referred to as the template. Sometimes the template and jig refer to the same part of a production system.

The Balanced Scorecard (BSC) is a widely recognized strategic performance metric that has been extensively studied in large corporations but remains underexplored in the context of Small and Medium-sized Enterprises (SMEs). Understanding the effectiveness of BSC in aligning performance metrics with strategic goals and enhancing performance management in SMEs is critical for fostering growth and competitive advantage. This systematic review aims to evaluate the effectiveness of the Balanced Scorecard in measuring performance and strategy execution within SMEs, particularly focusing on financial and non-financial outcomes such as employee motivation, customer satisfaction, and business sustainability. A systematic review was conducted following the PRISMA framework, incorporating 136 studies sourced from Google Scholar, Web of Science, and SCOPUS. Eligibility criteria focused on publications from the last ten years that address the implementation of BSC in SMEs. Quantitative, qualitative, and mixed-method studies were included, with 49.63% of studies using quantitative methods, 26.67% qualitative, and 23.70% mixed-methods. Risk of bias was assessed using the Chrohane tool, revealing high risks in 90 studies due to incomplete outcome data and selective reporting. The review found that 74% of studies adopted a holistic approach by focusing on all four BSC perspectives (Financial, Customer, Internal Processes, and Learning and Growth). ROI emerged as the most frequently discussed key performance indicator, addressed in 36.84% of publications, while customer satisfaction and employee engagement were covered in 26.32% each. Geographic disparities were observed, with most of the research concentrated in Asia and Europe (40%) and only 8% in Africa. Despite positive outcomes such as improved business sustainability and competitive advantage (38.52% of studies), challenges persisted, including poor strategy execution and limited resources, necessitating careful customization of BSC to SME-specific contexts. The Balanced Scorecard demonstrates significant potential in enhancing performance management and strategic execution within SMEs, particularly when tailored to address unique constraints such as resource limitations. This review highlights the need for further research to standardize key performance indicators and explore sector-specific applications. By addressing these gaps, the BSC can be more effectively utilized to drive growth, agility, and long-term competitive advantage in SMEs.

Exposure to endocrine-disrupting chemicals (EDCs) is inevitable, and growing scientific evidence indicates that even very low doses can negatively impact human health, particularly during pregnancy and the neonatal period. As part of the European project LIFE18 ENV/IT/00460, this study aims to identify the presence of EDCs in 20 infant formulas (both powdered and liquid) and the release from baby bottles and teats. Particularly, sensitization of young people and future parents towards the potential harmful effects of EDCs could significantly help to reduce exposure. Seven different UPLC-MS/MS methodologies and one ICP-AES were set-up to quantify already assessed and suspected EDCs among 85 different chemicals (bisphenols, parabens, phthalates, pesticides, herbicides, and their main metabolites, PFAS, and metals). Results showed that in 2 out of 14 baby bottles only anthracene and phenanthrene of the group of PAHs were released (10.68-10.81 ng/mL). Phthalates such as mono-ethyl phthalate (MEP) were found in 9 of 14 samples (0.054–0.140 ng/mL), while mono(2-ethyl-5-oxohexyl) phthalate (MeOHP) appeared in 2 samples (0.870–0.930 ng/mL). In accordance with current EU regulations, other chemicals were not detected in baby bottles and teats. However, Bisphenols, Parabens, PAHs, Phthalates, PFAS and Metals were detected in infant formula, emphasizing the need for continued monitoring and public health interventions.

The adoption of social media metrics by small and medium-sized enterprises (SMEs) has become a critical tool for engaging customers and enhancing strategic outcomes. However, many SMEs face challenges in effectively tracking and evaluating social media investments, making it difficult to assess the effectiveness of their strategies. This systematic review identifies and analyses key social media activities and performance metrics that contribute to SME strategic success. Through an extensive review of literature from Google Scholar, Web of Science, and Scopus, we selected 138 studies published between 2014 and 2024. The analysis reveals that engagement metrics (e.g., likes, shares), brand awareness (e.g., mentions), and conversion metrics (e.g., click-through rates) significantly influence organizational performance and long-term success. SMEs leveraging detailed engagement metrics reported a 25% increase in customer retention, while brand awareness metrics led to a 30% improvement in customer acquisition. The review highlights areas requiring further research, including a need for sector-specific metric analysis and a deeper understanding of emerging social media platforms. These results offer practical insights for SMEs to enhance their strategic use of social media and adapt to rapidly changing market dynamics.

Accurate forecasting and efficient management of commodities are crucial for stakeholders in the industry, given the volatile nature of these markets. This study addresses these needs by leveraging advanced forecasting techniques and machine learning models to predict prices and enhance supply chain efficiency. TThe focus is on utilizing ARIMA, SARIMAX, and LSTM models to analyze historical trading data for commodities such as cocoa, coffee, and sugar sourced from Kaggle’s comprehensive dataset. The research applies ARIMA and SARIMAX models to forecast price trends, overcoming initial challenges related to data index frequency and seasonality. LSTM models are employed for more nuanced demand forecasting, particularly for random-length lumber, demonstrating the model's capability to predict future market trends accurately. The study highlights significant improvements in prediction accuracy and supply chain management through meticulous feature engineering and model optimization, offering valuable insights for strategic decision-making in the supply chain sector.

The objective of this study was to analyze the epidemiological links of HIV, HCV and HIV-HCV coinfections to less studied types of transmission in certain populations. We performed an observational, prospective study on 903 patients aged between 15-87 years who took part in the Open Test Project. They were divided into two subgroups: general population and individuals from prisons, and questioned about multiple risk factors. Chi-Square Independence Test was used to establish correlations between the risk factors and the results of the screening tests. Logistic regression was used to calculate the probability of a reactive screening test based on each independent risk factor and age. HIV was very strongly associated with unprotected sexual intercourse with HIV-positive partners (the strongest association), unprotected sexual intercourse with sex workers, newly diagnosed sexually transmitted diseases (STDs), intravenous drug use (IDUs) and sharing injecting materials. In the case of HCV reactive tests, very strong associations have been established with injecting drug use (the strongest association), unprotected sexual intercourse with IDUs and sharing injecting materials. Our study indicates the need for implementing targeted public health programs, tailored to the local epidemiology that can ultimately lead to micro-elimination of hepatitis and HIV infections in this area.

A new data processing method based on Machine Learning (ML) algorithms has been developed and tested in clinical environments during COVID-19 trials at the Medical University of South Carolina (MUSC). Breath samples have been collected in 2020–2021 from 100 participants using gas chromatography combined with ion mobility spectrometry (GC-IMS, G.A.S. mbH). Polymerase chain reaction (PCR) tests combined with clinical diagnostics were used as a reference method. Once the ML training-testing cycle is completed, the diagnostics of a single data set may be completed in as little as a few seconds. A convincing difference in the Volatile Organic Compounds (VOS) breath biomarker signature with COVID-19 positive and negative participants has been found. This approach is a generic method that can be applied to data sets collected with other types of hardware, e.g., different settings of GC, GC-MS, GC-GC, or other methods providing high information density in 2-dimensional data sets.

During the development of new medicines mutually exclusive specificity appears to be required but hitherto is most often included at the late clinical trial stage. Here, I describe the use of Hedgehog signalling in Drosophila as a model to represent a mutually exclusive relationship within the context and report the identification of CG43658, a putative Rho guanine nucleotide exchange factor and orthologue of the Charcot-Marie-Tooth disease associated gene ARHGEF10. Furthermore, a Computer that is able to consider the whole and describe the sum of everything in it absolutely in unambiguous terms is a useful tool. Here I describe such a computer with a suggestion for an application to examine any component in the context where a medicine is sought. Taken, together I would like to propose this method as a fundamental necessity and improvement upon current existing methods related not only to Hedgehog signalling but plausibly to life science research and the search for medicines in general.

This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, a novel hybridized architecture, AE-GRU, integrating the encoder-decoder principle with GRU is designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations.

Corrugated board is a composite material characterized by strong orthotropy in both the elastic and plastic phases. Additionally, due to its layered structure, which consists of alternately arranged flat and wavy layers of paper, its mechanics becomes complicated. Therefore, a precisely selected set of laboratory tests is needed to correctly identify all the necessary material parameters of cardboard. The work attempts to define a full set of laboratory tests in order to determine material parameters describing the linear-elastic, orthotropic behavior of cardboard and the basic parameters defining popular plasticity criteria, such as the asymmetric orthotropic Hoffman criterion. For this purpose, sensitivity analysis and numerical models of several selected basic laboratory tests were used. Since the aim of the work is to choose appropriate laboratory tests necessary to correctly determine the material parameters of corrugated board (not its individual layers), the numerical models do not contain detailed cardboard geometry, but only its homogenized representation in the form of the flat shell models. All numerical models are based on the nonlinear finite element method and constitutive modelling in the form of UMAT subroutines implemented in the ABAQUS software. Based on the analyses, appropriate measurements and laboratory tests were indicated that are most sensitive to the material parameters sought.

Precision farming is transforming how antibiotics get used on farms. It fights the overuse that creates superbugs and harms nature. Farmers now have high-tech tools to use antibiotics ultra-precisely. Drones, AI, and advanced watering equipment help them pinpoint only sick plants and animals needing treatment. This shields crops while avoiding environmental damage. More careful antibiotic use makes livestock healthier and boosts harvests too. And it prevents resistant germs, where antibiotics stop working altogether. The approach helps farms in multiple ways, using fewer resources for even better results. There are some growing pains, like high costs for small farms. And questions around data privacy also cause concern. But overcoming these hurdles could help precision farming spread far and wide. This technology promises safer, greener, and more productive agriculture. Targeted use of antibiotics protects both crops and people from drug-proof germs. And nature benefits when less medicine spreads into soil and water. The future looks bright for farming with precision.

Artifact metrics is a technology for authenticating artifacts based on their unique characteristics. The artifact-metric system offers "clone resistance," i.e., it makes it highly improbable to create another object exhibiting the same measured values as a genuine artifact. However, determined adversaries may still attempt to create imitations or clones with close physical characteristics to those of registered products, even if they cannot perfectly replicate the genuine product. Such clones serve to deceive users into believing they are genuine rather than counterfeits. Thus, in this study, we consider a scenario in which we measure raster-scanning-generated clones via a non-raster scanning method. Further, we employ image processing techniques to generate image data representing the clones and theoretically assess the filtering effects on these images. Our findings reveal that applying filters to specific frequency components in the spatial frequency domain can effectively highlight differences between raster-scanning-created clones and the corresponding genuine artifacts. Thus, we demonstrate directional emphasis filtering in artifact metrics as an effective approach for rejecting raster-scanning-created clones.

We discuss three key points: defining mathematically the notions of phase, symmetry breaking, topological defects and their stability in a formal language of homotopy theory and fiber bundle formalism; postulating and proving two theorems for successive symmetry breaking scenarios in condensed matter physics and cosmology, and demonstrating application of theorems to superfluid Helium 3 and Kibble-Lazarides-Shafi (KLS) walls, supporting and mathematically justifying the results of [Volovik, 2020].

This study investigates the application of deep residual networks for predicting the dynamics of interacting three-dimensional rigid bodies. We present a framework combining a 3D physics simulator implemented in C++ with a deep learning model constructed using PyTorch. The simulator generates training data encompassing linear and angular motion, elastic collisions, fluid friction, gravitational effects, and damping. Our deep residual network, consisting of an input layer, multiple residual blocks, and an output layer, is designed to handle the complexities of 3D dynamics. We evaluate the network's performance using a dataset of 10,000 simulated scenarios, each involving 3-5 interacting rigid bodies. The model achieves a mean squared error of 0.015 for position predictions and 0.022 for orientation predictions, representing a 25% improvement over baseline methods. Our results demonstrate the network's ability to capture intricate physical interactions, with particular success in predicting elastic collisions and rotational dynamics. This work significantly contributes to physics-informed machine learning by showcasing the immense potential of deep residual networks in modeling complex 3D physical systems. We discuss our approach's limitations and propose future directions for improving generalization to more diverse object shapes and materials.

Conventional fertilization practices can lead to many ecological problems, such as nutrient imbalance, soil acidity, and reduced soil fertility in natural rubber plantations. To address these challenges, a field investigation was strategically carried out to substitute inorganic fertilizer with organic fertilizers consisted of six treatments, i.e., no fertilization (CK), inorganic fertilizer (NPK), 25 % replacement of inorganic through organic (25%M), 50 % replacement of inorganic through organic (50%M), 75 % replacement of inorganic through organic (75%M), and 100% organic fertilizer (100%M). The soil physicochemical properties (SOC, TN, AN, C:N, pH, NH4+-N, and NO3--N) and carbon and nitrogen converting enzymes (BG, NAG, and LAP) were all determined. The partial substitution of inorganic fertilizers with organic fertilizers (i.e., 75%M at surface soil layer) showed higher SOC (14.52 g.kg-1), TN (1.06 g.kg-1), AN (20.07 mg.kg-1), C:N (14.63), NH4+-N (10.63 mgkg-1), and NO3--N(11.06 mg.kg-1) than NPK and CK. Such increase in physicochemical properties in partial replacement of inorganic with organic fertilizers plots resulted from higher carbon and nitrogen enzymes activities (BG (143.17 nmol.g-1.h-1), NAG (153.96 nmol.g-1.h-1), and LAP (153.48 nmol.g-1.h-1)) compared to NPK and CK, and further the person correlation and RDA analysis confirmed a significant positive correlation between SOC, AN, and soil enzymes. This study presents a new strategy for assessing the impact of partially replacing inorganic fertilizers with organic fertilizers in rubber plantations, mainly by modifying the soil nutrient composition in tropical regions.

In the Northeast of Portugal, like in many parts of the world, most soils are acidic, which may hamper crop productivity. This study presents the findings of a factorial experiment involving three factors: i) soil type [schist (Sch) and granite (Gra)]; ii) cultivars [Cobrançosa (Cob) and Arbequina (Arb)]; and iii) fertilizer treatments [liming (CaCO3) plus Mg (LMg), P application (+P), B application (+B), all fertilizing materials combined (Con+), and an untreated control (Con-)]. Dry matter yield (DMY) did not show significant differences between cultivars, but plants grown in schist soil exhibited significantly higher biomass compared to those in granite soil. Among the treatments, +B and Con+ resulted in the highest DMY (50.8 and 47.2 g pot-1, respectively), followed by +P (34.3 g pot-1) and Con- (28.6 g pot-1). Treatment LMg yielded significantly lower values (15.6 g pot-1) than Con-. LMg raised the pH above 7 (7.36), leading to a severe B deficiency. Although Con+ also raised the pH above 7 (7.48), it ranked among the most productive treatments for providing B. Therefore, when applying lime to B-poor sandy soils, moderate rates are advised to avoid inducing a B deficiency. Additionally, it seems prudent to apply B after lime application.

Soil resilience has become a central theme in research aimed at understanding the impacts of human activities on the environment and mitigating the negative effects of soil disturbance. To evaluate how soil management practices affect soil resilience, 26 small farms were studied in a mountainous district of Lebanon. Farms were categorized into conventional (C), neutral (N), and regenerative (R), based on the practices adopted including tillage, amendments, rotation, cover crops, residues management, and pest control. Common practices included intercropping (85%), residue retention (73%), cover crops (61%), and organic amendments (46%). Qualitative assessment of soil health used indicators from Latin American Society for Agroecology (SOCLA) as well as from ‘Tool for Agroecological Performance Evaluation’ of the FAO. The indicators aligned with the classification of farms into their respective C/N/R groups. The sustainability scores were 4.28 (Low) for conventional, 6.34 (Moderate) for neutral, and 7.88 (Good) for regenerative farms. Quantitative analysis determined for 15 selected farms showed significant differences in soil organic matter (1.86% C, 2.75% N, 3.32% R), soil respiration (156C, 296N, 380R mg C-CO₂. week⁻¹), and earthworm abundance/liter (2.92C, 4.24N, 5.72R). The Soil Quality Index (SQI) provided an accurate representation of the current soil health condition, with increment from 0.05, 0.27 (low), to 0.49 (good) in conventional, neutral, and regenerative farms, respectively. The research highlights that soil resilience is influenced by a combination of intricate factors, encompassing biotic interactions, as well as physical, chemical, and biological processes. Particularly in regions like the Mediterranean basin, adopting sustainable soil management practices contributes to enduring productivity while preserving the functional integrity and resilience characteristics of the soil.

Understanding the soil fertility management practices is indispensable to improve faba bean productivity. However, little effort has been made to assess the soil fertility management practices of faba bean producing farmers of Wolaita Zone, southern Ethiopia. The study was conducted in Damot Gale and Sodo Zuria districts in Wolaita Zone to assess farmers’ soil fertility management practices for faba bean production, in 2019 on 310 framers. Faba bean productivity in the studied districts is majorly constrained by the scarcity of arable land, poor soil fertility, and soil acidity. These cumulative effects have caused negative consequences on soil fertility and faba bean productivity. In most soil fertility, management practices in faba bean farm did not significantly vary among the studied districts. The soil management practices by farmers were inadequate to improve soil fertility and to enhance faba bean productivity. Consequently, the average grain productions of both fertilized and unfertilized faba bean farm were far less than the national average. Therefore, intensive soil fertility management interventions such as faba bean residue management, crop rotation, application of sufficient and balanced fertilizers, adequate lime application, screening acidity tolerant varieties are required to improve faba bean productivity. in the studied districts.

Ephrin type A receptor 2 (EphA2) binds to membrane-bound ligands, ephrin A1, A2, and A5, eliciting bidirectional signaling. This signaling regulates many physiological processes, such as tissue development, homeostasis, and regeneration. The dysregulation of the EphA2-ephrins axis contributes to various diseases, including cancers. The high expression of EphA2 is observed in various cancers, which promotes cancer malignancy, whereas its levels are relatively low in most normal adult tissues. Therefore, EphA2 is a promising target for cancer therapy. In this study, we developed anti-human EphA2 monoclonal antibodies using the Cell-Based Immunization and Screening method. Among them, a clone Ea₂Mab-7 (IgG₁, κ) exhibited a high affinity and sensitivity in flow cytometry. The dissociation constant values of Ea₂Mab-7 for CHO/EphA2 and MDA-MB-231 cells were determined as 7.7 × 10⁻⁹ M and 2.1 × 10⁻⁹ M, respectively. Furthermore, Ea₂Mab-7 can detect endogenous EphA2 in western blot and immunohistochemistry. Therefore, the Ea₂Mab-7 is highly versatile for basic research and is expected to contribute to clinical applications, such as antibody therapy and tumor diagnosis.

A significant challenge encountered in mmWave and Sub-terahertz systems used in 5G and 1 the upcoming 6G networks is the rapid fluctuation in signal quality across various beam directions. Extremely high-frequency waves are highly vulnerable to obstruction, making even slight adjustments in device orientation or the presence of blockers capable of causing substantial fluctuations in link quality along a designated path. This issue poses a major obstacle because numerous applications with low-latency requirements necessitate precise forecasting of network quality from many directions and cells. The method that is proposed in this research demonstrates an avant-garde approach for assessing the quality of multi-directional connections in mmWave systems by utilizing the Liquid Time Constant network (LTC) instead of the conventionally used Long Short-Term Memory (LSTM) technique. The method’s validity was tested through an optimistic simulation involving the monitoring of multi-cell connections at 28 GHz in a scenario where humans and various obstructions were moving arbitrarily. The results with LTC are significantly better than those obtained by conventional approaches such as LSTM. The latter resulted in a test Root Mean Squared Error (RMSE) of 3.44 dB, while the former, 0.25 dB, demonstrating a 13-fold improvement. For better interpretability and to illustrate the complexity of prediction, an approximate mathematical expression is also fitted to the simulated signal data using Symbolic Regression.

Human-elephant conflict has become a conservation challenge in Asia and Africa resulting in considerable human fatalities annually. We deployed an AI-embedded camera-alert system in a conflict hotspot in West Bengal, India to determine if AI algorithms could accurately detect elephants and if real-time alerts that triggered a rapid response could prevent serious incidents. The system successfully detected elephants near villages (266 events) and transmitted real-time alerts (x̄ = 42 seconds) permitting quick response by field personnel to prevent conflict (x̄ = 18.2, SD = 12.4 min to arrival). Matriarch herds of elephants were detected mostly at night when human traffic was minimal; lone adult bulls, the class most prone to conflict, were detected day and night. Frequent detections of humans (33,217 events) on routes used by elephants highlighted the value of real-time monitoring especially during daytime. Twelve human fatalities and eighteen serious injuries occurred in the study area between 2019-2023 but none during deployment of the AI system, supporting the hypothesis that alerts integrated with rapid response can reduce conflict. Loss of lives and livelihoods underscore the urgency of applying this approach to other elephant ranges and adapting AI models to conflict-prone species to provide early warning and promote coexistence.

Sublobar resection is a standard surgical procedure for small-sized non-small cell lung cancer (NSCLC). However, clinical role of adjuvant chemotherapy for small-sized NSCLC with pathological lymph node (LN) metastasis after sublobar resection is unknown. National Cancer Database was queried for NSCLC patients between 2004 and 2018. Eligibility included sublobar resection with pathological LN metastasis, R0 resection, Charlson comorbidity score = 0, clinical stage T1a-b, and tumor size ≤ 20 mm. Kaplan-Meier method with log-rank test and multivariable Cox proportional hazards analyses were used for assessing survival. Samples were evaluated before and after propensity score matching (PSM) with respect to age, sex, histologic type, and pathological LN status. Of 810 patients who met the eligibility criteria, 567 (70.0%) underwent adjuvant chemotherapy. After PSM, patients with adjuvant chemotherapy had a significantly longer survival than those without (median survival: 64.3 vs. 34.0 months, hazard ratio for death: 0.61, p < 0.0001). Multivariate analyses after PSM showed that younger age (p = 0.0206), female (p = 0.0005), and adjuvant chemotherapy (p < 0.0001) were independent prognostic factors for longer survival. Adjuvant chemotherapy has a prognostic impact in patients with small-sized NSCLC and pathological lymph node metastasis who undergo sublobar resection.

According to Young Minds [1] ‘everyone gets angry sometimes’. Their website offers a number of de-escalating strategies, including staying calm, managing responses and setting limits to help young people who most deem resistive. Yet, Young Minds are not alone because such logical advice is ubiquitous in the literature about challenging behaviour despite the fact that in our experience when faced with high states of arousal, most young people tend to act first and deal with the consequences later. It’s not that they are stupid or non-caring, but they are human capable of great feats as well as stupidity. The same is true for any claims that solution focused (SF) conversations can put right the several decades of psychological theory suggesting resistance requires logic and better cognition. By giving the correct thinking skills, young people will walk away from risk, avoid physical confrontation, handle challenging situations like logical thinkers, and generally discount that the process of growing up is part of the challenge. It is with this in mind that over recent years in our current SF practice (with staff group supervision) we have examined the concept of ‘resistance’ and how it can be put to use as a process of collaboration. To do this, we have revisited SF theory that preoccupied many of its pioneers during the 1980’s and 1990’s and attempted to make it useful for the early 2020’s for professionals having conversations with young people, who, in traditional models, are labelled challenging. We want to introduce key differences of solution focused practice and how Anita started to formulate a 6D-SF model (details, dynamics, dimensions, dispositions, dislocations, descriptions) for contemplating how groups of professionals relate to each other and are triggered by challenging behaviour. We do not claim to have proof, logic or exactness on our side, but we’re happy to suggest how our resistance mirrors what many of the staff teams feel and describe when working through their work.

Background: Critical states present scale-free dynamics, optimizing neuronal complexity. We explored electroencephalogram (EEG) criticality in amnesic Mild Cognitive Impairment (aMCI) patients with clinical improvement in working memory, verbal memory, verbal fluency and overall executive functions after the completion of a 6-month prospective memory training. (2) Methods: We compared “before” and “after” stationary resting-state EEG records of right-handed MCI patients (n = 17; 11 females), using the method of critical fluctuations and Haar wavelet analysis. (3) Results: Significant criticality enhancement was present in frontotemporal electrodes [mean dif: 0.10; Z = 7, p = .019] and isolated electrodes T6 [mean dif: 0.204; t (10) = - 2.3, p = .044] and F4 [mean dif: 0.0194; t (10) = - 2.82; p = .018]. (4) Conclusions: EEG criticality agreed with clinical improvement, consisting a possible monitoring biomarker in MCI and Alzheimer’s disease. Further evaluation is needed in patients under cognitive training or even drug-modifying therapies.

This study investigates the potential of natural compounds against HIV-1 protease, a critical enzyme in the replication cycle of human immunodeficiency virus type 1 (HIV-1). HIV-1 protease is essential for cleaving large precursor proteins into smaller functional proteins necessary for viral maturation and infectivity. Protease inhibitors, specifically targeting HIV-1 protease, effectively suppress viral replication, aiding in the management of HIV infection. Using the SWISS DOCK Server, several natural compounds were investigated for their potential as inhibitors of HIV-1 protease. Docking results revealed that Amentoflavone showed excellent binding affinity with high Estimated ΔG values, suggesting its potential as an inhibitor of HIV-1 protease. Additionally, Amentoflavone exhibited similar promising results when tested against Roussarcoma virus protease. These findings indicate the potential of Amentoflavone as a natural compound against HIV and warrant further investigation into its therapeutic applications.

Urbanization influences food culture, particularly in low- and middle-income countries, where there is an increasing consumption of processed and pre-packaged foods. This shift is contributing to a rise in non-communicable diseases. Food labeling standards are crucial for regulating manufacturing practices and aiding consumers in making healthy food choices. We aimed to assess the compliance of local and imported pre-packaged snacks with Tanzanian and international labeling standards in Dar es Salaam, Tanzania. A cross-sectional study was conducted on 180 snack products. A checklist based on Tanzanian and Codex labeling standards was used to evaluate adherence. We also examined factors influencing adherence, such as product origin, price, category, purchase location, and package size. The majority of the snacks demonstrated partial adherence to labeling standards. Imported products showed significantly better full adherence to both Tanzanian and International standards. Notably, over half of the products used English for labeling, and infrequently used the recommended World Health Organization Front-of-Pack nutrition Labeling. Product category, origin, and package size were significantly associated with higher levels of International standard adherence (p<0.05). The inadequate adherence to mandatory labeling standards, scarce use of Swahili and FoPL highlight the need to strengthen labeling practices and potential challenges faced by consumers in understanding nutritional information. Thus, strengthening and emphasizing good labeling practices is of urgency as we seek to address diet-related Non-communicable diseases.

Intelligent ship technology is currently an international research hotspot, and model predictive control is widely used in the path-tracking control of intelligent vehicles. To construct an intelligent ship anti-disturbance path-tracking control method, firstly, an environmental disturbance model was constructed with the actual meteorological data of the target sea area. Secondly, the Fossen ship equation of motion is linearized and discretized as the ship motion model. Thirdly, the expression of the prediction equation is derived from the ship motion model. Fourthly, the cost function is constructed by using the polar diameter and polar angle values of the ship. Fifth, the power function in the cost function is replaced with an exponential function to obtain an improved cost function. Sixthly, according to the Lyapunov theory and the MPC terminal constraint theory, the stability of the improved cost function is verified. Seventh, different test paths are set up, the environmental disturbance model is taken as the external disturbance, the ship motion model, the prediction equation, and the improved cost function are used to design the anti-disturbance path-tracking control algorithm according to the model prediction control idea for simulation experiments. Finally, different MATLAB simulation results show that the improved cost function can resist disturbance of the external wave, current, and wind, and effectively track the target path. Therefore, this study provides a reference for improving the navigation safety of ship path-tracking.

The cultivation systems of Coffea spp in a cooperative society in Puebla, Mexico, include rustic, traditional polyculture, commercial polyculture, unshaded monoculture and shaded monoculture. In this work, the properties of the soil were analyzed through physical, chemical, and biological analysis to determine its nutritional status. Composite sample analyses were conducted to determine physical, chemical and microbiological parameters (fungi, actinomycetes, mesophilic bacteria, nitrifying and denitrifying bacteria). Leaf nutrients were determined. Rustic was the cropping system with the highest amount of K (0.76 cmol kg-1) in soil and nutrient assimilation in leaf (N= 2.79%, P= 660.01, K= 17297.22 and Fe= 271.24 mg kg-1) (p= 0.001); in addition to presenting high populations of mesophilic bacteria, fungi and actinomycetes (30.16, 0.59 and 0.83 respectively, x10 6 CFU g-1 soil) and very low nitrification and denitrification rates. The principal component analysis (PCA) (>3.25%) indicated that actinomycetes and K in soil favor the assimilation of Fe, K and P. This Coffea spp cultivation system had a lower impact on soil health than the rest of the systems and favored forest ecosystem conservation.

: Soil free living nematodes community composition was compared for five agro-management systems in a basaltic soil. The sites differed in their long-term management system and were designated as organic farming (OM), conventional (MI), natural pasture (NP), foliage farming (FF) and open field (OF). The effect of five diverse land use types in vineyard growth in basaltic soils on soil free living nematodes, density, diversity, and functionality in the plant rhizosphere was examined. We found 22 families, 42 genera of which only 14 genera were present in all soil samples, three bacteria feeders, 4 fungi feeders, 3 omnivores-predators and 4 plant parasites, in the soil collected from the five sampling sites. The organic and conventional managements were found to influence soil free living nematode community and its trophic composition. The higher abundance of bacteria feeders included the Cephalobidae and Panagrolaimidae families. The fungi feeders included the Anguinidae and Aphelenchidae families. Ominvores-predators included the Aprocelaimidae and Qudsianematidae, and the plant parasites included the Anguinidae (genus Anguina), Nothotylenchidae and Tylenchidae families. The changes in plant parasite nematodes were one of the main pests in the below-ground rhizosphere biome. Promoting the bacterial community could be a beneficial and promising way for a considerable increase in organic management.

Soil proteases are involved in the transformation of organic matter and thus influence the nutrient turnover in the ecosystem. Phytohormones, similarly to proteases, are synthesized and secreted into the soil by fungi and microorganisms and regulating their activity in the rhizosphere. The aim of our work was to find out how the presence of auxins, cytokinins, ethephone and chlorocholine chloride affects the activity of native soil proteases at the spruce tree stand. Auxins stimulated the native proteolytic activity in the spruce tree stand. Synthetic auxins most stimulated the activity of 2-naphthoxyacetic acid and the naturally occurring auxins of indole-3-acetic acid in the organic horizon of the spruce forest. Cytokinins, ethephone and chlorocholine chloride inhibited the activity of native soil proteases in the spruce tree stand. The highest inhibitory effect was found in ethephone and chlorocholine chloride. Overall, the negative effect of phytohormones on the activity of the native proteolytic activity may slow down the decomposition of organic matter and thus make plant nutrition more difficult. The outcomes of our work assist with understanding of the effect of substances produced by the rhizosphere on the activity of soil microorganisms and the soil nitrogen cycle.

In many soil processes, including solute and gas dynamics, the architecture of intra-aggregate pores is a crucial component. Soil management practices and wetting-drying (W-D) cycles—the latter having a significant impact on pore aggregation—are two key factors that shape pore structure. This study examines the effects of W-D cycles on the architecture of intra-aggregate pores under three different soil management systems: no-tillage (NT), minimum tillage (MT), and conventional tillage (CT). The soil samples were subjected to 0 and 12 W-D cycles, and the resulting pore structures were scanned using X-ray micro-computed tomography, generating reconstructed 3D volumetric data. The analysis was conducted in terms of multifractal spectra, normalized Shannon entropy, lacunarity, porosity, anisotropy, connectivity, and tortuosity. The morphological and geometric properties of the soil pores indicated that W-D cycles did not cause significant changes across the management systems studied. Furthermore, multifractal analysis revealed that the porous systems exhibited fractal behavior rather than multifractality. The results demonstrate that, within the resolution limits of the microtomography analysis, pore architecture remained resilient to changes, despite some observable trends in specific parameters.

In this note we prove that the Riemann hypothesis is false. The proof is by contradiction based on a criterion of Hu.

This study aims to investigate the microphysical properties of Arctic, tropospheric aerosol in the transition from the Arctic Haze in spring towards the summer season in 2021. A special focus lies on the hygroscopicity of the aerosol. Therefore, a one-parameter growth curve model is applied to lidar data from the Koldewey Aerosol Raman Lidar (AWIPEV in Ny-Ålesund, Svalbard) and simultaneous radiosonde measurements. Hygroscopic growth depends on different factors like aerosol diameter and chemical composition. To detangle this dependency, three trends in hygroscopicity are additionally investigated by classifying the aerosol first by its color ratio, and then by its season and altitude. Furthermore, two special days are discussed using Mie-theory. They show on the one side the complexity of analyzing hygroscopic growth by means of lidar data, but on the other side demonstrate that it is in fact measurable with this approach. For these two case studies we calculated, that the aerosol effective radius increased from 0.16 μm (dry) to 0.18 μm (wet), and from 0.28 μm to 0.32 μm for the second case. Generally, we found two different modes of stronger or weaker hygroscopic particles and a complex altitude dependence with the least hygroscopic particles in the middle free troposphere.

Conventional tutoring approaches are confronted with limitations such as restricted availability, inconsistent pedagogical quality, and scalability con-straints. Furthermore, the exclusive use of Large Language Models (LLMs) like ChatGPT in education has its shortcomings, including the potential for incorrect responses and the lack of customized guidance aligned with specific course con-tent. This research proposes an innovative intelligent chatbot tutoring system, in-tegrating the Retrieval Augmented Generation (RAG) approach with a custom LLM. The developed system aims to overcome the limitations of traditional tu-toring and general-purpose LLMs by providing accurate, contextually relevant, and personalized assistance, thereby enhancing student understanding and en-gagement. The system, powered by an intelligent agent, retrieves relevant information from curated academic sources, incorporates interactive features for user feedback, and utilizes machine learning algorithms for ongoing performance enhancement, ensuring a robust and effective tutoring experience. The anticipated outcome is an enriched educational experience for university students, advance-ment in personalized learning, and improved student engagement, retention, and academic performance. Through continuous research and refinement, it is expected that the intelligent chatbot tutor will assume an important role in enhancing and supporting the educational journey of students.

Orificed Hollow Cathodes are electric devices necessary for the functioning of common plasma thrusters for space applications. From their reliability mainly depends the success of a spacecraft’s mission equipped by electric propulsion. The development of plasma models is crucial in the evaluation of plasma properties within the cathodes that are difficult to measure due to the small dimensions. Many models, based on non-linear system of plasma equations, have been proposed in the open literature. These are solved commonly by means of iterative procedures. This paper investigates the possibility to solve them by means of Particle Swarm Optimization method. The results of the validation tests confirm the expected trends for all the unknowns; the confidence bound of the discharge current as function of mass flow rate is very narrow (2÷5V), moreover the results match very well the experimental data except at the lowest mass flow rate (0.08mg/s) and discharge current (1A), where the computations underpredict the discharge current to the utmost by 40%; the highest data dispersion regards the plasma density in the emitter region (± 20 % of the average value) and the wall temperatures (± 50K respect to the average values) of orifice and insert; those of the others variables are very tiny.

The construction sector is a paradigm shift into the sustainable era to solve environmental issues, combat global warming. And the reflection of this is on Green Buildings where energy savings, cost savings, occupant comfort takes priority. The idea of green building efficiency merges sophisticated technologies, creative architectural design templates, and reusable materials into the whole life cycle of a building process in order to render minimum environmental impact. The pilot project of The National Institute of Geophysics and Volcanology (INGV) - Irpinia headquarters, illustrates in this context, innovative technologies of energy transition with a photovoltaic parking with geothermal piles. The project is consistent with the concepts of Nearly Zero Energy Buildings (NZEBs), directed to a high energy performance and very low environmental impact. Incorporating geothermal piles is a major area of advancement in sustainable building ideas that use the Earth's natural heat to create an energy efficient solution in heating and cooling. Project Goals – The project aims to install emission free renewable cleaning energy cost-effective clean production and increase energy efficiency, reduce CO2 emissions and energy security. For this purpose this paper, using as a support the PVGis software, evaluates the potential produced with the photovoltaic system installed in the canopies. With the help of this analysis, it is feasible to determine the amount of electricity produced as well as the system's amortization based on the excess energy sent to the public grid. The project is regarded as creative from the standpoints of energy given the use of geothermal and photovoltaic systems, as well as from an architectural and design perspective due to the use of novel materials. This all-encompassing strategy establishes INGV as a pioneer in the promotion of environmentally friendly technologies and shows a dedication to sustainability and energy efficiency.

We experimentally demonstrate waveguide and microring resonator (MRR) polarizers by integrating 2D graphene oxide (GO) films onto silicon (Si) photonic devices. The 2D GO films with highly anisotropic light absorption are on-chip integrated with precise control over their thicknesses and sizes. Detailed measurements are performed for the fabricated devices with different GO film thicknesses, coating lengths, and Si waveguide widths. The results show that a maximum polarization-dependent loss (PDL) of ~17 dB is achieved for the hybrid waveguides, and the hybrid MRRs achieved a maximum polarization extinction ratio (PER) of ~10 dB. We also characterize the wavelength- and power-dependent response for these polarizers. The former demonstrates a broad operation bandwidth of over ~100 nm, and the latter verifies performance improvement enabled by photo-thermal changes in GO films. By fitting the experimental results with theoretical simulations, we find that the anisotropy in the loss of GO films dominates the polarization selectivity of these devices. These results highlight the strong potential of 2D GO films for realizing high-performance polarization selective devices in Si photonic platform.

Background: Research articles are essential for describing new ideas, techniques, and procedures and consolidating concepts. It creates an opportunity to expose studies and publicly assimilate published knowledge. Therefore, one of them is the most used and accepted index to verify the author’s scientific impact is the H-index or Hirsch index. It provides a robust single metric that combines quality and quantity. Therefore, it presents disadvantages, (1) researchers with shorter scientific carriers may have fewer articles and citations than those with longer scientific life; (2) it allows scientists to receive citations even if no new papers are published; (3) it is only useful for comparing the better scientists in a field; (4) this indicator can never decrease; (5) it is only weakly sensitive to the number of citations received; (6) the major and minor (or no) contributor in the research gained equal h-index. Then, the H-index ignores the total of citations and the time of a researcher's experience (“scientific age” [sa]). Objective: Thus, the goal of this study was to present a new index system (GFsa index ©) based on the two variables completely forgotten (total of citations and “scientific age”), introducing a new terminology “scientific age”, to evaluate the best-ranked researchers in Dentistry. Methods: The AD Scientific Index was used, and all researchers included in this study were cited in the AD Scientific Index (2024) in the Dentistry field and had the Google Scholar page accessible for a consultation to manually obtain the total of citations and calculate the “scientific age”. The information was retrieved by two authors (G.V.O.F. and J.C.H.F.), who assessed the data twice to avoid errors or mistakes. For inclusion: a minimum H-index value of 30; for exclusion: (i) the author did not have a Google Scholar page; (ii) duplicated author; or (iii) presented any suspicious activities about manipulation of articles. Then, a dataset of all researchers who were reported in Dentistry was prepared (name, H-index, i10 index, and publications). The formula developed and applied was GFsa=(total number of citations)/(“scientific age)². The Pearson correlation statistically evaluated the data obtained; the confidence interval was 95%. Results: A total of 9,093 researchers in Dentistry were recorded, of whom 1,020 were included. Then, we collected all 1,020 researchers' Google Scholar data (publications, citations, and indexes), and each researcher's total citations and “scientific age” were registered and calculated. The mean “scientific age” registered was 34.18 ± 13.34 (standard error of mean = 0.4181). With this information and data collected, GFsa index © was calculated, presenting a minimum value of 0.2186 and a maximum of 154.8 (mean = 10.33 ± 11.82; standard error of mean = 0.3706). Then, the data was organized and sorted following the ranking obtained in the GFsa index ©. The Pearson correlation showed that the H-index had a weakly positive association with the researcher’s “scientific age”; this fact means that the H-index will increase according to the “scientific age” increase. Otherwise, a moderately negative correlation between GFsa and “scientific age” was demonstrated, whereby a higher GFsa © had a lower “Scientific age”. This assessment shows that the variables introduced were fairly used. Moreover, a positive correlation was observed between both indexes (H-index and GFsa). Conclusion: It is possible to conclude that the new variable reported (“Scientific age”) and using the total citations brought a fairer and more feasible evaluation among the researchers. Thus, we are launching the new GFsa index © and the new information named “Scientific age” for the first time in the literature. The future perspective of the GFsa © application will be (1) the evaluation of the 9,093 researchers in Dentistry (total initially registered), including more authors/researchers in the Dentistry field, (2) expanding the exposition of this new index for other areas of the knowledge, and (3) provide free access of all data through the website (http://gfindex.us.to).

Domestic crime, conflict, and instability pose a significant threat to many contemporary governments. These challenges have proven to be particularly acute within modern-day Mexico. While there have been significant developments in predicting intrastate armed and electoral conflict in various contemporary settings, such efforts have thus far been limited in their use of spatial as well as temporal correlations, as well as in the features they have consid- ered. Machine learning, especially deep learning, has been proven to be highly effective in predicting future conflicts using word embeddings in Convolutional Neural Networks (CNN) but lacks the spatial structure and, due to the black box nature, cannot explain the importance of predictors. We develop a novel methodology using machine learning that can accurately classify future anti-government violence in Mexico. We further demonstrate that our approach can identify important leading predictors of such violence. This can help policymakers make informed decisions and can also help governments and NGOs better allocate security and humanitarian resources, which could prove beneficial in tackling this problem. Using a variety of political event aggregations from the ICEWS database alongside other textual and demographic features, we trained various classical machine learning algorithms, including but not limited to Logistic Regression, Random Forest, XGBoost, and a Voting classifier. We considered a mix of CNN + Long Short Term Memory (LSTM) networks to decode the spatial and temporal relationship in the data. The performance of all the black box deep learning models was not at par with the classical machine learning models. We also evaluated the temporal relationships in the data to identify important predictors that fuel anti-government violence along with their directions using Shapley additive values. The voting classifier, utilizing a subset of features derived from LASSO across 100 simulations, consistently surpasses alternative models in performance and demonstrates efficacy in accu- rately classifying future anti-government conflicts. Notably, Random Forest feature importance indicates that some features, including but not limited to homicides, accidents, material conflicts, and positive worded citizen information sentiments emerge as pivotal predictors in the classification of anti-government conflicts. Finally, we conclude the research by analysing the spatial structure of the data using Moran’s I index extended version for spatiotemporal data to identify the global spatial dependency and local clusters followed by modelling the data spatially and evaluating the same using Gaussian Process Boost(GPBoost). The global spatial autocorrelation is minimal, characterized by local- ized conflicts cluster within the region. Furthermore, in Phase 3, the Voting Classifier method demonstrates superior performance over GPBoost, leading to the inference that no substantial spatial dependency exists among the various locations.

This study investigated the relationship between aluminum adjuvant vaccines and the prevalence of 4 childhood diseases (Autism, Allergies, Asthma, ADD/ADHD) using data from the 2020-2021 National Survey of Children's Health (NSCH) [1] and the Centers for Disease Control National Immunization Survey (CDC NIS) [2]. The study found an association with aluminum adjuvant dosage and the prevalence of each disease studied (Autism, Allergies, Asthma, ADD/ADHD) through a weighted regression analysis of disease prevalence against the summed likelihood of aluminum adjuvant vaccine uptake.

This paper presents the perspective that universe is relative. The universe, which changes according to the traveler’s proper time, is dynamic. While the universe generally expands, it could also contract if traveler accelerates sufficiently. The Hubble parameter merely represents that change. If the Hubble parameter is dependent on the proper time, the measurement can only be uncertain. Identifying the factors that influence the proper time could potentially lead to a more accurate measurement of the universe’s age, and it could also offer insights into the state of the early universe and the causes of the accelerated expansion observed in the current epoch.

Social media platforms have become pivotal tools for small and medium-sized enterprises (SMEs), offering vast opportunities for enhanced organizational engagement. However, these platforms also present challenges such as data privacy concerns, feedback management, and content saturation. This systematic review critically evaluates the existing literature on the dual role of social media in fostering organizational engagement while addressing key barriers faced by SMEs. We systematically assessed 104 peer-reviewed articles sourced from Scopus, Web of Science, and Google Scholar, focusing on the impact of social media on marketing strategies and organizational outcomes in SMEs. The Newcastle-Ottawa Scale was used for quality assessment, and effect measures, including mean difference and odds ratio, were employed to evaluate performance metrics such as customer engagement, business performance, and long-term organizational impact. Key findings indicate that 74% of SMEs reported improvements in brand visibility, with customer engagement increasing by 65%. However, significant concerns were identified, with 45% of studies highlighting privacy issues and 52% addressing challenges in managing negative feedback. The review emphasizes that while social media can enhance market reach and customer interaction, its effectiveness largely depends on strategic content management and planning. This review provides actionable insights for SMEs aiming to optimize social media use, highlighting the need for future research to address privacy management and feedback strategies for sustained success.

The critical role of manufacturing operations in enhancing the performance of Small and Medium Enterprises (SMEs) has become increasingly evident in recent years. This systematic review examines how manufacturing operations influence the performance of SMEs, analysing findings from 69 diverse research papers published between 2014 and 2024. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review focuses on key operational strategies, including lean manufacturing and advanced manufacturing technologies, assessing their effectiveness in improving business outcomes such as efficiency, productivity, and financial performance. The methodology included a thorough review of the research methods used in the studies, identifying potential biases, gaps, and highlighting the need for diverse approaches. While manufacturing operations have led to improved productivity and increased efficiency for many SMEs, challenges such as limited resources, lack of technical expertise, and resistance to change remain prevalent. Notably, a reporting bias was observed, with 48% of the literature composed of quantitative studies, 30% being mixed-method studies, and only 20% and 1% representing qualitative and exploratory studies, respectively. This imbalance suggests an overemphasis on quantitative methods, limiting the range of ideas and understanding available. Key strategies such as lean manufacturing and advanced technologies were shown to enhance productivity by 15% to 25% in SMEs. By adopting a broad approach, this review ensures that the findings are applicable across various sectors and geographies, offering valuable insights into best practices and challenges across diverse economic contexts. The broad scope also allows for the identification of under-researched areas, fostering a more comprehensive understanding of how manufacturing operations contribute to SME success. The review provides a decision-making framework for business leaders and policy recommendations to foster SME growth in a competitive, globalized market.

A fundamental aspect in the evolution of Time-to-Digital Converters (TDCs) implemented within Field-Programmable Gate Arrays (FPGAs), given the increasing demand for detection channels, is the optimization of resource utilization. This study reviews the principal methodologies employed for implementing low-resource TDCs in FPGAs. It outlines the foundational architectures and interpolation techniques utilized to bolster TDC performances without unduly burdening resource consumption. Low-resource Tapped Delay Line, Vernier Ring Oscillator, and Multi-Phase Shift Counter TDCs, including the use of SerDes, are reviewed. Additionally, novel low-resource architectures are scrutinized, including Counter Gray Oscillator TDCs and interpolation expansions using Voltage-Temperature-Consumption stable IODELAYS. Furthermore, the advantages and limitations of each approach are critically assessed, with particular emphasis on resolution, precision, non-linearities, and, especially, resource utilization. A comprehensive summary table encapsulating existing works on low-resource TDCs is provided, offering a comprehensive overview of the advancements in the field.

Immune-cell reprogramming driven by mitochondria-derived reactive electrophilic immunometabolites (mt-REMs—e.g., fumarate, itaconate) is an emerging phenomenon of major biomedical importance. Despite their localized production, mt-REMs elicit significantly-large local and global footprints within and across cells, through mechanisms involving electrophile signaling. Burgeoning efforts are being put into profiling mt-REMs’ potential protein-targets and phenotypic mapping of their multifaceted inflammatory behaviors. Yet, precision indexing of mt-REMs’ first-responders with spatiotemporal intelligence and locale-specific function assignments remains elusive. Highlighting the latest advances and overarching challenges, this perspective aims to stimulate thoughts and spur interdisciplinary innovations to address these unmet chemical-biotechnological needs at therapeutic immuno-signaling frontiers.

Background: Chronic Myeloid Leukemia (CML) is initiated in the bone marrow due to the chromosomal translocation t(9;22), resulting in the fusion oncogene BCR-ABL. Tyrosine kinase inhibitors (TKIs) targeting BCR-ABL have transformed fatal CML into an almost curable disease. However, TKIs lose efficacy during disease progression, and the mechanism of CML progression remains to be fully understood. Additionally, common molecular biomarkers for CML progression are lacking. Our studies previously detected ANKRD36 (c.1183_1184 delGC and c.1187_1188 dupTT) associated exclusively with advanced phase CML. However, clinical validation of this finding was pending. Therefore, this study aimed to clinically validate mutated ANKRD36 as a novel biomarker of CML progression. Materials and Methods: The study enrolled 124 patients in all phases of CML, recruited from Mayo Hospital and Hameed Latif Hospital in Lahore, Punjab, between January 2020 and September 2023. All response criteria were adopted from the European LeukemiaNet guideline 2020. Informed consent was obtained from all study subjects. The study was approved byscientific and ethical review committees of all participating centers. Sanger sequencing was employed to detect ANKRD36mutations in CML patients in accelerated phase (AP) (n=11) and blast crisis (BC) (n=10), with chronic-phase CML (CP-CML) patients as controls (n=103). Samples were processed using Big Dye Terminator Cycle Sequencing Ready Reaction kits and sequenced using ABI Prism 3730 Genetic Analyzer, and sequencing using forward and reverse primers for ANKRD36.Results: During our study, 17% of CML patients progressed to advanced phases AP-CML n= 11 (8.9%) and BC-CML n=10(8.1%). The chronic- and advanced-phase patients showed significant difference with respect to male-to-female ratio, hemoglobin level, WBC count, and platelet count. Sanger sequencing detected ANKRD36 mutations c.1183_1184 delGC and c.1187_1185 dupTT exclusively in all AP- and BC-CML patients but in none of the CP-CML patients. Nevertheless, mutations status was not associated with male-to-female ratio, hemoglobin level, WBC count, and platelet count, which makes ANKRD32 as an independent predictor of early and terminal disease progression in CML. Conclusions: The study confirms ANKRD36 as a novelgenomic biomarker for early and late CML progression. Further prospective studies should be carried out in this regard. ANKRD36, although fully uncharacterized in humans, shows the highest expression in bone marrow, particularly myeloid cells. Functional integrated genomic studies are recommended to further explore the role of ANKRD36 in the biology and pathogenesis of CML.

The automotive industry is continuously developing to meet the needs of users in terms of efficiency and convenience. The Thailand government has set a policy for Thailand to be a hub for the production of electric vehicles and key components in the region, with a target to produce electric and zero-emission vehicles by 2030. This article is an example of communication in an electric vehicle system. The CAN Bus system is a communication system in electric vehicles that can receive and send communication data via CAN-Hight/Low lines designed for vehicles. CAN Bus is a standard communication system supported by global organizations and used in modern automotive systems. The design of the communication system in electric vehicles uses MRS Developers Studio software to help write programs to receive and send data and prioritize the importance of the received data. The system consists of communication examples studied, such as pressing the start button, turning on/off the headlights, turning on/off the turn signal, turning on/off the brake light, turning on/off the emergency light, sending the horn signal, sending the signal to shift forward/backward, and tuning the motor gear system. From the communication examples mentioned, the communication will be a prototype transport set for small electric vehicles under the scope of the lithium battery energy storage system, NMC type, size 72 V 25 Ah, to test the use, efficiency, and stability in CAN Bus communication up to 1000 times.

In road construction, before applying an asphalt or concrete surface, the ground must be compacted and stabilized. There are two basic methods of soil stabilization: in situ and in a stationary node (ex-situ). The method of performing stabilization in place (in-situ) is the most frequently used method due to its convenience and lower price. The most popular type of binder for stabilization is a hydraulic binder (most often cement and various ashes). Such stabilization is performed at a depth of 10-50 cm, achieving the desired load-bearing parameters. In order to improve them, various chemical additives for stabilization are often used, such as ion exchange compounds, additives based on sulfuric acid, additives based on vinyl polymers or even organic additives using lignosulfonates. However, the use of such additives is associated with much greater costs and environmental burden, resulting in seeking for cheaper and equally effective alternatives. The win-win situation would be for instance recycling the problematic waste-based materials that on one hand are landfilled or impossible to recycle and on the other hand cause problems for the waste producers. Therefore, an interesting issue is the production of stabilization additives from various types of waste materials. As a result of the extensive testing of various waste-based materials blends with soil, the mechanical (compressive strength after 7 and 28 days) and hydraulic (capillary rise, water absorption, frost resistance) soil properties were measured. The optimization process led to obtaining additives compositions ensuring the best strengthening and sealing properties. These were for sandy soil: Pure foil (wax emulsion), Pure foil (wax emulsion) + waste sulphuric acid, RDF from waste tires (wax emulsion), Pure foil (wax emulsion) + “by-pass” waste ash + NaOHx2 and for clayey soil: Pure foil (wax emulsion) + NaOH, Pure foil (wax emulsion) + waste sulphuric acid, Tequant, Pure foil (wax emulsion).

Soil water content (SWC) and soil organic carbon (SOC) are two important factors to consider when revegetation in degraded land of arid and semiarid areas. Knowing the response of SWC and SOC to vegetation restoration as well as their coupling interaction is important for sustainability of vegetation restoration. In this study, three common plantations of the same recovery years were selected on China’s Loess Plateau, which included Pinus sylvestris, Pinus tabuliformis and Populus simonii, to evaluate the response of SWC and SOC to vegetation restoration as well as their interactions, and then revealed the influencing factors of SWC and SOC variations. Results showed that compared to the grassland, three plantations all exacerbated the deficit of deep soil water, but Populus simonii increased the SOC, while the rest all decreased the SOC. And tradeoff of Populus simonii had the lowest root mean squared error (RMSD). Therefore, Populus simonii was identified as representing a suitable revegetation technique for this region. There was strong interaction between SWC and SOC for shallow soil layers. Land use type (LUT) has an important effect on deep SOC variations, while SOC and soil texture for deep SWC variations. The results were helpful to the sustainable development of artificial forests in this region as well as other similar arid and semiarid areas.

Minirhizotron tubes were installed to monitor root growth dynamics of mature Shiraz grapevines in a rootstock trial established in the hot climate Riverina region of New South Wales, Australia. The vertical root distribution and seasonal root growth dynamics of Shiraz on own-roots and Shiraz grafted on the rootstocks Ramsey, 140 Ruggeri and Schwarzmann was studied for five seasons across a seven-year period to a depth of 60 cm. New root production was significantly influenced by genotype, soil depth, season, growth stage and year. Soil moisture and soil tem-perature were monitored at 10, 30 and 60 cm in the last two seasons. Soil moisture at 30 cm and soil temperature at all three depths were significant predictors of root growth. New root numbers were significantly higher in 140 Ruggeri than the other rootstocks. To the depth studied, 140 Ruggeri roots were evenly distributed from the topsoil down, whereas the majority of roots of Schwarzmann and Shiraz were located at intermediate depths in the 10-40 cm ad 20-40 cm zones respectively, while Ramsey roots were found at 20 cm or below. Depending on genotype, root growth occurred across several phenological stages but tended to peak at flowering. In some years we observed root growth in early and late winter at rates exceeding that of autumn, and this was associated with warmer temperatures during this period. Overall, seasonal rooting dynamics were responsive to abiotic factors but dominated by genotype.

The national-scale evaluation and modelling of the impact of agricultural management and cli-mate change on soils, crop growth, and the environment require soil information at a spatial res-olution addressing individual agricultural fields. This manuscript presents a data science ap-proach which agglomerates the soil parameter space into a limited number of functional soil pro-cess units (SPUs) which may be used to run agricultural process models. In fact, two unsupervised classification methods were developed to generate a multivariate 3D data product consisting of SPUs, each being defined by a multivariate parameter distribution along the depth profile from 0 to 100 cm. The two methods account for differences in variable types and distributions and in-volve genetic algorithm optimization to identify those SPUs with the lowest internal variability and maximum inter-unit difference with regards to both, their soil characteristics and landscape setting. The high potential of the methods was demonstrated by applying them to the agricultural German soil landscape. The resulting data product consists of twenty SPUs. It has a 100 m raster resolution in the 2D mapping space, and its resolution along the depth profile is 1 cm. It includes the soil properties texture, stone content, bulk density, hydromorphic properties, total organic carbon content, and pH.

There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at field scale for food security, monitoring long-term changes related to soil health and climate change. In Germany, large-scale SOC maps are mostly available with a spatial resolution of 250 m to 1 km2. The nationwide availability of both digital elevation models at various spatial resolutions and multi-temporal satellite imagery enables the derivation of multi-scale terrain attributes and Landsat-based multi-temporal soil reflectance composites (SRC) as explanatory variables. On the example of an Bavarian test of about 8000 km2, the scale-specific dependencies between the representativeness of 220 soil samples and different aggregation levels of the explanatory variables were analyzed for their scale-specific predictive power. The aggregation levels were generated by applying a region-growing segmentation procedure, the SOC content prediction was realized by the Random Forest algorithm. In doing so, established approaches of (geographic) object-based image analysis (GEOBIA) and machine learning were combined. The modeling results revealed scale-specific differences. Compared to terrain attributes, the use of SRC parameters lead to a significant model improvement at large field-related scale levels. The joint use of both terrain attributes and SRC parameters resulted in further model improvements. The best modeling variant is characterized by an accuracy of R2=0.84 and RMSE=1.99.

The integration of Customer Relationship Management (CRM) systems within Small and Medium Enterprises (SMEs) is increasingly recognized for its potential to enhance business performance. While CRM adoption is on the rise, the specific impacts on operational efficiency, customer satisfaction, and financial outcomes in SMEs require further investigation. This systematic review evaluates the impact of CRM systems on SME performance, with a focus on operational improvements, sales growth, and customer retention. The study aims to identify key determinants of successful CRM implementation and assess the long-term effects on SMEs' competitive advantage. The review encompasses studies published between 2014 and 2024, sourced from Google Scholar, Web of Science, and SCOPUS. A total of 46 studies were included after screening 18,306 records. The mixed-methods approach involves quantitative analysis of survey data from SME owners and managers, along with qualitative synthesis of case studies. Metrics evaluated include customer acquisition, retention rates, revenue growth, and organizational efficiency. The analysis indicates that CRM adoption leads to a 25-40% improvement in customer retention and a 15-30% increase in sales across SMEs. Operational efficiency gains range from 20-35%, primarily driven by process automation and enhanced data management capabilities. Successful CRM implementation is strongly linked to managerial support, system customization, and user training. However, barriers such as limited financial resources and technical expertise hinder the full realization of CRM benefits. CRM systems significantly contribute to SME growth by optimizing customer relationships and enabling data-driven decision-making. The review underscores the importance of strategic CRM implementation tailored to the specific needs of SMEs to maximize its impact. Future research should focus on overcoming adoption barriers and refining CRM strategies for sustained performance improvements.

Mutations in p53 and KRAS are seen in most cases of colon cancer. The impact of these mutations on signaling pathways related to cancer growth has been studied in depth, but relatively less is known on their effects on amino acid transporters in cancer cells. This represents a significant knowledge gap because amino acid nutrition in cancer cells profoundly influences macropinocytosis and ferroptosis, two processes with opposing effects on tumor growth. Here we used isogenic colon cancer cell lines to investigate the effects of p53 loss and KRAS activation on two amino acid transporters relevant to macropinocytosis (SLC38A5) and ferroptosis (SLC7A11). Our studies show that the predominant effect of p53 loss is to induce SLC7A11 with resultant potentiation of antioxidant machinery and protection of cancer cells from ferroptosis whereas KRAS activation induces not only SLC7A11 but also SLC38A5, thus offering protection from ferroptosis as well as improving amino acid nutrition in cancer cells via accelerated macropinocytosis. Niclosamide, an FDA-approved anti-helminthic, blocks the functions of SLC7A11 and SLC38A5, thus inducing ferroptosis and suppressing macropinocytosis with resultant effective reversal of tumor-promoting actions of oncogenic mutations in p53 and KRAS. These findings underscore the potential of this drug in colon cancer treatment.

A novel concept of ion separation Anisotropic Diffusion Mobility Analyser (ADMA) with Individual Ion Counter (IIC) was shown by numerical modelling to be able to separate ions with high resolution and ultra-high sensitivity. An ADMA-IIC device has been developed and tested at sheath flow rates below 40 l/min with a number of chemicals relevant to airport security and military applications. The resolving power of the ADMA was observed to be in the range of 40 to 80. These data were close to the resolution obtained in numerical modelling predictions. It is greater than or comparable with the RP of currently available on-the-market devices for airport security and other applications. It was also confirmed that the ADMA technology enables very fast detection of analytes, at least 100 times faster than ion mobility spectrometers. It was shown that Brownian diffusion can be controlled and even overpowered by generating highly anisotropic diffusion in the ADMA device. The ultimate sensitivity of single-ion detection below the ppq range has been demonstrated.

The fractal dimension of is identified in this paper. More importantly, the undertaken research has shown how affects the Koch snowflake . The study has demonstrated the existence of negative values of (Koch snowflake), which connects to a considerable number of extremely intriguing mathematical and physical research areas. The information-theoretic impact on the Sierpiniski Gasket dimension , which corresponds to , was treated in a more fundamental way. Notably, the credibility of this current study adds to the existing kThe fractal dimension of is identified in this paper. More importantly, the undertaken research has shown how affects the Koch snowflake . The study has demonstrated the existence of negative values of (Koch snowflake), which connects to a considerable number of extremely intriguing mathematical and physical research areas. The information-theoretic impact on the Sierpiniski Gasket dimension , which corresponds to , was treated in a more fundamental way. Notably, the credibility of this current study adds to the existing knowledge for the information- theoretic fractal dimensions. On another applicative note, this paper offers some potential applications of fractal dimension to smart cities. The investigation of the threshold theorems for and in terms of the triad is part of the next study phase. Additionally, investigating the newly obtained 's corresponding arbitrary Sierpinski Gasket for the first time.applicative note, this paper offers some potential applications of fractal dimension to smart cities. The investigation of the threshold theorems for and in terms of the triad is part of the next study phase. Additionally, investigating the newly obtained 's corresponding arbitrary Sierpinski Gasket for the first time.

Amidst the challenges posed by the COVID-19 crisis, marked variations in individuals' resilience and vulnerability have emerged. This eight-month longitudinal study engaged 535 community adults (58% female, M_age = 43.97) to explore the nuanced aspects of coping and personal growth during the challenging period. Grounded in Self-Determination Theory, the research examines the influence of autonomy support from close others—manifested through active listening and providing choices—on psychological need satisfaction (i.e., autonomy, competence, and relatedness), integrative regulation of emotions; posttraumatic growth; and subjective well-being (positive affect and life satisfaction). Structural equation modeling revealed that, over time, the experience of psychological need satisfaction was intricately related to integrative regulation, posttraumatic growth, positive affect, and life satisfaction. Notably, the impact was partially mediated by autonomy support. These findings shed light on the pivotal role that autonomy supported relationships play in fostering personal growth and meaning making during life's difficult junctures. The study underscores the practical significance of purposeful support from close others and paves the way for future research endeavors.

Analysis of complex DNA mixtures is limited by the selected genetic markers characterized and assay technology. Small panels of short tandem repeats (STRs) are the current standards in most countries. Extension of STRs with hundreds of single nucleotide polymorphisms (SNPs) is now commercially available. The current set of extension SNPs focus on phenotypic and biogeographic ancestry informative SNPs. Larger panels with thousands of SNPs have been developed that can identify up to 10 contributors to forensic DNA samples. Herein, multiple sets of SNPs were selected with the focus of increasing the upper limit of detectable contributors. Larger SNP panel sets with lower minor allele frequency (MAF) alleles can detect large numbers of contributors to in silico mixtures of randomly selected profiles from the 1,000 Genomes project. These results provide a path to enable characterization of complex DNA mixtures that are currently impossible to analyse.

AbstractThe escalating impact of artificial intelligence (AI) has birthed the fascinating technology of deepfakes, capable of meticulously crafting hyper-realistic audio-visual manipulations. Within the realm of healthcare, this technology presents a double-edged sword, brimming with potential for revolutionizing various facets of care delivery while simultaneously harboring significant ethical and practical challenges. This research delves into the complex narrative surrounding AI deepfakes in healthcare, meticulously analyzing both the exciting opportunities they offer, and the inherent risks associated with their implementation.Background:Traditional healthcare practices face limitations in patient engagement, accessibility, and training efficacy. Deepfakes, with their ability to simulate real-world scenarios and interactions, emerge as a potential solution, offering personalized patient education, immersive mental health interventions, remote consultations with virtual avatars, and realistic training simulations for medical professionals.Objective:This research aims to comprehensively understand the multifaceted impact of AI deepfakes in healthcare systems. It seeks to: Identify and analyze the potential benefits of deepfakes in specific healthcare applications. Assess the ethical and practical challenges associated with deepfake implementation in healthcare settings. Propose recommendations for safe, responsible, and ethical integration of deepfakes within the healthcare system.Results/Conclusion:The research findings illuminate a landscape replete with opportunities and challenges. Deepfakes offer the potential to enhance patient education, personalize mental health interventions, facilitate remote consultations, and revolutionize medical training. However, concerns regarding misinformation, ethical breaches, data privacy, and technical limitations pose significant challenges.The conclusion emphasizes the need for a balanced approach that capitalizes on the innovative potential of deepfakes while mitigating potential harm. Collaborative efforts involving healthcare professionals, technologists, and policymakers are crucial for establishing ethical frameworks, ensuring responsible development, and harnessing the positive power of deepfakes to build a future where healthcare benefits from cutting-edge technology while prioritizing patient well-being and ethical principles.

This paper offers a novel geometric and visual approach to the renowned Four Color Map Theorem and K5 non-planarity problem while unveiling their profound connection to the "kissing number" problem in 2D. We represent planar graphs through circles and tangents, simplifying complex structures and shedding light on these classic problems. Our proofs by contradiction, rooted in the kissing number concept, reveal that both the Four Color Map Theorem and K5 non-planarity are fundamentally linked, as they pivot around the concept of coloring. This study bridges the realms of geometry and graph theory, providing fresh insights and emphasizing the significance of the kissing number problem in various fields.

We investigate some information properties of consecutive k-out-of-n:G systems in light of fractional generalized cumulative residual entropy. We firstly derive a formula to compute fractional generalized cumulative residual entropy related to the system's lifetime and explore its preservation properties in terms of established stochastic orders. Additionally, we obtain useful bounds. To aid practical applications, we propose two non parametric estimators for the fractional generalized cumulative residual entropy in these systems. The efficiency and performance of these estimators are illustrated using simulated and real datasets.

This review is a first attempt at bringing together various concepts from research on wall- and magnetically-bounded turbulent flows. Brief reviews of both fields are provided: The main similarities identified are coherent (turbulent) structures, flow generation and transport barriers. Examples are provided and discussed.

This study examines the Soil Organic Carbon (SOC) within Greek croplands, offering a comprehensive understanding of its dynamics. SOC, a cornerstone in soil health, nutrient cycling, and global carbon dynamics, assumes critical significance in sustainable agriculture and climate change mitigation. Drawing on diverse soil properties, including pH, soil texture as well as different drainage and slope categories, the research explores the nuanced relationships shaping SOC dynamics in the diverse agroecological landscape of Greece. The investigation transcends local boundaries, emphasizing SOC's global role in climate change mitigation by sequestering carbon dioxide. Two maps were used as data sources: 1) SOC stock baseline map (2010) by JRC, 2) SOC stock map (2021) by the Institute of Soil and Water Resources, Hellenic Agricultural Organization – DIMITRA in collaboration with FAO. Greek croplands emerge as a mosaic of agroecological diversity, where anthropogenic activities wield transformative influences on SOC stock, demanding a delicate balance between agricultural productivity and soil health. The study unveils the influence of Soil Order, weaving a tapestry of SOC variability. Microscopic factors, from soil texture to cation exchange capacity, further shape SOC dynamics, emphasizing the role of clayey soils and coarse materials in carbon retention. Although soil organic carbon decreased from 2010 to 2021 the degree of carbon loss varied. This scientific endeavor synthesizes existing knowledge and unveils novel insights, offering a narrative woven from diverse soil properties. The findings contribute valuable insights for sustainable land management practices and climate change mitigation strategies, underscoring the importance of region-specific approaches in addressing global challenges.

Soil erosion is now almost universally recognized as a serious threat to man's well-being, if not his very existence. As a result, we assessed the soil physicochemical properties of two possible levels of soil bund and fanya juu. RCBD with three replications was used to collect soil samples from each soil conservation structure. Five composite soil samples were collected from each soil structure based on slop (0-30cm). Soil physicochemical properties such as erosion index, dispersion ratio, and erodibility proportionality ratio were investigated. The effect of different soil structure levels revealed that soil properties differed significantly (P≤0.05) for all parameters studied. The control plots had significantly higher (P≤0.05) dispersion ratio, erosion indexes, and erodibility proportionality than the soils treated by the level bund and level Fanya juu structures. On the control plot, this result showed lower clay content and higher sand content. The level of soil bund and fanya juu had a significant (P≤0.05) effect on soil OC, CEC, OM, and TN, as well as available phosphorous and potassium. As a result, all related soil properties show a positive relative change when the level of soil bund and fanya juu is compared to the control plot. Aside from this result, the dynamic natures of the sciences compel us to conduct additional research based on the agro-ecological zones of the study area.

One of the most important parameters that characterize the traction-coupling properties of a wheeled tractor is its slip. The more tractor's gross traction, the higher its traction-coupling properties. But, this gross traction should not exceed its maximum possible value, which, in turn out, is to be determined by the maximum permissible slip. This article provides the equation to calculate this crucial parameter and establishes the dependencies between the tractor's slip and soil structure coefficient. It was shown that the value basically depends on such soil characteristics as the bulk deformation coefficient and the coefficient of rolling resistance. Calculations showed that for the average value of the soil bulk deformation coefficient at, the average value of rolling resistance coefficient at 0.16, the ratio value of the maximum permissible soil pressure to the tractor wheel rolling radius at the maximum permitted amount slip of the tractor wheels should not exceed 15%. With more slip, the soil structure deteriorates significantly. In this case, its structure coefficient may be less than critical, equal to 0.4.

Cadmium (Cd) contaminated soil is a serious environmental problem due to its effect on crop production, soil microbial community, and risk to human health. The commercial amendments are widely used in the immobilization of heavy metal-contaminated soil because of their cost-effectiveness, eco-friendliness, and community acceptance. This study is conducted to assess the effect of sole commercial amendments including bentonite (B), talc (T), activated carbon (AC), cornstarch (CS), and their combination on exchangeable Cd content in soil, Cd uptake by the plant, and soil microbial community. The results indicated that CS and its composites (T+CS and B+CS) showed the highest effect on reducing Cd mobility in soil (up to 10% in CS), decreasing the Cd uptake by lettuce (up to 78% in T+CS), and increasing the microbial community in soil compared to the control. Furthermore, results from Biolog Ecoplate and realtime-Polymerase chain reaction indicated that the mixture amendments (B+CS and T+CS) showed the highest effect on the microbial community by increasing soil microbial population maximum of 3.81-fold difference (in B+CS) compared to the control. The results indicated that applying composite amendments provided an efficient option for immobilizing Cd in soil, reducing the Cd uptake by plants, and increasing microbial community.

In this study the effects of three different main preparatory tillage operations [ploughing at 0.4 m (P40) and 0.20 m (P20) depth and minimum tillage at 0.20 m depth (MT) each of them carried out at two different soil water contents (WC) [low, 58% (LH) and high, 80% (HH) of field capacity] were investigated. The results obtained in this research show high values of soil strength in term of Penetration resistance (CI) and shear strength (SS) particularly in deeper soil layers at lower water content. Fossil-fuel energy requirements both for P40 LH and P20 LH were 25 and 35% higher with respect to the HH treatments and tractor slip were very high (P40 LH = 32.4%) with respect to the P40 HH treatment (16%). Therefore soil water content had significantly influenced tractor performance during soil ploughing, particularly at 0.40 m depth while MT was not influenced at all. A significant correlation between grain yield and soil penetration resistance was found highlighting how soil strength may be good indicator of its productivity. Obtained results during these field tests allowed considering MT and P20 treatments more suitable for this type of soil in climate change scenarios.

Lithium has long been recognized for its therapeutic efficacy in the treatment of manic-depressive illness (MDI). Additionally, numerous reports highlight its protective properties against cell insults, with a remarkable majority of these effects documented in neurons. Utilizing the major mechanisms of its action, we bring insights that explain lithium's ability to preferentially promote survival in neurons. Specifically, we explore its interactions with magnesium at protein binding sites, with a particular focus on its impact on glycogen synthase kinase 3 beta (GSK3β). We propose that lower intracellular free magnesium levels in the brain, coupled with the higher expression of GSK3β, which is an enzyme whose activity is magnesium-dependent, amplify its impact on brain cells, promoting neuronal survival.

Objective: Repetitive transcranial magnetic stimulation (rTMS) shows promise as an aphasia treatment, however its physiological mechanisms in people with aphasia (PWA) remain largely unclear. Electroencephalography (EEG) could be used to explore rTMS mechanisms. The present study investigated the acute effects of rTMS in PWA on: 1) an EEG measure of language (N400 event-related potential); and 2) picture naming. Methods: Eleven people with chronic non-fluent or anomic aphasia (64 ± 10.13 years old; 8 males; 37.27 ± 20.64 months since stroke) received one session of 1-Hz rTMS and one session of sham to the right pars triangularis at 80% resting motor threshold (RMT). N400 mean amplitude and picture naming accuracy were compared before and after each session. Results: No acute changes were observed in N400 mean amplitude or picture naming accuracy as compared to sham (p > 0.05; BF₁₀ < 0.33). Conclusions: Results suggest that language networks underlying the N400 are not altered following one session of 1-Hz rTMS in PWA. Significance: Although EEG could be useful for assessing physiological mechanisms of rTMS in PWA, the N400 does not appear to be a viable measure of acute 1-Hz rTMS effects, at least when delivered at 80% RMT in a single session.

Partial or transient cellular reprogramming is defined by the limited induction of pluripotency factors without fully de-differentiating cells into a pluripotent state. Comparing in vitro and in vivo mouse studies, and in vitro studies in humans, supported by visualizations of the interconnections among the data, we show consistent patterns in how such reprogramming modulates key biological processes. Generally, it leads to enhanced chromatin accessibility, upregulation of chromatin modifiers, and improved mitochondrial activity. These changes are accompanied by shifts in stress response programs, such as inflammation, autophagy, and cellular senescence, as well as dysregulation of extracellular matrix pathways. We also underscore the challenges in evaluating complex processes like aging and cellular senescence, given the variability in biomarkers used across studies. Overall, we highlight biological processes consistently influenced by reprogramming while noting that some effects are context-dependent, varying according to cell type, species, sex, and the reprogramming method employed. These insights inform future research and therapeutic applications in aging and regenerative medicine.

Background: Narcolepsy is characterized by excessive daytime sleepiness (EDS) and episodes of cataplexy, frequently triggered by psychological stress, significant disturbances in sleep-wake cycles, pregnancy, or trauma. Modafinil is widely used as the preferred treatment due to its effectiveness in alleviating EDS, improving disease outcomes, and enhancing overall quality of life. Nonetheless, long-term safety and efficacy data are still lacking, underscoring the importance of future research to advance treatment options for narcolepsy. Aim: In our systematic review and meta-analysis, we aim to assess the effectiveness of Modafinil in the treatment of Narcolepsy. Methods: We searched in electronic databases (PubMed, Embase, Google Scholar, Scopus, Science Direct, Web of Science) was performed to identify cohort studies and randomized controlled trials (RCTs) up to 8th July 2024. Data extraction focused on baseline characteristics of the included studies and efficacy outcomes, including scores on the Maintenance of Wakefulness Test (MWT) and Epworth Sleepiness Scale (ESS) . We performed the meta-analysis utilizing the Review Manager software, version 5.4. To assess the outcomes, we compared the Modafinil-treated group with the placebo group, employing the mean difference (MD) and a 95% confidence interval (CI) for statistical analysis. Result: In total, 3833 articles were identified for screening, with nine studies included in the systematic review and five in the meta-analysis. These studies involved 997 adult patients with Narcolepsy treated with Modafinil. MWT revealed a significant increase in total scores (MD= 3.56, 95% CI [2.25 to 4.86], p < 0.00001) and ESS revealed a significant decrease in scores (MD= -3.34, 95% CI [-4.13 to -2.56], p < 0.00001). Conclusion: The research emphasizes the potential of Modafinil in alleviating EDS associated with Narcolepsy. While the results are promising, they are based on short term scale studies. It is crucial to undertake larger-scale, extended-duration, placebo-controlled studies that include a broad range of demographic populations. These trials are essential to confirm the efficacy, verify the safety profile, and fine-tune dosing strategies to maximize the long-term therapeutic benefits.

This paper investigates the integration of Graph Neural Networks (GNNs) and incremental learning for blockchain applications, with a focus on fraud detection, anomaly detection, and smart contract verification. By leveraging graph structure exploitation, GNNs can propagate information across nodes, reducing label dependency. Incremental learning enhances the model's adaptability to evolving blockchain networks, allowing continual learning without full retraining. Together, these technologies provide a scalable, efficient solution for improving security, performance, and adaptability in decentralized financial systems and smart contract environments.

Background:Tourette syndrome (TS) is a characterized by motor and vocal tics, often accompanied by obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD). Cannabis-based medicines (CBM) present a promising avenue for future therapeutic intervention owing to their interaction with the endocannabinoid system, potentially mitigating tics and associated symptoms. Unlike conventional treatments such as antipsychotics, CBM exhibits improved tolerability and reduced side effects. While the available evidence is still limited, ongoing research underscores the potential of CBM to enhance the quality of life for patients, establishing it as a pivotal area for future therapeutic advancements in TS. Aim: In our systematic review and meta-analysis, we aim to assess the effectiveness of cannabis-based medicine (CBM) in the treatment of Tourette Syndrome. Methods: We searched in electronic databases (PubMed, Google Scholar, Science Direct, Cochrane Collaboration Database of Randomized Trials) was performed to identify cohort studies and randomized controlled trials (RCTs) up to 18th June 2024. Data extraction focused on baseline characteristics of the included studies and efficacy outcomes, including scores on the Yale Global Tic Severity Scale (YGTSS) and Premonitory Urge for Tics Scale (PUTS). We conducted the meta-analysis using Review Manager version 5.4. software. We compared the measurements before and after CBM intake using mean difference (MD) and 95% confidence interval (CI). Result: In total, 1105 articles were identified for screening, with eight studies included in the systematic review and seven in the meta-analysis. These studies involved 306 adult patients with TS treated with cannabis. YGTSS revealed a significant reduction in total scores (MD = -13.29, 95% CI [-21.67 to -4.91], P = 0.002) and PUTS revealed a significant decrease in scores (MD = -4.09, 95% CI [-7.24 to -0.93], P = 0.01). Conclusion: The research emphasizes the potential of cannabis-based medication in alleviating tics and premonitory urges associated with Tourette syndrome. While the initial results are promising, they are based on limited-scale studies. It is imperative to conduct larger, placebo-controlled trials encompassing diverse demographic groups to substantiate the effectiveness, ensure safety, and optimize dosages for long-term therapeutic advantages.

Purpose: Arab Americans are one of the largest ethnic groups in the United States. The mental health of Arab Americans and opinions on mental health, however, remain understudied despite higher rates of mental illness and lower rates of treatment. Unique cultural conceptualizations of mental illness may generate stigma, contributing to disparities in treatment. This study aimed to assess the attitudes and perspectives of Arab Americans regarding mental illness in order to address barriers to treatment. Methods:Using an anonymous bilingual Qualtrics survey, we assessed Arab and non-Arab participants (n = 294; ages 18+) on their conceptualizations of the causes of mental illness and on their stigma towards mental illness and mental health treatments. Comparisons were performed between Arabs and non-Arabs, as well as among Arabs. Results:Compared to non-Arabs, Arabs reported higher average mental illness stigma and were more likely to rate psychiatric medications as addictive and unhelpful. Among Arabs, being Christian, male, and older (>29 years old) were each associated with higher stigma. Arabs were less likely than non-Arabs to believe in a biological explanation for mental illness. Conclusion: Our results demonstrate higher levels of stigma towards mental illness among Arab Americans, especially Christian, male, and older Arabs, potentially related to a weaker belief in a biomedical model for mental illness. These are among the first findings describing mental illness stigma and beliefs about mental health in an Arab American community and may inform future interventions providing culturally competent care to Arab Americans.

For a number of reasons, a curative treatment for most chronic diseases is still unrealized [2–4] and the incidence of many chronic diseases is rapidly increasing [5–9]. A new model is offered as a solution; that being the individualized diagnosis of homeostasis, leading to the correction of epimutations. Homeostasis diagnosis and epimutation correction, HDEC. Individuals with chronic diseases are increasingly being defined by their epimutation signatures [10,11]. Modern innovations in toxicogenomic studies are also becoming more accurate in their ability to identify the unique epigenetic signature of each toxin [12–14]. Hormesis studies (over 5,000) [15] suggest a universal principle, that a small dose is always a stimulant [16–18].These three facts present an opportunity; a method to diagnose and reverse chronic disease tendencies. This new model requires a paradigm shift in our understanding of disease origins: that dysfunctions in homeostasis [19,20] are the primary and deepest level of chronic disease causation. This is the level where the diagnosis needs to be made and where the corrections need to take place [21].

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of AN- TXR cell adhesion molecule 2 (ANTXR2)-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which ANTXR2-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and determine the mechanism of functioning be- tween the ANTXR2 and X. In this research work, we cover combinations of ANTXR2 with Collagen (COL), Integrin (ITG), Matrix metalloproteinases (MMP), WNT, TNF and Interleukin (IL) .

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of Wnt-X (X, a particular gene/protein) at 2nd order level after drug administration. These rank- ings reveal which Wnt-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and deter- mine the mechanism of functioning between the Wnt and X. In this research work, we cover combinations of Wnt with Achaete-scute complex homolog 2 (ASCL2), ATP- binding cassette (ABC) domain transporters, Interleukin (IL), ubiquitin conjugating enzyme E2 (UBE2) family, exosome (EXOSC), caspase (CASP), TP53 and B-cell lymphoma (BCL) family.

A simple model of a measurement in a laboratory isolated from the environment is analyzed in detail, demonstrating the features of the collapse effect. Such an experiment can readily be implemented today under the idealization of an observer's memory as a simple quantum system. The central role of loss of information is emphasized.

This literature review investigates the importance of simulation and measurement techniques in the development of transmission lines and antennas, which are crucial to the reliability and efficiency of communication system. It emphasizes the necessity of exact measurement and computational approaches for simulating complicated electromagnetic process, which help to optimize designs before physical prototypes are built. The review examines a variety of research articles that offer innovative solutions to improving accuracy in antenna design, scalability and cost challenges in antenna measurements, and crosstalk measurement methods. It also explores how these methodologies affect antenna design parameters such as gain, bandwidth, and radiation patterns, emphasizing the importance of correct modeling to ensure the performance of transmission lines and antennas in modern communication system. The review serves as a valuable reference for guiding future research and technological advancements in these fields.

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from January 1, 2013, to December 22, 2023. Articles were collected using key search terms such as “artificial intelligence” and “machine learning.” Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. 8 studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

Biases in Artificial Intelligence (AI) or Machine Learning (ML) systems due to skewed datasets problematise the application of prediction models in practice. Representation bias is a prevalent form of bias found in the majority of datasets. This bias arises when training data inadequately represents certain segments of the data space, resulting in poor generalisation of prediction models. Despite AI practitioners employing various methods to mitigate representation bias, their effectiveness is often limited due to a lack of thorough domain knowledge. To address this limitation, this paper introduces human-in-the-loop interaction approaches for representation debiasing of generated data involving domain experts. Our work advocates for a controlled data generation process involving domain experts to effectively mitigate the effects of representation bias. We argue that domain experts can leverage their expertise to assess how representation bias affects prediction models. Moreover, our interaction approaches can facilitate domain experts in steering data augmentation algorithms to produce debiased augmented data and validate or refine the generated samples to reduce representation bias. We also discuss how these approaches can be leveraged for designing and developing user-centred AI systems to mitigate the impact of representation bias through effective collaboration between domain experts and AI.

The existence of palindrome numbers of arbitrary length greater than 2 that are divisible by the prime number 37 is proven in this paper. The method to construct such a palindrome is also described. It turns out that a palindrome number divisible by 37 is also divisible by 3 and, consequently, divisible by 111.

Models of quantum measurements are presented, including observers subject to the laws of quantum mechanics. Observers perform multiple measurements in an attempt to disprove the collapse hypothesis. It is shown that under reasonable assumptions the effort cannot succeed. Under a unitary evolution the end state is a correlation between the result of the first measurement and the result(s) of the others, consistent with the observer's collapse hypothesis. The observer may conclude that the laws of nature include a non-unitary collapse mechanism, even if one does not actually exist. Discard of information is the crucial factor which distinguishes entanglement from measurement.

Aiming to improve medical diagnosis and treatment, artificial intelligence (AI) has emerged as a transformational force in the healthcare industry. In this article, we examine how AI is increasingly being used to improve healthcare, with an emphasis on how it is changing medical diagnosis and treatment. We explore the opportunities, threats, and ethical concerns of incorporating AI into healthcare practice. In addition, we feature important AI applications in radiology, pathology, cardiology, and cancer. AI has great potential to improve patient outcomes, diagnostic accuracy, and the ability to provide individualized treatment plans in the future if it is used and refined with care.

Background: India leads in road traffic accident (RTA) deaths, according to the 2018 World Road Statistics. Most unidentified patients exhibit poor Glasgow Coma Scale (GCS) scores ,often less than eight , heightened trauma severity, and suboptimal outcomes despite standard treatment. Our study concentrates on individuals involved in hit-and-run RTAs or found unconscious and brought to the emergency department and their final outcome.Case Presentation: We are presenting a case series featuring five patients admitted to our hospital for treatment. At the time of admission, all these patients' identities were unknown.We did not face any legal challenges in managing the unidentified patients as we followed preestablished protocols. We report a case series of five male patients who had been admitted after road traffic accidents. All of them regained consciousness after conservative management. These complex cases frequently led to extended hospital stays, underscoring the intricacies of providing care to individuals with uncertain medical histories. All of the patients were treated and successfully discharged. Conclusion: Addressing challenges with unidentified adult patients requires proactive measures like staff training and improved resources. In a developing nation, prioritizing public awareness on road safety, prehospital trauma care, and enhancing healthcare delivery, especially in hospitals, is essential for optimal outcomes.

Climate change related issues, such as yield decline, quality losses, accelerated phenology, extreme abiotic stress, pose growers new challenges in olive growing areas. Main cultural solutions that could alleviate stress and improve long-term sustainability of modern olive orchards are here briefly reviewed. Selecting varieties that are more resistant to drought or high temperatures would be the optimal solution to mitigate the effects of climate change, but the available scientific information is relatively scarce. Sustainability of irrigation implies that the less water is used, the better. Best results in terms of saving water and uniform application are obtained using micro-irrigation methods. Water can also be saved by not fully satisfying the tree water needs and different deficit irrigation strategies have been developed and tested for olive orchards. The use of green covers and the application of organic mulches and shredded pruning materials should be encouraged as these practices tend to stock carbon, increase organic matter content and water holding capacity of the soil. Olive orchards are efficient CO2 sinks as they can store considerable amounts of carbon in their organs and permanent structures. Using environmental friendly practices improves the ecosystem carbon budget and contributes to mitigate climate change.

The lung epithelium regulates gas exchange and is the first line of defense against airborne toxicants and microbial infections. Disruption of the integrity of lung epithelium results in altered permeability and severely impacts normal lung functions. To develop biomarkers of potential harm (BoPH) for assessing adverse health effects of cigarette smoking, we have evaluated lung barrier function. In a single-center, ambulatory, clinical study we measured lung permeability, as the half-life (T1/2) of inhaled 99mTC-DTPA, in 17 subjects consisting of 6 Smokers (SMK), 5 Moist Snuff Consumers (MSC), and 6 Non-Tobacco Consumers (NTC) at three weekly test visits. Smokers, relative to MSC and NTC, exhibited significantly faster clearance of the inhaled probe (shorter T1/2), indicating increased lung permeability. NTC and MSC had comparable clearance times of the inhaled probe (longer T1/2). Least squares regression modeling showed that the variability of the test was minimal and was not significant. Prediction profiling showed that the repeatability of the lung permeability test was robust and was able to differentiate the tobacco product use groups in this study. Thus, altered lung barrier function may serve as a BoPH for assessing the effects of tobacco use.

Active magnetic bearings have higher efficiency than other bearings because there is no physical contact. However, this benefit is mitigated by the addition of electrical power consumption. It is therefore important for magnetic bearings to have efficient power electronics. Gallium nitride-based transistors are a relatively new form of transistor which have shown to be more efficient than MOSFETs. There is an insufficient body of literature in the area of application of these transistors for magnetic levitation. This work presents a simple 1 degree-of-freedom voltage controlled levitation experiment in which levitation is achieved with both a gallium nitride transistor and a MOSFET.
Voltage losses and current consumption are measured when using each transistor during levitation. In particular, transients during open-to-closed and closed-to-open states are measured for PWM pulsing. It is found that the
gallium nitride transistor is superior in both switching time and efficiency in situ for magnetic levitation.

Long Covid has become a blight, its economic consequences overwhelming. But Long Covid (LC) may represent persistent Covid-19 in those with preexisting Candida overgrowth (CO). The features shared by both are striking. LC may be a growing subset of CO. The altered tryptophan metabolism encountered in LC is driven by IFN-γ in reaction to CO. Females are robust producers of IFN-γ, yet estrogen promotes CO.The consequent decline in serotonin and melatonin triggers the mood swings and sleeplessness respectively in LC. The low serotonin, the primary messenger in the gut brain axis, creates autonomic dysfunction. The CO induced gut dysbiosis and leaky gut initiate an autoimmune path. TNFα seen in both LC and this opportunistic yeast is at the center of the cytokine triad that leads to neurodegeneration. The Western diet, high in carbohydrates, alcohol, and glutamate, is exploited by this commensal turned parasite. Residual SARS CoV2 and Candida generate abundant ROS, inducing mitochondrial dysfunction. Alcohol tastes different for many with LC, because acetaldehyde requires functioning hepatic mitochondria. This article delves into the complex physiology and biochemistry that drive the symptoms of this conspicuous connection. Recent research has revealed innovative approaches that might address this global scourge, including D-mannose for the immune dysfunction and butyrate for the gut dysbiosis created by and seen in both LC and intestinal Candidiasis.

Small and medium-sized enterprises (SMEs) are vital to economic growth, yet they face considerable competitive pressures. This systematic review evaluates the impact of Porter’s Five Forces on SME performance to provide actionable strategic insights for business sustainability using qualitative and quantitative methods. A thorough search of Scopus, Web of Science, and Google Scholar was conducted, focusing on literature published between 2014 and 2024. Search terms included "Porter’s Five Forces," "SME competitiveness," and "competitive advantage." Studies were selected based on predefined inclusion and exclusion criteria, resulting in 51 out of 126 initial studies meeting the inclusion standards. Prominently, industry rivalry and customer bargaining power were found to be the most influential forces, particularly in competitive sectors such as retail and telecommunications, where differentiation and cost leadership play crucial roles. The review concludes that strategically managing Porter’s Five Forces can significantly enhance SMEs' competitive advantage and sustainability. SMEs leveraging differentiation and innovation are better equipped to address rivalry and buyer power. In industries such as manufacturing and healthcare, managing supplier power is critical for cost control and quality. Moreover, sectors like renewable energy can capitalize on the low threat of substitutes by fostering innovation. Future research should focus on specific sectors such as technology, retail, and tourism to develop tailored strategies that help SMEs navigate unique competitive pressures more effectively.

Aedes aegypti and Aedes albopictus are responsible for transmitting major human arboviruses such as dengue, Zika, and Chikungunya, posing a global threat to public health. The lack of etio-logical treatments and efficient vaccines makes vector control strategies essential for reducing vector population density and interrupting pathogen transmission cycle. This study evaluated the impact of long-term pyriproxyfen exposure on the genetic structure and diversity of Ae. aegypti and Ae. albopictus mosquito populations. The study was conducted in Manaus, Amazonas, Bra-zil, where pyriproxyfen dissemination stations have been monitored since 2014 up to the present day. Double digest restriction-site associated DNA sequencing was performed, revealing that despite significant local population reductions, focal pyriproxyfen interventions have no signifi-cant impact on the population stratification of Ae. aegypti and Ae. albopictus in urban scenarios. The genetic structuring level of Ae. aegypti suggests it is more stratified and directly affected by PPF intervention, while for Ae. albopictus exhibits a more homogeneous and less structured popu-lation. The results suggest that although slight differences are observed among mosquitoes sub-populations, intervention focused on neighborhoods in a capital city is not efficient in terms of genetic structuring, indicating that larger-scale pyriproxyfen interventions should be considered for more effective urban mosquito control.

SDG target 12.3 calls for halving food waste at retail, food service, and household by 2030. The food waste index for this purpose includes parts attached to food that are usually removed intentionally ("unavoidable food waste") and allows counting the conversion to animal feed and utilisation as biomaterial as means of reduction. In Japan, the "Food Waste Recycling Law" which has been in effect since 2001, promotes the recycling of food waste generated from businesses, designating feed conversion as top priority. On the other hand, based on the more recent "Food Loss Reduction Promotion Act" of 2019, the government is currently promoting the reduction of avoidable food waste, as to meet the SDG target. Based on statistical sources and interviews with Ministry officers, this paper explains the history and achievements of Japan's 23-year policy based on the Food Recycling Law, as well as the tension between the two legislations.

Integrated crop-livestock production (ILP) is an interesting alternative for more sustainable soil use. However, more studies are needed to analyze soil pore properties under ILP at the micrometer scale. Thus, this study proposes a detailed analysis of soil pore architecture at the micrometer scale in three dimensions. For this purpose, samples of an Oxisol under ILP subjected to minimum tillage (MT) and no-tillage (NT) with ryegrass as cover crop (C) and silage (S) were investigated. The soil micromorphological properties of the soil were analyzed using X-ray microtomography. The MT(C) system showed the highest values of porosity (c. 20.4%), connectivity (c. 32.8×103), volume (c. 26%), and number of pores (c. 32%) in a rod-like shape. On the other hand, the MT(S), NT(C), and NT(S) systems showed higher tortuosities (c. 2.2, c. 2.0, and c. 2.1) and lower pore connectivities (c. 8.3×103, c. 6.9×103, and c. 6.2×103), especially in S use. Ellipsoidal and rod-shaped pores predominated over spheroidal and disc-shaped pores in all treatments. The results of this study show that the use of ryegrass as a cover crop improves soil physical properties, especially in MT. For S use, the type of soil management (MT or NT) did not show any differences.

As digital technologies continue to evolve; small and medium enterprises (SMEs) are increasingly adopting Virtual Collaboration and Communication Technologies (VCCTs) to enhance performance in a competitive business landscape. This systematic review aims to assess the impact of VCCTs on SME performance metrics, such as operational efficiency, employee engagement, and customer satisfaction, by analyzing 41 studies selected from an initial pool of 662,422 records. The review also explores factors influencing VCCT implementation and barriers to adoption. Studies from 2014 to 2024 were reviewed, comprising journal articles (92.68%) and conference papers (7.32%). After rigorous screening, 41 studies met the inclusion criteria. The selection process included a risk bias assessment to ensure the reliability of the findings. The analysis revealed that operational efficiency improvements were the most frequently reported (68.29% of studies), followed by employee engagement (41.46%) and customer satisfaction (43.9%). Scalability was the most measured IT performance metric (41.46%), while user engagement appeared in 39.02% of the studies. Business sustainability and competitive advantage were highlighted in 46.34% and 41.46% of studies, respectively. Factors such as digital skills, organizational culture, and IT infrastructure were identified as critical for effective VCCT implementation. Nonetheless, financial constraints and employee resistance were prominent barriers. This review underscores the transformative potential of VCCTs in enhancing SME performance, particularly in operational efficiency and stakeholder engagement. A balanced focus on technological and human factors is crucial, with strategic planning and investment in digital capabilities driving success. Future research should focus on developing tailored models to maximize VCCT benefits, especially in cost-efficiency, sustainability, and innovation.

Children on farms face high risks of work and non-work-related fatalities, with tractors being a significant contributor. This study examines children's involvement in fatal tractor-related accidents within agriculture in Iceland in 1950-2024, explores adult reflections on childhood tractor-driving experiences, and analyses Members of Parliament´s arguments against setting a minimum age for off-road tractor driving. Data rests on fatal tractor-related accidents using newspaper archives and supplementary sources, narrative interviews with former summer children who stayed at farms in childhood, and parliamentary debates on tractor-related legislation. Over half of the 81 registered accidents involved children, primarily boys, with 75% occurring between 1958 and 1988 when no minimum age for off-road tractor driving existed. Children's fatality incidence rate was more than four times higher than adults. Arguments against minimum age requirements for off-road driving included the need for child labour, children's superior driving skills, and a denial that children were more often victims than adults. Since 1988, no child has died when driving a tractor. A human-centred approach focusing on working conditions, driver capacity, and adherence to safety procedures and legal frameworks is needed to prevent future accidents.

Light is dispersive in gravitational field. It results in that, under the condition of strong gravitational field, the observed numbers, size, distance and spectrogram of a celestial object is different from the real ones. Therefore, the observed jets of the Sgr A* and M87 need be understood further with the orbit of the stars around them. And, the observed orbits of the S-stars around the Sgr A* is different from the real ones. It implies that the observed optical images of the distant celestial body with strong gravitational field need be further understood with other observations and physics laws to accurately and precisely know the real physical configuration of it.

Soil contamination and the uptake of contaminants by food crops is a global problem that differs significantly from region to region, with the key factors affecting the contaminants present in local soils being the mineralogical composition of the underlying layers and local human activity. The area of ​​the Pannonian region is known for the fact that agriculture has, historically, played an important role since Roman times, which was particularly influenced by human activity. In this study we performed a preliminary study of the contamination of soil samples across the Pannonian region, to determine the potential presence of harmful contaminants. Both soil and crop samples were analysed for the potential presence of heavy metals, with additional focus placed on the potential presence of microplastics in the region`s soil.

Keywords: emerging pollutants; pharmaceuticals; plant uptake; soil behavior; soil remediation.

This study investigates the historical variability in annual average precipitation in the northwest region of Mexico, aiming to evaluate the cumulative impact of precipitation on soil degradation and associated risks posed by rainfall. Despite being known as "The Agricultural Heart of Mexico," the region's soil has experienced significant damage to its granulometric structure due to unpredictable rainfall patterns attributed to climate change. Sixteen historical series of average annual rainfall were analyzed as stationary stochastic processes for spectral analysis. The results revealed exponential decay curves in each radial spectrum, indicating a linear relationship between frequency and amplitude. These curves identified initial impulses correlated with moments of severity for structural damages caused by rainfall-induced degradation. The degradation process, exacerbated by water stress, accelerates, as evidenced by maps illustrating approximately 75% soil damage. In the context of climate change and the uncertainty surrounding soil responses to extreme meteorological events, understanding this phenomenon becomes crucial. Recognizing the dynamic nature of soil responses to environmental stressors is essential for effective soil management. Emphasizing the need to employ numerical processes tailored to new environmental considerations related to observed soil damages is crucial for sustainable soil management practices in any region.

The paper concerns the distribution of apparent magnetic susceptibility in soil profiles located in the areas of topsoil magnetic susceptibility anomalies in Krakow. The type of land use, possible sources of magnetic carriers, and the type of soil were taken into account. Additionally, at each soil profile, a comparison between soil magnetic susceptibility and the results of geochemical analyzes of soil samples was made. The study shows very characteristic changes in magnetic susceptibility with depth, reflecting the interdependencies between natural and anthropogenic factors. A visible magnetic susceptibility maximum at the depth of 10-30 cm is observed at each soil profile. The maximum is associated mainly with the deposition of atmospheric dust and its vertical range depends on the level of anthropopression and natural conditions of soils. At the depth above 40 cm in the eastern part of Krakow, a correlation between the magnetic susceptibility and the soil type (chernozems de-veloped on loess) was found. All indicates that the thicknesses of contaminated upper horizons are not accidental and they depend on human interactions with the environment and the type of soil. An attempt at template establishment with the sources of magnetic particle carriers for different places in the city was made. As the result, in high urbanized sites, the extreme values of magnet-ic susceptibility rapidly change in short vertical distances can identify the richness of anthropo-genic layers with various types of anthropogenic ferrous material and/or additionally Fe-carrying objects buried in soils. In industrialized sites, anthropogenic input plays the most important role in the creation of soil magnetic characteristics. What is more, industrial pollution hides the natural magnetic properties of chernozems. In opposite, the studies at the sites under low anthropopression (mainly located in forests) allow for better insight into magnetic proper-ties arising during pedogenic processes, indirectly giving information about soil conditions. In the forest areas, the lowest values of soil magnetic susceptibility were measured. Additionally, the influence of pedogenic and lithogenic factors on forest soils is manifested in the results. Among the sites concerned, particular attention should be paid to the vicinity of the steel plant because of the agricultural land in the surroundings.