This research focuses on deriving the equation of motion for fermions on a ring. The paper proposes an alternative equation for fermions on the ring, incorporating a self-rotation angle denoted as $\Delta$, and explores its relationship with the original equation.

Submerged aquatic vegetation, also referred to as submerged macrophytes, provides important habitats and serves as a significant ecological indicator for assessing the condition of water bodies and for gaining insights into the impacts of climate change. In this study, we introduce a novel approach for the extensive monitoring of submerged vegetation, validated against established monitoring techniques. Employing full waveform airborne laser scanning, frequently used for topographic mapping and forestry applications on dry land, we extend its application to the detection of underwater vegetation in Lake Constance. The primary focus of this research lies in the automatic classification of LiDAR (Light Detection And Ranging) point clouds, distinguishing the three vegetation classes (i) Low Vegetation, (ii) High Vegetation, and (iii) Vegetation Canopy based on their height. The results reveal detailed three-dimensional representation of submerged vegetation, enabling the identification of vegetation structures and inference of vegetation types with reference to pre-existing knowledge. While the results within training areas demonstrate high precision and alignment with comparative data, the findings in test areas exhibit comprehensible deficiencies that are likely addressable through corrective measures in the future.

The paper presents the reconstruction of paleoenvironmental conditions in the Gdansk, Bornholm and Arcona Basins of the Baltic Sea over the last millennium. A complex study (including geochemical, XRF, grain size, AMS and micropalaeontological analyses) of five sediment cores, retrieved by short gravity corer of Niemistö type, was performed. The cores are mainly represented by undisturbed silty muds of olive-grey and grey colours. The age models for the cores were built based on Pb distribution along the sediment sequences as radiocarbon dating resulted in excessively old age. Despite generally very low foraminiferal amount and diversity the increased inflow activity was reconstructed during the Medieval Climate Anomaly. The strongly positive North Atlantic Oscillation (NAO) index during this period led to a prevalence of westerlies over the Baltic region and stronger saltwater intrusions. In the recent sediments, the reconstructed inflow frequency demonstrates a decadal variability rather than the reduction, although, the general decline compared to the Medieval Climate Anomaly is seen.

Introduction: Agricultural practices significantly influence soil microbial populations and physicochemical properties, which are crucial for crop growth and quality. This study aims to investigate the impact of different agrochemical applications on soil microbial dynamics, soil physicochemical properties, as well as yield and proximate properties of maize. Methods: Carefully gathered topsoil samples at depths ranging from 1 to 15 cm, were transported to Jimma University to cultivate maize. Over a period of up to 120 days, soil and maize samples were collected at specified days to analyze various parameters, including soil pH, microbial populations, as well as nutrient content both in soil and plants. The collected data was statistically analyzed using one-way ANOVA, with a significance level of p < 0.05. Results: The soil bacterial and fungal populations were measured on days 5, 10, 20, 40, 80, and 120. The highest total mesophilic bacterial count (TMBC) was measured in the pots containing compost (G) and the lowest in pots that received macronutrient fertilizers and glyphosates (B) (i.e. 91.8 x 105 and 13.13 x 105 cfu/g of soil, respectively). The highest total mesophilic fungal count (TMFC) was observed in pots containing glyphosates and compost (F) (i.e. 67.25 x 104 cfu/g soil) and again the lowest in pot treated with macronutrient fertilizers and glyphosate (B) (i.e. 3.23 x 104 cfu/g soil). Moreover, the pots treated with macronutrient fertilizers and glyphosate (B), macronutrient fertilizers (A), and micronutrient fertilizers (C) exhibited the lowest levels of Fe and Zn. Furthermore, the pots receiving macronutrient fertilizer combined with glyphosate (B), as well as those receiving macronutrient fertilizers (A) alone had the lowest concentrations of Mn and Cu micronutrients. Finally, in maize the lowest protein, fats, and carbohydrates (g/100g) were found in the pots treated with macronutrient fertilizer combined with glyphosate (B, 2.21 ± 0.2), micronutrient fertilizer (C, 8.57 ± 0.25), and glyphosates only (D, 57.34 ± 0.1). Maize treated with compost (G) showed the highest levels of Fe, Cu, and Zn, while macronutrient fertilizer combined with glyphosate (B) resulted in the lowest content of these micronutrients. Additionally, maize receiving micronutrient fertilizer (C) had the highest concentration of Mn, whereas those treated with glyphosates (D) had the lowest. Conclusions: Significant variations in soil’s mesophilic bacterial and fungal populations, micronutrient levels, and nutritional composition were observed, indicating treatment-related changes. Generally, treatment with micromineral fertilizer combined with glyphosate (B) seemed to deplete the soil while compost treatment improved. Compost-treated soils exhibited the highest mesophilic bacterial and fungal count, as well as Fe and Zn micronutrient concentrations. The use of agrochemicals also had a negative effect on maize yield quality. The fluctuations in the soil parameters underscore the multifaceted effects of agrochemical treatments.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants of emerging concern, recognized for their toxicity and potential carcinogenic properties. PFAS chemicals are reported to act as endocrine disruptors through their interactions with various nuclear receptors (NRs). Despite their significant impact, comprehensive screenings and detailed analyses of PFAS binding strengths at both the orthosteric and allosteric sites of NRs are currently lacking in the literature. Our study addresses this gap by focusing on the binding interaction analysis of both common and uncommon PFAS with key nuclear receptors such as the vitamin D receptor (VDR), peroxisome proliferator-activated receptor gamma (PPARγ), pregnane X receptor (PXR), and estrogen receptor alpha (ERα). We used advanced docking simulations to screen 9,507 PFAS chemicals at the orthosteric and allosteric sites of the PPARγ, PXR, VDR, and ERα. We verified the accuracy of our docking protocol through multiple docking procedures and validations. The python code used for PFAS chemical screening is deposited in the supplementary file. Among the PFAS assessed at the orthosteric site of the VDR, 21 demonstrated similar binding affinities compared to the calcitriol, while 130 PFAS, exhibited stronger binding affinities than calcitriol. At the VDR allosteric site, where lithocholic acid (LCA) natively binds, 412 PFAS matched the binding energies of LCA, and 2,229 showed even greater binding energies. Similar testing on the PXR, PPARγ, and ERα revealed that the orthosteric site of PPARγ had 1,863 PFAS binding with a higher affinity than native ligand ET1 and 693 PFAS binding at the allosteric site with greater affinity than T35 native ligand. At the PXR orthosteric site, 650 PFAS bound with a greater affinity than native ligand 4WH, while at the allosteric site, 9,148 PFAS demonstrated a greater binding affinity than the known allosetric ligand glycerol. At the orthosteric site of ERα, 40 PFAS displayed binding with a greater affinity than the native ligand estradiol and at the allosteric site, only 8 PFAS exhibited greater binding affinity than coactivator ligand SRC-1. Further, our mixture modeling analysis indicates that PFAS can bind in various combinations with themselves and with endogenous or native ligands simultaneously, to disrupt the endocrine system and cause carcinogenic responses. These findings reveal that PFAS can interfere with nuclear receptor activity by displacing endogenous or native ligands by binding to the orthosteric and allosteric sites. This study elucidates the mechanisms through which PFAS exert their endocrine-disrupting effects, potentially leading to more targeted environmental regulations and therapeutic strategies. Importantly, this study is the first to explore the binding of PFAS at allosteric sites and to model PFAS mixtures at nuclear receptors. Given the high concentration and persistence of PFAS in humans, this study called for the urgent need for further research into the carcinogenic mechanisms of PFAS and the development of intervention strategies that target nuclear receptors. Our ongoing studies extend these findings by further investigating the interactions of PFAS and co-exposed chemicals with nuclear receptors. These investigations are supported by experimental verification, which will enhance our understanding of PFAS broad effects and aid in the design of effective mitigation strategies.

Sentiment analysis has become an important tool for understanding public opinion across languages and domains. Recently, there has been an increase in the number of studies on sentiment analysis in low-resource languages such as Kazakh. This is important to ensure that modern text analysis technologies are accessible to all users, regardless of their language background. The aim of the study is to create a sentiment analysis model for analyzing texts in Kazakh. As part of this work, we aim to use fine-tuning techniques on our own dataset for already existing models, thus improving their accuracy and efficiency for analyzing Kazakh language texts. This paper presents a manually collected dataset "KazIntTelCom" from the city information service 2GIS, consisting of user reviews, manually annotated by the authors taking into account the polarity of sentiment (i.e. negative positive or neutral). This dataset was used to fine-tune two pre-trained multi-lingual Transformer-based sentiment analysis models taken from the HuggingFace platform. The distillBERT and XLM-RoBERTa models were used for tuning. Also, the models were tested on the dataset "KazSAnDRA". The results show that accurate tuning even on a relatively small dataset gives a significant increase in performance, which is confirmed by an increase in the accuracy index by 20%-30%. In addition, false misses and false detections are analyzed, which allows us to identify directions for further improvement of the models. The contribution of this work, in addition to the dataset, is the analysis of model errors, which will help future developers to make more accurate settings of hyperparameters of training for sentiment analysis in Kazakh. These results are important for natural language processing and their adaptation to low-resource languages, promoting more inclusive and equitable access to modern analytical tools. Thus, this study demonstrates the effectiveness of the Transformers architecture for sentiment analysis in Kazakh and opens new opportunities for further model improvement.

The primary objective of this experiment is to enhance road safety and mitigate the occurrence of accidents by leveraging Low Power Wide Area Networks (LPWAN), specifically focusing on the Long Range (LoRa) network. Through comprehensive research into LPWANs and their various applications, we have designed and implemented a novel approach employing two mobile robots. These robots are utilized to emulate a typical traffic scenario, with one robot leading the way, skillfully maneuvering around obstacles, while the other robot closely follows behind, tracing the same path. The leading robot is tasked with collecting data from its internal environment, the external surroundings, and its operator through an array of sensors. Subsequently, this data is transmitted to the trailing robot. The follower robot's role is to analyze the data received and formulate decisions based on this information. Its objective is to mimic safe driving behavior, particularly when the received data deviates from the expected norms. In such instances, the follower robot will opt to decelerate significantly, thereby minimizing the risk of collision. By implementing this innovative system, we aim to significantly enhance driving safety and reduce the occurrence of accidents on the road.

Patients with chronic kidney disease (CKD), particularly those with end-stage kidney disease (ESKD) undergoing dialysis or kidney transplantation, face ongoing challenges in relation to the risk of COVID-19. In addition to their underlying condition, factors such as immune-evasive variants, immunosuppressive therapies, and the need for dialysis contribute to poor COVID-19 outcomes in this population. Severe COVID-19 can lead to the deterioration of CKD/ESKD, resulting in acute kidney injury, septic shock, and respiratory failure. Moreover, patients with CKD/ESKD have shown impaired response to COVID-19 vaccination, with seroconversion failures associated with the burden of immunosuppressive treatments, further heightening their vulnerability to severe disease. Despite the current endemic, it is crucial to acknowledge the ongoing significance of COVID-19 as a serious threat to patients burdened with chronic diseases like CKD and ESRD. Understanding the impact of COVID-19 on these patients is essential, as they remain at risk of severe disease progression. This paper presents the results of a literature review to enhance our understanding of COVID-19-related risks in patients with CKD/ESKD. Expert insights and illustrative scenarios provide practical perspectives and highlight the importance of risk stratification based on clinical factors for guiding targeted measures and treatments to mitigate COVID-19 risks in these vulnerable patients.

In the realm of metal steel and iron manufacturing, there is a constant drive to optimize production efficiency and enhance the quality of the final products. This abstract explores a paradigm shift in this industry through the utilization of Computational Fluid Dynamics (CFD) as a powerful tool for achieving these goals .The study focuses on various critical aspects of the manufacturing process. Firstly, it delves into three-phase processes such as slag and gas entrainment in liquid steel, which have a significant impact on the overall product quality. Understanding and optimizing these processes is crucial to ensure the desired chemical composition and physical properties of the final metal.The abstract investigates the role of CFD in vacuum degassing, a process that plays a pivotal role in removing impurities, such as hydrogen and oxygen, from the liquid steel. By leveraging CFD to simulate and analyze the fluid flow patterns, researchers and engineers can design more efficient vacuum degassing systems, leading to improved product purity and reduced waste.Another crucial aspect explored in this abstract is the alloy melt and mixing process. By employing CFD simulations, researchers can gain insights into the movement of different alloy components and optimize their distribution within the melt. This aids in achieving the desired alloy composition, ensuring uniformity, and enhancing the mechanical properties of the final product.In addition, the study delves into the movement and flotation of inclusions, which are unwanted impurities present in the metal. CFD can be utilized to model and analyze the behavior of these inclusions, enabling engineers to design efficient strategies for their removal, thereby enhancing the purity and integrity of the final product.Moreover, the abstract considers the impact of melt temperature losses, which can occur during different stages of the manufacturing process. By employing CFD, it becomes possible to understand and mitigate these temperature losses, leading to improved energy efficiency and better control over the manufacturing parameters.Part 1 of this abstract critically evaluates the use of CFD in iron making, analyzing its benefits and limitations. It highlights how CFD can aid in process optimization, improving the understanding of complex phenomena involved in iron making, and ultimately leading to enhanced production efficiency.Part 2 focuses on the application of CFD in steel making and steel operation. It explores the fluid flow dynamics during different stages of steel production, including refining, casting, and rolling. By leveraging CFD, engineers can optimize these processes, leading to improved product quality, reduced defects, and increased productivity.This abstract demonstrates the transformative potential of Computational Fluid Dynamics in metal, steel, and iron manufacturing. By utilizing CFD as a tool to model and analyze complex fluid flow phenomena, researchers and engineers can revolutionize the production process, maximizing efficiency, and ensuring high-quality end products.

The article offers a comprehensive overview of recent breakthroughs in eco-friendly polymers and polymer composites, underscoring their pivotal role in addressing sustainability challenges across diverse industrial sectors. Despite encountering obstacles related to performance and processability, biopolymers sourced from renewable materials emerge as promising alternatives for the plastics that are currently used by human beings. Natural fillers are highlighted as a strategic avenue for crafting sustainable composites, with residual natural fillers being particularly explored. Heightened environmental concerns propel discussions on the criticality of integrating biodegradable materials with natural fillers, underlining the urgency for clear frameworks and financial incentives to support adoption of these materials. Additionally, the article examines the potential of biodegradable polymers and polymer composites, with a specific emphasis on the utilization of polyhydroxyalkanoate (PHA) in composite materials and its market prospects. Moreover, it emphasizes the indispensable need for ongoing research and development endeavors to address environmental challenges within the polymer sector, reflecting a mounting interest in sustainable materials across industries.

Due to the host immunological disease brought on by infection is sepsis, multiple organ dysfunction syndrome (MODS), which has a high morbidity and mortality rate, could be the result. With advances in pathogen detection technologies, breakthroughs in anti-infection drugs, and the creation of organ function support, the clinical diagnosis and treatment of sepsis have advanced significantly. Pathogen-induced abnormal immunological responses, which can range from excessive inflammation to immunosuppression, are recognized to play a significant role in the high death rate. Unfortunately, no medications that are specifically intended to treat sepsis have been licensed. In order to offer a theoretical foundation for the management of sepsis, we will now discuss current advancements in research on immunological responses in sepsis. The prognosis of sepsis patients may be improved by developing and optimizing a dynamic immune system treatment plan based on anti-infection therapy, fluid replacement, support for organ function, and timely application of immunomodulatory therapies

The signs and symptoms of Long COVID can be explained by a shortage of blood in the body and a resulting deficient blood flow through nearly all organs. This shortage arose during the acute phase of COVID19 by an increased breakdown of haematocytes, to which the liver responds by reducing the production of albumin, in order to prevent a too large decrease in haematocrit.In order to ensure the perfusion of organs that are directly necessary for survival, the body takes the emergency measure of diverting blood from other organs and tissues. The perfusion of the blood-producing organs is also affected by this distribution measure, which hinders the smooth recovery of the total blood volume. The body is stuck in this vicious circle: a shortage of circulating blood hinders the recovery of blood production. This explains the long duration of Long COVID. My proposed treatment of Long COVID focuses on the recovery of the correct volume of blood in the body of the right composition by the intravenous administration of donor blood products, starting with albumin concentrate. A trial treatment can be performed in any hospital without much additional preparations, and has a lower associated risk for the patient than analysing the total blood. A diagnosis ex juvantibus, by therapeutic response, is therefore preferable. I suspect that most Long COVID patients will exhibit a high serum ferritin level as a result of internal haemolysis, and haematocrit and albumin values at the high and low extremes of the reference range because the liver can not keep up with the recovery of the red bone marrow.

In this paper, a small wind power generator is proposed with a new straight shape stator and rotor. The advantages of the proposed structure are introduced through a comparison between the basic and the proposed models. By comparing the pole slot combination of the proposed generator, the combination with optimal cogging torque characteristics was selected. The electromagnetic characteristics of proposed shape are analyzed for design variables using a finite element analysis (FEA) of ANSYS Maxwell. The final model of the proposed structure is designed by considering cogging torque and the generator electromagnetic characteristics. The electromagnetic and structural simulations of the final model are performed to satisfy the required performance of generator and mechanical safety. To verify the FEA results of the final model, a prototype is manufactured, experimented, and compared with the FEA results.

This study provides a comprehensive analysis of global patent trends in battery recycling, focusing on secondary batteries and related technologies across Korea, China, and the United States. The data, collected from various patent databases, was quantitatively analyzed to identify technology trends and direct future development. The analysis reveals distinct emphases on technologies such as lithium-ion and waste battery recycling, highlighting notable differences in patent activities among key companies and countries. China's large number of patents in battery manufacturing processes contrasts with the USA's focus on electrochemical cell construction and storage systems, while Korea shows significant activity in waste battery technology. The study underscores the competitive landscape and innovation efforts in the battery recycling sector, providing insights into the technological focuses and priorities of each country. It also identifies key challenges, such as the need for consistent innovation and broader geographic coverage in Korea, enhancing patent influence and international presence in China, and ensuring high patent quality and fostering innovation in lagging sectors in the United States. Addressing these challenges through enhanced collaboration, increased R&D investments, and supportive policies is crucial for strengthening the global position and driving further innovation in the battery recycling sector.

This study challenges the prevailing belief in the necessity of complex models for accurate forecasting by demonstrating the effectiveness of parsimonious econometric models, namely ARCH(1) and GARCH(1,1), over deep learning robust approaches, such as LSTM and 1D-CNN neural networks, in modeling historical volatility within pre-emerging stock markets, specifically the Moroccan and Bahraini stock markets. The findings suggest reevaluating the balance between model complexity and predictive accuracy. Future research directions include investigating the potential existence of threshold effects in market capitalization for optimal model performance. This research contributes to a deeper understanding of volatility dynamics and enhances forecasting models’ effectiveness in diverse market conditions.

Asthma is a significant public health concern. This study identified the provinces with the highest morbidity and mortality rates due to asthma among the working-age population (15-69 years) in the Republic of Ecuador. The secondary objective was to explain the possible differences attribut-able to occupational exposure. This nationwide ecological and exploratory study was conducted in 24 provinces between 2016 and 2019. Government databases were used as sources of infor-mation. Standardized rates were calculated for codes the J45 and J46. The hospitalization mor-bidity rate for asthma decreased from 6.51 to 5.76 cases per 100,000 working-age population, and the mortality rate has consistently been low and stable from 0.14 to 0.15 deaths per 100,000 working-age population. Geographic differences between the provinces were evident. The risk of hospitalization and death due to asthma was higher in the Pacific coast (five provinces) and the Andean region (two provinces). The high rates observed in provinces with greater agricultural and industrial development could be the main determinants of national heterogeneity and could act as occupational risk factors. The contribution of occupational hazards in each province should be examined in depth through ad hoc studies. These findings provide valuable information for creating public policies aimed at promoting and safeguarding the respiratory health of the popu-lation, particularly that of workers.

In order to explore the effects of glutamate on the growth performance, antioxidant capacity and protein metabolism of juvenile Chinese mitten crab. 0%, 1% and 2% glutamate were supplemented to low protein (30%) and normal protein (35%) diets respectively. There were 5 parallel tanks in each treatment, and the feeding duration was 8 weeks. The results showed that dietary glutamate did not significantly affect the weight gain of Chinese mitten crab. Diets supplemented with 2% glutamate significantly decreased the crude protein of crabs. The T-AOC of crabs fed the 30% protein diets was significantly lower than crabs fed the 35% protein diets. At 30% protein level, the superoxide dismutase (SOD) activity significantly increased with the increase of glutamate content. Dietary glutamate significantly down-regulated the relative expressions of mTOR, PI3K, S6K1 and 4EBP at 35% protein level. In conclusion, dietary glutamate cannot significantly increase the growth of Chinese mitten crab, but it can improve the antioxidant capacity in Chinese mitten crab under low protein condition.

High-density polyethylene (HDPE) waste poses a significant environmental challenge due to its non-biodegradable nature and the vast quantities generated annually. However, conventional recycling methods are energy-intensive and often yield low-quality products. Herein, HDPE waste is upcycled into anti-aging superhydrophobic thin films suitable for outdoor applications. A two-layer spin-casting method combined with heating-induced crosslinking is utilized to produce an exceptionally rough superhydrophobic surface, featuring a root mean square (RMS) roughness of 50 nm, an average crest height of 222 nm, an average trough depth of –264 nm, and a contact angle (CA) of 148°. To assess durability, weathering tests were conducted, revealing the films' susceptibility to degradation under harsh conditions. The films' resistance to environmental factors is improved by incorporating a UV absorber, maintaining their hydrophobic properties and mechanical strength. Our research demonstrates a sustainable method for upcycling waste into high-performance, weather-resistant superhydrophobic films.

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time-frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate attention mechanism for time-frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time-frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed signal time-frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification.

Several types of guided bone regeneration materials are available with various properties and clinical indications. Among them, dentin-derived matrix from extracted teeth is an excellent alternative to traditional graft materials, such as allografts and xenografts. Different clinical preparation protocols exist: block form versus powder form, demineralized versus non-demineralized form. Out of these methods, demineralization protocols for dentin-derived matrix from extracted teeth have been noted for a wide range of successful clinical outcomes. The purpose of this animal study was to find out the most effective method of demineralizing dentin-derived matrix to result in the highest regenerative ability in the bone graft material in rabbit calvaria. The study conducted a comparative histomorphometric analysis of four groups in distinguished matrix conditions: a control group without graft placement, a non-demineralized dentin-derived matrix graft placement group, a 15-minute demineralized dentin-derived matrix graft placement group, and a 30-minute demineralized dentin-derived matrix graft placement group. Regenerative processes were evaluated three times at 2, 4, and 8 weeks post-graft placement. These groups of rabbits were evaluated randomly in a blinded setting to prevent bias. Among the four groups compared, the group with a 15-minute demineralized dentin-derived matrix graft showed the most efficient bone regeneration in the histomorphometric analysis. Based on this animal study, it was apparent that a 15-minute demineralization of dentin-derived matrix is the preferred preparation protocol to maximize the regenerative potential in the matrix.

Purpose: This research investigates the influence of supplier relationship management methods on the operational efficiency and customer satisfaction of Italian restaurants.Design/Methodology/Approach: Employing a qualitative research methodology, data was gathered via interviews and questionnaires from a sample of 200 individuals across many Italian restaurants throughout Italy.Findings: The investigation uncovered robust relationships between supplier relationship management approaches and other operational factors, highlighting the interdependence of management tactics. Efficient management of supplier relationships not only improves the efficiency of the supply chain, but also has an impact on the procurement of ingredients, the variety of menu options, and the integration of technology.Research Limitations/Implications: Although technology developments were shown to enhance operational efficiency, their influence on customer satisfaction was minimal. The research highlights the complex nature of factors that influence customer happiness, indicating the need of adopting a well-rounded strategy that combines technology with personalised care. Identified continuous improvement opportunities provide valuable insights for restaurant owners to enhance strategy and better meet consumer expectations.Practical Implications: This research provides useful insights on the management of Italian restaurants, but it has limitations due to its qualitative approach and dependence on self-reported data.Social Implications: However, the results add to the current knowledge and provide practical consequences for restaurant owners who want to improve their operational efficiency and customer pleasure.Originality/Value: Italian restaurants may achieve long-term success in a highly competitive culinary industry by acknowledging the interdependence of management strategies and using customer-focused techniques.

This critical review delves into the often-overlooked aspect of early childhood language development: the potential impact of exposure to foreign languages. While multilingualism is widely celebrated for its cognitive and cultural benefits, emerging research suggests that early exposure to foreign languages may pose challenges for young children's linguistic development. The review synthesizes findings from 17 studies to explore the complexities surrounding this issue, including linguistic confusion, language delay, reduced vocabulary growth, impaired language processing, and socio-emotional implications. Additionally, mitigating factors and future directions for research and intervention are discussed. By shedding light on this topic, the review aims to inform educators, parents, and policymakers about the nuances of bilingual language acquisition and the importance of providing tailored support to optimize early childhood language development in multilingual contexts.

G-protein coupled estrogen receptor (GPER) was described to exerts several cardioprotective effects. However, the exact mechanism involved in cardiac protection remains unclear. The aim of this study is to investigate the role of GPER activation on excitation-contraction coupling (ECC), and the possibility that such effect participates in cardioprotection. Cardiac myocytes of male Wistar rats were isolated with digestive buffer and loaded with Fura-2-AM for measurement of intracellular calcium transients (CaT). Sarcomere shortening (SS) and L-type calcium current (ICaL) were also registered. Confocal technique was used to measure nitric oxide (NO) production in cells loaded with DAF-FM-diacetate. Cardiac myocytes exposed to E2 (10 nM), or G-1 (1 µM) for fifteen minutes decreased CaT, SS and ICaL. These effects were prevented using G-36 (antagonist of GPER, 1 µM), L-Name (NO synthase inhibitor, 100 nM), or wortmannin (phosphoinositide-3-kinase inhibitor, 100 nM). Moreover, G1 increased NO production, and this effect was abolished in the presence of wortmannin. We concluded that the selective activation of GPER with E2 or G1 in isolated cardiac myocytes of male rats induced a negative inotropic effect due to the reduction of ICaL and the decrease in CaT. Finally, the pathway that we proposed to be implicated in these effects is PI3K-NOS-NO.

Background: The two-stage technique combining the Endoscopic Retrograde CholangioPancreatography (ERCP) with common bile duct (CBD) clearance carried out before the laparoscopic cholecystectomy represents the most common approach for CBD stones. Over time, different techniques have been proposed to manage cholecysto- choledocholithiasis (CCL) advantageously in one stage. Among these, the laparoendoscopic rendez-vous (LERV) addresses the CCL issue with a laparoscopic cholecystectomy with insertion of a guide wire performed at initial stage of the procedure, followed by the clearance of the bile duct carried out by the endoscopists. The goal of this study was to evaluate the efficacy and safeness of LERV procedure recording results and complications, also making a comparison between our results and current literature. Methods: We reviewed our experience of all consecutive patients with cholecysto-choledocholithiasis managed with LERV between January 2018 and December 2023. LERV success rate, operative time, length of hospital stay, stone recurrence, incidence of endoscopic and surgical complications were analysed. Results: A total of 120 patients underwent LERV in our facilities. We reported a 97% LERV success rate, 1.6% conversion-to-open rate, a median intraoperative time of 122 (range 95-220) min. The median hospital stay was 4 (range 2-38) days. An overall morbidity of 15% was reported (18/120): 15 Clavien-Dindo type 1/2, 2 type 3a and one type 3b. In two cases a re-operation during the hospital stay was necessary. At a median follow-up of 14 months (3-59) five patients experienced stone recurrence requiring redo-ERCP in two cases. Conclusion: LERV offers the significant advantage of a single stage procedure and hospitalisation along with a lower risk of post-ERCP pancreatitis, failed CBD cannulation and complications. Our experience confirms safety and effectiveness of the technique for CCL management.

Background and Objective: Patients with spinal muscular atrophy (SMA) treated with a dis-ease-modifying therapy (DMT) are often classified as responders or non-responders based on at-tainment of a specific improvement threshold on validated functional scales. This categorization may significantly impact treatment reimbursement in some countries. The aim of this research is to evaluate the perception of treatment benefit by patients considered as responder or non-responder. Methods: In this non-commercial multicenter study, 99 post-symptomatically treated SMA type I-III patients with a median age of 11.2 (0.39-57.4) years at treatment initiation were stratified into three groups based on their treatment outcomes, i.e. those exhibiting clinically significant improvement (N=41), those with non-clinically significant improvement (N=18), or those show-ing no improvement (N=40). Fifteen months after treatment initiation patients or patients’ care-givers were assessed using a patient-rated scoring system based on the Patient Global Impression of Change (PGIC) scale, comprising 22 questions targeting important aspects and tasks in the daily life of patients with SMA. Results: We found no statistical difference in patient perception of treatment benefit in 17 out of 22 domains across patient groups. Conclusion: Our results suggest that functional motor scales do not recapitulate patients and pa-tients’ caregivers experience about the effect of nusinersen treatment in SMA.

To meet the dual-carbon objective, China's economy needs to move from a carbon-intensive to a green economy. Green technology innovation and renewable energy development are key to promoting green economic growth. The study uses 2010–2022 provincial panel data from 30 Chinese provinces. The super-efficient SBM model measures the province's green economic growth index. A panel fixed-effects model is used to experimentally evaluate how green technology innovation and renewable energy development affect green economic growth. Green financing moderates and a panel threshold model tests the threshold effect of renewable energy development on green economic growth. The results demonstrate that (1) China's green economic growth is rising and has a "U" shape. (2) all selected variables significantly impact green economic growth. However, population density, government intervention, and industrial structure are inversely associated with GEG. Green economic growth is significantly boosted by the moderate role of green finance in the development of renewable energy and green technological innovation. (3) Renewable energy development has a single threshold non-linear effect on green economic growth, depending on its development level and green technological innovation. Considering China's current condition and research findings, this study examines the green energy transition to promote green economic growth and high-quality economic development. Meanwhile, puts forward some important policy recommendations.

Methylation is a biochemical process involving the addition of a methyl group (-CH3) to various chemical compounds. It plays a crucial role in maintaining the homeostasis of the endothelium, which lines the interior surface of blood vessels and has been linked, among other conditions, to coronary artery disease (CAD). Despite significant progress in CAD diagnosis and treatment, intensive research continues into genotypic and phenotypic CAD biomarkers. This review explores the significance of the methylation pathway and folate metabolism in CAD pathogenesis, with a focus on endothelial dysfunction resulting from deficiency in the active form of folate (5-MTHF). We discuss emerging areas of research into CAD biomarkers and factors influencing the methylation process. By highlighting genetically determined methylation disorders, particularly the MTHFR polymorphism, we propose the potential use of the active form of folic acid (5-MTHF) as a novel CAD biomarker and personalized pharmaceutical for selected patient groups. Our aim is to improve the identification of individuals at high risk of CAD and enhance their prognosis.

Introduction:The concept of same-day discharge has garnered increasing significance within orthopedic surgery, particularly in hip and knee procedures. Despite initial concerns surrounding the absence of prolonged hospital care, a burgeoning body of evidence highlights numerous advantages associated with same-day discharge, ranging from mitigating in-hospital infections to offering substantial financial and psychosocial benefits for both patients and healthcare providers. In this study, we aim to scrutinize the trends in same-day discharge specifically within the realm of total hip arthroplasties. Method:This retrospective analysis delves into the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database spanning from 2016 to 2021. Result:In this study, same-day hip patients skewed younger, comprising 48.3%females compared to 55.6% in hospitalized hip patients. Operatively, same-day hip surgeries boasted shorter durations, averaging 83.9 minutes, in contrast to the 92.3minutes for hospitalized hip procedures. Postoperatively, same-day hip patients exhibited significantly diminished rates of 30-day readmissions (1.7%vs. 3.5%), procedure-related readmissions (1.0%vs.2.1%), reoperations (1.1%vs.1.9%), and mortality (0.02% vs. 0.04%). In addition, we found that that unplanned readmissions in the same-day group are significantly predicted by being over 65 years old (OR: 2.0), having hypertension (OR: 1.6), chronic steroid usage (OR: 2.2), and severe COPD (OR: 2.0), while gender, emergent surgery indication, and smoking were not significant predictors. Moreover, the prevalence of the same-day discharge concept experienced a remarkable ascent from 2016 to 2021, with rates escalating from 1.5% to 25.6% of all total hip arthroplasties over a span of just six years. Conclusion:In conclusion, same-day discharge is a feasible and safe option for selected THA patients, improving healthcare efficiency and patient satisfaction.

This bibliometric study explores the cryptocurrency accounting (CA) literature and the connections between authors, institutions, and countries where cryptocurrency activity involves transactions that must be legally recognized in accounting, assure accuracy and reliability for auditing, and adhere to tax compliance. The design involves data selection from Web of Science Core Collection (WoS) and Scopus, published between 2007 - 2023. The technique helps identify influential publications, collaboration networks, thematic clusters, and trends in research on CA using tools VOSviewer, Biblioshiny, and MS Excel. The originality of the study lies in its dual role as a support for accounting professionals and academics to develop innovative solutions for the challenges posed by crypto technology across core accounting areas: financial and managerial accounting, taxation, and auditing. The findings offer insights into the themes mentioned, and even if the collaboration between the authors is not very developed, the innovation and public recognition of the subject could raise researchers' interest. The limitation of the dataset is that it does not cover all relevant publications in a different period from the one in which the data was retrieved, 9-11 May 2024. This review might need periodic updates because the CA landscape is constantly changing.

This study assesses the impact of militarization, industrialization, technological innovation, and energy consumption on CO2 emissions in NATO countries, divided into "traditional" (Group 1) and "new member" states (Group 2), using data from 1985 to 2019. It identifies heterogeneity in effects and dependencies between the groups. Employing Pedroni and Kao panel cointegration tests, a long-term equilibrium relationship among the variables is confirmed. The analysis, using panel ARDL and GMM methods, reveals distinct outcomes for each group. In Group 1, military spending and energy consumption significantly increase CO2 emissions, while industrialization and technological innovation reduce them. Conversely, in Group 2, technological innovation and energy consumption raise emissions, whereas military spending and industrialization decrease them. The findings suggest Group 1 should strategize military expenditure and energy consumption to cut emissions, whereas Group 2 should focus on eco-friendly technology and efficient energy use to achieve environmental sustainability.

Relevance of the subject. Keyword selection is critical to finding relevant literature and requires justification for querying abstract databases and other sources to ensure accuracy and completeness. The topic of Energy Systems is of great importance due to the introduction of renewable energy sources, modern control methods, and complex energy systems during the energy transition. The aim of this study. The goal of this paper was to identify keywords that would be useful to subject matter experts when collecting literature on a particular topic, based on entries in the IEEE Terms field. Data. Bibliometric data were exported from the IEEE Xplore platform in the following order: for 2019-2023, 2000 records sorted by relevance, for 2024, 1680 records current as of April 11, 2024. Analytical methods applied and software used. VOSviewer, Scimago Graphica implementing the Clauset-Newman-Moore algorithm and agglomerative hierarchical clustering method implemented in Multidendrograms. Results. The main issues of the Energy Systems topic are presented in tabular and graphical formats. The fpgrowth utility offers flexible data preparation options, which makes it worthwhile to conduct a separate study to analyze the score of co-occurrence terms given by its algorithm.

Mutations in the SACS gene are associated with autosomal recessive spastic ataxia of Charlevoix-Saguenay disease (ARSACS) or complex clinical phenotypes of Charcot-Marie-Tooth disease (CMT). This study aimed to identify SACS mutations in a Korean CMT cohort with cerebellar ataxia and spasticity. As a result, eight pathogenic SACS mutations in four families were identified as the underlying causes of these complex phenotypes. The prevalence of CMT families with SACS mutations was determined to be 0.3%. All the patients showed sensory, motor, and gait disturbances with increased deep tendon reflexes. Lower limb magnetic resonance imaging (MRI) was done in four patients, and all had fatty replacements. Of note, they all had similar fatty infiltrations between the proximal and distal lower limb muscles, different from the neuromuscular imaging feature in most CMT patients without SACS mutations who had distal dominant fatty involvement. Therefore, these findings were considered a characteristic feature in CMT patients with SACS mutations. Although further studies with more cases are needed, our results highlight lower extremity MRI findings in CMT patients with SACS mutations and broaden the clinical spectrum. We suggest screening for SACS in recessive CMT patients with complex phenotypes of ataxia and spasticity.

Metabolic Associated Steatohepatitis Liver Disease (MASLD) currently impacts a quarter of the global population and its prevalence is expected to increase in the future. As a result, the incidence of hepatocellular carcinoma associated with MASLD is also on the rise. Notably, hepatocellular carcinoma in this group does not always develop alongside liver cirrhosis, often leading to a more advanced stage at diagnosis. The challenge lies in accurately identifying patients who are at a higher risk of hepatocellular carcinoma to tailor screening processes effectively. Additionally, several therapeutic approaches are being explored to prevent hepatocellular carcinoma, although there are yet no universally accepted guidelines

The review aims to assess fifteen (15) academic literature sources, highlighting the application of machine learning algorithms in the maintenance operations of optical fiber networks. It exhibits the collection of data using PRISMA methodology—Preferred Reporting Item for Systems Review and Meta-Analyses. The application, results, and performance metrics are discussed based on the collected observations, computations, and statistics in the studies, which revealed records of high accuracy degrees ranging from 86% to 98% on average and quality ML models including Neural Networks (NNs), Support Vector Machines (SVMs), and LSTM, as well as deep learning models that disclosed effective results of determining challenges and problems within the optical fiber lines. The review mainly centralized on superior machine learning technologies that surpass traditional techniques in fault detection and localization for improved optical fiber networks’ operations while providing insights into the limitations and challenges encountered in real-world applications of these models, offering a comprehensive perspective on the optical fiber network’s domain.

Entropion is a condition characterized by the total or partial inversion of the eyelid margin, resulting in contact between the outer skin and the corneal or conjunctival surface, and sometimes both. This condition can affect various animals, including horses, pigs, goats, sheep, rabbits, felines, and particularly canines. It typically manifests in the lower lid's lateral portion but can affect any part of the lid. This contact between eyelashes or facial hair and the cornea or conjunctiva leads to severe irritation, causing self-inflicted trauma, as evidenced by clinical signs of pain, irritation, ulcerations, and vision loss. Entropion can be classified as primary (congenital or developmental) or secondary (spastic and scarring). Diagnosis relies on clinical signs, the animal's history, and its breed. It's essential to assess the degree of entropion in the dog without applying topical anesthesia. This study conducts a literature review on surgical techniques for correcting various types of entropion in canines.

This qualitative research investigates the impact of price inflation on sustainable supply chains within the renewable energy sector. Through in-depth interviews with stakeholders, it explores how price inflation influences sourcing, production, distribution, and stakeholder relationships. Findings reveal that price inflation poses significant challenges to project economics, supply chain dynamics, and stakeholder relationships, necessitating proactive risk management, innovation, and collaboration. Strategies such as hedging, diversification, and scenario analysis mitigate the impact of price inflation on project viability and financial resilience. Government policies and regulatory frameworks play a crucial role in shaping responses to price inflation, with stable regulatory environments and incentives fostering investment and innovation in renewable energy projects. Innovation emerges as a critical strategy for addressing price inflation challenges, with advancements in technology and digitalization enhancing supply chain efficiency and resilience. Stakeholder collaboration and engagement are essential for mitigating the impact of price inflation, promoting transparency, trust, and cooperation among supply chain partners. By understanding the complexities of price inflation and implementing effective strategies, companies can enhance the resilience and sustainability of renewable energy supply chains, contributing to the transition towards a more sustainable energy future.

The observation that certain therapeutic strategies for targeting inflammation benefit patients with distinct immune-mediated inflammatory diseases (IMIDs) is exemplified by the success of TNF blockade in conditions including rheumatoid arthritis, ulcerative colitis and skin psoriasis, albeit only for subsets of individuals with each condition. This suggests intersecting "nodes" in inflammatory networks at a molecular and cellular level may drive and/or maintain IMIDs, being “shared” between traditionally distinct diagnoses without mapping neatly to a single clinical phenotype. In line with this proposition, integrative tumour tissue analyses in oncology have highlighted novel cell states acting across diverse cancers, with important implications for precision medicine. Drawing upon advances in the oncology field, this narrative review will first summarise learnings from the Human Cell Atlas in health as a platform for interrogating IMID tissues. It will then review cross-disease studies to date that inform this endeavour, before considering future directions in the field.

Drought and arsenic contamination pose a serious threat to global rice crop yield. Existing solutions focus on either heavy metal or drought remediation, but not both. Examination of the Oryza sativa metagenome via the National Center for Biotechnology Information (NCBI) database revealed promising candidates within the aquaporin gene family capable of efficiently mitigating both arsenic toxicity and drought stress. Rice cotyledons were genetically augmented with arsenic-resistant OsNIP2;1 and OsNIP3;2 genes, and drought-resistant OsPIP2;2 genes. In moderate drought conditions and 25 ppm arsenic-contaminated soil, transgenic plants showed drought tolerance and reduced translocation of arsenic from root to shoot. Soil arsenic levels plummeted from 25 ppm to 5 ppm, demonstrating the strategy's efficacy. Statistical significance was confirmed using T tests. Thus, aquaporin-augmented Oryza sativa offers a promising solution to mitigate both arsenic and drought stress in rice plants improving crop yield and facilitating soil decontamination.

Multifunctional landscapes are complex systems that integrate biophysical, cultural-historical and socio-economic components into unique territorial structures and regional ensembles. Facing increasing pressures generated by urbanization, climate change and overexploitation of natural resources, assessing the sustainability of these landscapes has become an urgent priority. The present research is based on the development and parametrization of a new tool for evaluating the sustainability of multifunctional landscapes that aims to measure the level of biocultural resilience of a landscape, integrating biological, cultural and socio-economic indicators in a single analytical framework. This index, called the biocultural resilience index (BRI), provides a holistic perspective on the dynamics of landscapes, allowing the identification of vulnerability areas and the development of conservation and sustainable management strategies. The evaluation of the of multifunctional landscapes sustainability requires a comprehensive approach that has to integrate both biological aspects, such as biodiversity and ecosystem health, as well as identity and cultural aspects, such as cultural value and the relationship of the local communities with the environment. In this context, this study highlights the fact that the development of appropriate tools and methods for assessing and monitoring the sustainability of multifunctional landscapes becomes essential for their sustainable management and for protecting the benefits they offer to society. Our findings show that the biocultural resilience index (BRI) can be used in a variety of contexts and fields, including spatial planning, biodiversity conservation, sustainable rural development and ecotourism. In the meanwhile, this index offers a robust and flexible analytical framework, adaptable to the needs and particularities of different regions and communities.

Convolutional Neural Networks (CNNs) are often criticized for their lack of transparency, acting as 'black boxes' in decision-making, a challenge compounded by class imbalance in ECG datasets, which limits their clinical application. This study introduces a CNN-based ECG signal classification model that enhances interpretability and addresses class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE). The model also integrates Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and SHAP value analysis, facilitating the visualization of decision boundaries and the assessment of feature contributions. Our evaluation using the MIT-BIH Arrhythmia Database highlights the model's high performance, with accuracy and precision nearing 1.00 for Normal ECG (NOR), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), and Ventricular Premature Beat (PVC) in the six-class task. In the ten-class task, the model demonstrated robustness, particularly with an accuracy of 0.9846, precision of 0.9783, recall of 0.9736, and F1 score of 0.9760 in Pacemaker Fusion Beat (PFHB), supported by an AUC of 0.9999 and AP of 0.9885. These results underscore the model's efficacy in cardiac rhythm recognition and resilience to class imbalance. Future research will explore sophisticated model architectures and feature extraction methods to enhance the model's generalization and clinical applicability for early heart disease diagnosis and personalized treatment.

A large share of the reinforced concrete (RC) building stock in Mediterranean countries faces a dual challenge of seismic vulnerability and energy inefficiency, calling for urgent renovation efforts. While energy upgrades have been the focus of previous renovation policies, recent research highlights the critical need for integrated retrofitting solutions that address both structural integrity and energy performance. Multi-criteria decision-making (MCDM) approaches are a promising tool for optimizing the combined choice of these integrated interventions, considering various decision variables (DVs) of economic, social, environmental, and technical nature. To understand the impact of climate and seismic hazard conditions on multi-criteria-based retrofitting assessment, a case-study RC school building is selected and assumed to be located in three distinct climate conditions, cold, mild, and warm, and three seismic hazard levels, low, medium and high. Moreover, given the complexity and challenges of quantifying seismic performance metrics for practitioners, an available simplified (practice-oriented) approach is compared herein with a more thorough research-based one for quantifying the seismic performance of RC buildings within the MCDM framework. Both approaches are applied to the case-study building, considering twelve possible combinations of energy and seismic interventions. The accuracy of the practice-oriented approach and its impact on the retrofitting rankings is evaluated, emphasizing the importance of accessible and efficient evaluation methods in facilitating informed decision-making for building renovation

In this research, we present the Combined Theoretical Signal (CTS) model, which integrates influences from dark matter, quantum fluctuations, and higher-dimensional effects. Our goal is to provide a potential framework for unifying gravity and quantum physics. We validate the CTS model against observational gravitational wave data and perform residual analysis to assess its accuracy. The results suggest that CTS aligns well with the observational data and provides a robust theoretical model that may contribute significantly to the quest for a universal theory. This model has the potential to enhance our understanding of the fundamental forces and the structure of the universe, supporting a hierarchical tree structure rather than parallel universes.

In response to the burgeoning global demand for premium agricultural products, particularly within the competitive nut market, this paper introduces an innovative methodology aimed at enhancing the grading process for almonds and their shells. Leveraging state-of-the-art Deep Convolutional Neural Networks (CNNs), specifically the AlmondNet-20 architecture, our study achieves exceptional accuracy exceeding 99%, facilitated by the utilization of a 20-layer CNN model. To bolster robustness in differentiating between almonds and shells, data augmentation techniques are employed, ensuring the reliability and accuracy of our classification system. Our model, meticulously trained over 1000 epochs, demonstrates remarkable performance, boasting an accuracy rate of 99% alongside a minimal loss function of 0.0567. Rigorous evaluation through test datasets further validates the efficacy of our approach, revealing impeccable precision, recall, and F1-score metrics for almond detection. Beyond its technical prowess, this advanced classification system offers tangible benefits to both industry experts and non-specialists alike, ensuring globally reliable almond classification. The application of deep learning algorithms, as showcased in our study, not only enhances grading accuracy but also presents opportunities for product patents, thereby contributing to the economic value of our nation. Through the adoption of cutting-edge technologies such as the AlmondNet-20 model, we pave the way for future advancements in agricultural product classification, ultimately enriching global trade and economic prosperity.

Ever since the virus was discovered in the 80s of the 19th century, plant viruses had a great impression within the field of plant pathology. In this book we trace the growth in the research on plant viruses beginning from traditional era (1882-1900) in which a new pathogen was discovered other than fungi and bacteria, later known as virus; biological era (1900-2000) in which the mode of spread and relationship of the virus with its vector was understood; biochemical/biophysical era (1930-1968) that marked the basis of virus purification and chemical characterization; molecular era of plant virology (1943-1995) in which establishment, progression & transmission of plant viruses was described within the host at molecular details ; era of viral molecular genetics (1980–2000), in which a significant role was played by the plant virologists to understand the position and function of genes, methods of viral gene cloning, gene silencing and other viral genome associated properties ; era of plant viral diagnostics (1980 – 2021) which led to rapid, specific and sensitive detection of plant viruses.

We present an analysis of the Dirac equation when the spin symmetry is changed from SU(2) to the quaternion group, $Q_8$, afforded by multiplying one of the $\gamma$-matrices by the imaginary number. The reason for doing this is to introduce a bivector into the spin algebra. This modifies the Dirac equation which separates into two distinct and complementary spaces: one describing polarization and the other coherence. The former describes a 2D structured spin and the latter its helicity, generated by a unit quaternion.

This study introduces a method for quantitatively assessing the complexity and predictability of fish behavior in closed systems through the application of information entropy, offering a novel lens to understand how fish adapt to environmental changes. Utilizing simulations rooted in a random walk model for fish movement, we delve into entropy fluctuations under varying environmental conditions, including responses to feeding and external stimuli. Our findings underscore the utility of information entropy in capturing the intricacies of fish behavior, particularly highlighting the synchrony in collective actions and adaptations to environmental shifts. This research not only broadens our comprehension of fish behavior but also paves the way for its application in fields like aquaculture and resource management. Through our analysis, we discovered that smaller grid sizes in simulations capture detailed local fluctuations, while larger grids elucidate general trends, pinpointing a 2.5m grid as optimal for our study. Moreover, changes in swimming speeds and behavioral adaptations during feeding were quantitatively analyzed, with results illustrating significant behavior modifications. Additionally, employing a Gaussian Mixture Model helped to clarify the nuanced changes in fish behavior in response to altered light conditions, demonstrating the layered complexity of fish responses to environmental stimuli. This investigation confirms the efficacy of information entropy as a robust metric for evaluating fish shoal behavior, offering a fresh methodology for ecological and environmental studies, with promising implications for sustainable management practices.

The interaction between electromagnetic fields (EMFs) and biological systems has been a subject of intense research and debate. This study explores the potential influence of magnetic fields on neural circuits, which are the building blocks of the human brain. By developing a mathematical model and conducting computational simulations, we investigate how magnetic fields can affect the transmission of electrical signals within neural circuits. The results suggest that magnetic fields can alter the speed and direction of signal propagation, offering potential applications in medical treatments and brain-machine interfaces. However, the study also highlights the need for more comprehensive models that consider the complexity of real-world neural circuits and the multifaceted nature of magnetic fields. The interaction between EMFs and neural circuits is a rich and complex subject that requires a multidisciplinary approach, combining physics, biology, mathematics, and computer science. While this study provides valuable insights, further research is needed to fully understand the implications of this interaction and to realize its potential applications in medicine, neuroscience, and technology. The findings contribute to a growing body of knowledge that bridges the gap between physics and neuroscience, opening new horizons for understanding the human mind and its connection to the physical world.

While the role of safe riding behavior as a safety contributor for cyclists has been increasingly studied in recent years, there have been few studies analysing cycling behavior in relation to crash-related outcomes in Baltic countries. Indeed, to the best of our knowledge, this is the first time this issue has been addressed in the case of Latvia. Aim: The objective of this study was to assess the relationships among self-repotted cyclists’ behavior, traffic safety literacy, and their cycling crash involvement rates. Method: A total of 299 cyclists aged M=32.8 from across Latvia (42% females) participated in an online survey, which comprised questions regarding respondents’ demographics, frequency of riding, and the number of crashes in the past five years. The Cycling Behavior Questionnaire (CBQ) and the Cyclist Risk Perception and Regulation Scale (RPRS) were applied to assess cyclists' behavior patterns and traffic safety literacy. Results: According to the findings, it can be inferred that cyclists frequently engage in riding errors and traffic violations while cycling. Those who exhibit more antisocial behavior patterns are also more likely to be involved in road crashes. Conversely, cyclists with greater positive behavior rates more often also tend to possess better knowledge of traffic rules and exhibit a heightened risk perception, indicating a greater awareness of road traffic safety. Conclusion: All in sum, this study underscores key age differences, with older individuals significantly less involved in riding crashes, exhibiting fewer driving errors and a higher level of risk perception, which serves as a crucial factor in road safety. At the practical level, these results stress the need to address both traffic safety literacy and protective cycling factors of cyclists, in a manner to improve overall road safety and promote active transport modes in Latvia.

Since December 2019, the COVID-19 virus has rapidly spread worldwide, prompting the World Health Organization (WHO) to declare it a pandemic and advocate for the widespread use of face masks to mitigate transmission. In this review, we delve into the potential impact of prolonged face mask use on temporomandibular joint (TMJ) health, an area that has garnered limited attention amidst COVID-19 research. Research has revealed that improper mask fit, and constant readjustment can lead to TMJ abnormalities. Similarly, there is a demonstrated correlation between continuous mask usage and increased incidence of headaches, temporomandibular pain, and diminished quality of life. Many studies have highlighted discomfort in the preauricular area, headaches, TMJ noises, headache, jaw pain, and muscle fatigue, as well as dermatological disorders that have been attributed to prolonged mask wear and its impact on TMJ. Our study catalyzes future research endeavors, urging a deeper exploration of the implications of long-term mask wear, not only in the context of the COVID-19 pandemic but also among occupational groups regularly exposed to extended mask use. By unraveling the complexities of TMJ health in the face of evolving preventive measures, we aim to enhance our understanding and safeguard the well-being of mask-wearers worldwide.

A panorama of the current historiography on the Supernova of 1054 is outlined. Shklovsky (1968) [1], and Murdin (1985) [2] show how the interpretation of this phenomenon converged on a Su-pernova event. Here we reconsider the historical data, assuming a Gamma-Ray Burst (GRB) as its source. A Supernova correlated with the GRB explains well the fading time observed by the an-cient Chinese astronomers, in daytime and in night time, while the GRB power-law explains the present X-rays and GeV emission of the Crab. On the ground of recent understanding of the first episode of binary driven Hypernova GRB (BDHN GRB) in terms of the collapse of a ten solar masses core, we propose the possible identification of the real Supernova event at earlier time than Chinese chronicles. This work allows a new understanding of the significance of historical astronomical observations, including a fireball, due to gamma-rays air shower observation, and a plague of acute radiation syndrome, documented with several thousands of victims in the Eura-sian area (Egypt, Iraq and Siria).

Background: Localization of acute appendicitis in a hernia sac is a rare phenomenon with a frequency of around 0.08 - 1 %. Even fewer cases of postoperative hernias with a vermiform appendix in the hernia sac have been described. We selected 23 cases in the English literature describing this phenomenon and analyzed diagnostic and treatment options. Case presentation: We present a 72-year-old woman who was complaining of a painful, rigid mass in the anterior abdomen wall for two days. After performing an ultrasound, incarcerated ventral hernia was diagnosed and emergency surgery was performed. During the surgery, gangrenous appendicitis was found in the hernia sac. It was decided to do an appendectomy and primal hernioplasty. Postoperative period was uneventful. Conclusion: Appendicitis within incisional hernia is an extremely rare phenomenon. Diagnosis may be challenging because of dominating incarcerated hernia symptoms. However, computed tomography might play a crucial role, by differentiating causes of acute abdomen. Appendectomy is the standard option for appendicitis treatment, however, there is no consensus on how abdominal wall integrity should be restored.

The use of optical fibers is increasing in modern aircraft because it helps solve challenges of size, weight, communication, and reliability in new aircraft generation. This work describes a video and power transmission system using optical fibers (PoF) for in-flight entertainment (IFE) system application. We present the benefits and the limitations of this application, and we perform two practical experiments to demonstrate their performance. It used off-the-shelf devices in the experiments, such as one 15-W semiconductor laser operating at 808 nm, GaAs photovoltaic converters, optical transmitters and receivers, and video monitors. The power and video signals were transmitted using two 50-m multimode fibers. In addition, we proposed and tested two types of energy transformation units (ETU), which are responsible for supplying electrical energy to the IFE video monitor and the optical fiber receiver.

The growing interest in benchtop NMR systems is visible in a vast number of applications in science and industry. Low-field nuclear magnetic resonance (LF-NMR) became an instant in technologies that productions are dependent on the reliable structure identification, grade and purity control, chemical process inspection or reaction monitoring. Beyond the benefits of being cost-, space-, maintenance- and user friendly, insufficient sensitivity and low signal resolution are the main drawbacks, due to which the low-field NMR (LF-NMR) in the fields of organic chemistry and synthesis remains limited to solely academic teaching purposes. Herein, we discuss the possibilities of LF-NMR for the needs of organic chemistry and synthesis on a set of compounds 1-4 achieved as the products of student’s laboratory course and organic derivatives 5-8 that are from our research interests. Chemical structure determination of all compounds 1-8 presented herein was performed using the benchtop-type NMR instrument operating at 60 MHz (Pulsar Oxford Ltd.). Structure of novel compounds 5-7 prepared under the aims of our research was characterized by combined 1H/19LF-NMR and compared with an appropriate high-field NMR data from Bruker Ascend Instrument (400 MHz). Finally, possibilities of the low-field NMR as an analytical tool for the research purposes in the fields of organic chemistry and synthesis are documented on a set of 1H and 2D 1H-1H COSY for azomethine 8.

In order to quickly develop a multi ship collaborative collision avoidance plan in multi ship encounter scenarios, a multi ship collision avoidance decision model is proposed in accordance with the requirements of the International Maritime Collision Avoidance Regulations, taking into account the collision avoidance operations in multi ship encounter scenarios. Firstly, the collision risk between ships is determined by the closest encounter distance (DCPA) and closest encounter time (TCPA). Secondly, a decision objective function is established based on rules, degree of ship deviation, and ship risk. Finally, the prediction mechanism is used to calculate the minimum value of the objective function and complete the collision avoidance decision. The simulation results show that in the scenario of multiple ship encounters, the collision avoidance model can achieve safe collision avoidance with a smaller steering frequency and a shorter col-lision avoidance time. The proposed multi ship collision avoidance model can provide reference for collision avoidance decision-making in multi ship encounter scenarios, which helps to im-prove the collision avoidance efficiency in multi ship collision avoidance

: This study aimed to validate the effects of self-compassion-based forest healing therapy as an intervention for reducing stress and promoting coping resources. Therefore, we investigated the effects of self-compassion-based forest healing therapy on stress, self-compassion, and connectedness to nature. A total of 30 participants were divided into an experimental and control group. Two sessions of forest healing meditation activities were held at the Forest of Healing every two weeks between October and November 2023. As a repeated measures experiment, data were collected before the intervention, after the first intervention, and after the second intervention. Perceived stress, positive and negative affect, and salivary cortisol were measured to assess physiological and psychological stress in participants. Connectedness to nature and self-compassion were measured to evaluate stress-coping resources. The results showed that the forest healing program had significant positive effects on stress and emotional states in the experimental group after the intervention. Further, there was a significant incremental effect on connectedness to nature and self-compassion in the experimental group. Therefore, self-compassion meditation-based forest healing therapy is a feasible and efficient approach for facilitating stress management and coping resource building.

Using the example of a generator model with dedicated inertia, a theoretical study of two coupled self-oscillators with capacitive coupling, their sequential single-frequency synchronization, chaos and two-frequency synchronization with an adiabatic change in the magnitude of the coupling between partial self-oscillators was carried out. The parameters of self-oscillators and the values of the coupling coefficient at which the specified operating modes of coupled self-oscillators exist are determined. The results of numerical studies, illustrating the conditions for excitation of single-frequency, chaotic and dual-frequency oscillations in a system of coupled self-oscillators are presented.

The current global business landscape demands an acute commitment of international businesses’ sustainable practices to attain a global sustainability agenda. This paper examines the intricate nexus of Multinational Enterprises' (MNEs) Corporate Social Responsibility (CSR) practices, particularly environmental innovation, business ethics, CSR reporting and their environmental performance, social performance, as well as the influence of their alignment with the United Nations Sustainable Development Goals (SDGs). Using data from Refinitiv Eikon workspace for 91 MNEs from the Middle East and North Africa (MENA) region as a point of the study, the Partial Least Squares Structural Equation Modeling (PLS-SEM) results underline the potential and significant involvement of MNEs in sustainable development shaping, especially in developing and emerging countries. The harmonization and convergence of MNEs’ CSR practices and their adoption of the SDGs emerges as a pivotal approach to enhance MNEs’ contribution to a global development and sustainability agenda.

: Schizophrenia and schizoaffective disorder are generally treated with second-generation antipsychotic drugs. These drugs are mostly D2 and 5-HT2A antagonists. They improve positive schizophrenia symptoms sufficiently well; however, they ameliorate negative schizophrenia symptoms and cognitive functions to a small extent. We review novel antipsychotic drugs exerting a partial agonism at dopaminergic and serotonergic receptors such as cariprazine, brexipiprazole and lumateperone. Besides, the mechanisms of actions of non-anti-dopaminergic antipsychotic drugs are pointed out. Updated neural networks are used to explain the mechanism of action of M4 and M1 receptor agonists, for example xanomeline combined with trospium or emraclidine, and trace-amine-associated receptor 1 agonists (TAAR1), for example ulataront. Phase 3 clinical trials of new third generation antipsychotic drugs are presented. Novel antipsychotic drugs with a partial agonism at D2 and D3 receptors improve positive and negative schizophrenia symptoms as well as cognitive symptoms better than second generation antipsychotic drugs. Besides, they are well tolerated. M4 and M1 receptor agonists, i.e. xanomeline combined with trospium or emraclidine, and TAAR1 agonists, i.e. ulataront, have promising results in clinical trials; they well improve negative schizophrenia symptoms and cognitive functions. Phase 3 clinical trials offer promising results for anti-dopaminergic and non-anti-dopaminergic novel antipsychotic drugs. These new non-anti-dopaminergic antipsychotic drugs better emend negative schizophrenia symptoms, and they better improve cognitive functions than second-generation antipsychotic drugs. Promising new antipsychotic drugs are cariprazine, brexipiprazole, lumateperone, ulataront, and xanomeline combined with trospium. Although phase 3 clinical studies are not yet completed, they showed a therapeutic effect superior to those achieved by second-generation antipsychotic drugs. They are tolerated very well, and they better treat negative schizophrenia symptoms and improve cognitive functions.

Businesses use technological and social media marketing to respond to the Covid-19 pandemic. This study aims to understand better the factors that impact the spread of new technologies, the effectiveness of social media advertising, and the longevity of businesses. The study used a quantitative strategy based on partial least squares structural equation modeling to learn about the phenomenon of interest as much as possible. Dhaka, Bangladesh, small and medium-sized businesses participated in the survey. According to the findings, the popularity of internet and e-business technologies may be attributed to their apparent usefulness and ease of use. It is connecting internet/e-business technology, social media marketing, and the long-term success of small and medium-sized enterprises. Still, the expense factor was insufficient to prove that SMEs engage in social media advertising. Mediating effects between components may be better understood via internet/e-business technology and social media marketing, as shown by this study. This study is the first of its kind in Bangladesh and sheds light on the resilience of SMEs in the face of the Covivirus-19 pandemic, notwithstanding its focus on the capital city of Dhaka.

(1) Background: This paper illustrates an innovative methodological approach chosen to study and map the colors of the Medieval wall painting of Santa Maria Antiqua in the Roman forum, one of the pilot sites of the EHEM project (Enhancement of Heritage Experiences: The Middle Ages. Digital Layered Models of Architecture and Mural Paintings over Time); (2) Methods: We chose to use two methods for gathering information about colors and mapping, specifically colorimetry by spot measurements and hypercolorimetric multispectral imaging (HMI) to map those same colors sampled through colorimetry; (3) Results: Chromatic data of all colors in the wall paintings were obtained in the CIELAB color space. In those same points, chromatic similarity maps were performed by the innovative HMI system, a multispectral imaging technique able to obtain also color data information by means of the advanced calibration software named SpectraPick®: this allowed to have a complete knowledge of the color characteristics and distribution; (4) Conclusion: The color measurements and mapping was an important result to improve the reading of Medieval wall paintings, fragmentary and stratigraphically complex, giving a new light in the knowledge of the colors and allowing to better understand the original appearance of the iconographic patterns, reconstructed with colors as faithfully as possible similar to the originals.

Orb web spiders are common and highly diversified animals found in almost all habitats. They have remarkable plasticity against biotic and abiotic factors, making them excellent indicators of environmental health. The web-creation behavior of spiders is influenced by disturbances in the environment. The aim of the research was to observe the alteration in the web-building behavior of Neoscona vigilans caused by human activities, specifically traffic disturbance. Spider webs were located and photographed at nighttime along the roadside, and their web characteristics were calculated. Spiders were captured from webs for their body measurements. Spider 4^th leg length, carapace width, and body length had a significant association with web size and diameter, CTL, capture area, and mesh size. The quantity of trapped prey, the height of the plant, and the foliage radius increased with the distance from the road. Conversely, anchor points and web elevation from the ground dropped. The highest and lowest proportion of anomalies (modifications/defects) were recorded as holes (52.7%) in 105 webs (100%) and supernumerary (0.7%) in 55 webs (52.4%), respectively. Road disturbance had a negative influence on the spider’s behavior as the webs formed in close proximity to the road had a higher frequency of anomalies with a gradual decrease distantly. We can gain further insight into how different environmental changes, disruptions, and pollutants lead to this imperfection in the otherwise flawless perfect structure of spider webs.

Elevated blood glucose levels, known as glycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding the internal mechanisms has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing biophysics mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment, computational model of the human atrial node's action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on atrial node action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the sinoatrial node action potential frequency. In summary, our mathematical model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death.

Wellbeing is a multifaceted concept that involves various facets of human condition and experience, including individual and communal spiritual, emotional, intellectual, physical, and social dimensions. To explore the complex facets of sustainable wellbeing, this study utilized an interpretive qualitative research approach, looking at round table (focus group) discussion data obtained from a diverse group of public and private sector representatives, including education, government, non-governmental organizations, and businesses in Qatar. The purpose of this study was to gain a contextualized understanding of how participants perceive and experience wellbeing. A purposive sampling technique was employed to recruit participants representing different sectors of society. To elicit participants’ perspectives on wellbeing, thematic analysis was used to identify recurring themes and patterns that emerged from the focus groups, viewed through the lens of Bronfenbrenner’ Ecological Systems Theory. Findings reveal that wellbeing is a very personal and subjective experience. Key themes extracted from the data include challenges to sustainable wellbeing in Qatar, including technological disruptions and socio-cultural influences. These challenges require proactive measures that reflect educational priorities, promote cultural preservation, and foster resilient communities while emphasizing individual and collective sustainable wellbeing. This research also identified opportunities for enhancing sustainable wellbeing in Qatar.

This study presents a comparative analysis of length deformation in Classical and Relativistic Mechanics, specifically investigating 10-gram objects accelerating to 1% of the speed of light. By employing Hooke's Law in Classical Mechanics and the Relativistic Lorentz Factor, the research explores the implications of acceleration dynamics and the limitations inherent in Relativistic Mechanics. The results reveal significant differences in predicted length changes between the two frameworks, emphasizing the necessity of considering relativistic effects beyond velocity alone. This study underscores the critical importance of addressing the incomplete treatment of acceleration dynamics in Relativistic Mechanics to achieve a more accurate depiction of length deformation in high-speed scenarios.

Pulmonary hypertension (PH) is a severe and chronic disease characterized by increased pulmonary vascular resistance and remodeling often precipitating right-sided heart dysfunction and death. Although the condition is progressive and incurable, current therapies for the disease focus on multiple different drugs and general supportive therapies to manage symptoms and prolong survival, ranging from medications more specific to pulmonary arterial hypertension (PAH) to exercise training. Moreover, there are multiple studies exploring novel experimental drugs and therapies including unique neurostimulation, to help better manage the disease. Here we provide a narrative review focusing on current PH treatments that target multiple underlying biochemical mechanisms, including but not limited to imbalances in vasoconstrictor-vasodilator and autonomic nervous system function, inflammation, and bone morphogenic protein (BMP) signaling. We also focus on the potential of novel therapies for managing PH, focusing on multiple types of neu-rostimulation including acupuncture. Lastly, we also touch upon the disease’s different subgroups, clinical presentations and prognosis, diagnostics, demographics, and cost.

This qualitative research investigates the effectiveness of certification programs in ensuring supply chain sustainability within the renewable energy sector. Through interviews and content analysis, the study explores stakeholder perceptions, challenges, and impacts of certification initiatives. Findings reveal diverse motivations driving certification adoption, including regulatory compliance, market differentiation, and corporate social responsibility. However, challenges such as greenwashing and limited transparency undermine the credibility and effectiveness of certification programs. Despite these challenges, certification initiatives drive incremental improvements in environmental performance and stakeholder engagement, fostering a culture of continuous improvement and innovation. Multi-stakeholder collaboration and context-specific approaches are essential to address regional disparities and promote sector-wide transformation. Strategies for improvement include strengthening monitoring and enforcement mechanisms, integrating emerging technologies, and aligning certification standards with broader sustainability goals. Overall, certification programs offer a pathway for promoting transparency, accountability, and responsible business practices within the renewable energy supply chain, contributing to the transition towards a more sustainable and resilient energy future.

Turbidity is an important indicator of water quality in hydrology. More traditional ways to monitor turbidity can provide reliable results. However, they are prone to human error, have elevated costs, and lack real-time monitoring capacity. Addressing these hindrances, in this work we combine spectral bands and indices from Sentinel-2 with several machine learning paradigms, namely XGBoost, Random Forests, GMDH, Support Vector Regression, k-Nearest Neighbors and Least Absolute Shrinkage and Selection Operator to model turbidity, using data from twelve monitoring stations encompassing the Mississippi River, USA. Results show that considering the individual monitoring stations, the ML paradigms for turbidity modeling were satisfactory at locations with a larger range and standard deviation values, achieving a mean R2 value of 59.5%. Tree-based models were the best overall approach, often ranking as the best or second-best performing model. When all the samples from the monitoring stations were used, the XGBoost provided superior output for turbidity modeling, reaching an R2 equal to 75.7%. A comprehensive comparison with the literature found values showed that the models implemented using this study’s methodology could provide competitive results, deeming it as an alternative for turbidity modeling from remote sensing data.

Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating Energy Storage Systems (ESS) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This paper compares the performance of hybrid energy storage systems (HESS) to single battery, evaluating their energy supply cost and environmental impact through optimization problems. The model generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load while considering battery degradation. Seven technologies are assessed, with an example applied to an industrial site contrasting vanadium redox flow (VRFB) and lithium batteries considering the demand of a local load (building). The results also indicates that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.

D-dimer testing in combination with clinical assessment is commonly used to diagnose suspected DVT and pulmonary embolism. However, as D-dimer levels are physiologically higher in elderly patients, this test loses clinical specificity in the setting of geriatric patients. The aim of our study was to investigate the changes in D-dimer levels with age and the specificity of the D-dimer test in the diagnosis of pulmonary embolism. 1617 elderly patients consecutively admitted to the Geriatric Emergency Care Department of the INRCA-IRCCS hospital between January 2013 and February 2014 and for whom D-dimer results were available were included. The D-dimer immunoturbidimetric assay was performed on a cs-2100i Sysmex Siemens using a conventional cut-off level of 500 µg/L. The median D-dimer level was 1200 [550-2990] µg/L FEU, and the percentage of patients with D-dimer levels below the conventional cut-off was only 22.1%. As expected, there was a significant age-related increase in D-dimer, even when sex was considered, and a significant decrease in the specificity of D-dimer assay with age. The most established use of D-dimer testing is in the diagnosis of deep vein thrombosis or pulmonary embolism. Our study confirms the need for an age-adjusted cut-off for D-dimer testing in geriatric patients.

Abstract: Author attribution refers to identifying the author of a document by distinguishing between texts written by various authors. Since the author’s writing style is a straightforward concept of his writing habits, an author’s attribution can be used in various sections of society, including legal work, court cases, and plagiarism. This paper studies and compares a deep architecture that automatically identifies Persian writers through their writing styles. For this purpose, since there were no existing datasets suitable for author identification in Persian, the first step was to collect three different datasets: Novels, News, and Twitter. Afterward, the Word2Vec algorithm is trained on many Persian datasets and used as word embedding. To have syntactic features besides semantic ones, Part Of Speech (POS) embedding has also been used. In the next step, an attention mechanism and Long Short-Term Memory (LSTM) are coupled with residual connections. Thus, the current layer input becomes the output of all previous layers rather than the top one. The model implemented is superior in performance for all three types of data compared to earlier models in text classification and author attribution. Absolute accuracies of 90.22%, 85.10%, and 71.39% were achieved among the three Novels, News, and Tweets datasets.

The use of edible films has become a topic of great interest in the food and environmental sectors, due to their potential to prevent food deterioration and their biodegradability. In this context, this study aimed to develop and characterize edible films based on camu-camu residue, gelatin and glycerol, evaluating their solubility, thermal, degradability, antioxidant and water vapor permeability properties of the gelatin matrix. This is the first study incorporating camu-camu into a gelatin and glycerol matrix. The films produced with the incorporation of camu-camu residue proved to be manageable and soluble, with the presence of some non-soluble residues, providing a shine and a well-presented appearance. In the biodegradation results the samples 03 and 04 appeared to degrade the most, being two of the three most affected samples in the triplicate. The films showed degradation modifications from the third day of the experiment. In the germination and plant growth analysis, Sample 04 showed satisfactory development compared to other samples, making it the sample with the best overall result in the analyses. These results demonstrate that the produced materials have potential for food packaging applications.

Field of ResearchThis research study investigates the role of distributed leadership and culture within small enterprises in Malta, an area in which there has been very limited research. It focuses on the interaction between these two elements and their impact on organizational behaviour and effectiveness. Several studies (Jardon et al 2019; Cope et al., 2011; de Oliveira et al., 2015; Mohanty et al., 2012;) suggest that effective leadership and culture are vital elements in the successful endeavours of small organizations. Schein (2006) further asserts that they are two different faces on the same coin and the dyadic interaction characterizes “how leaders create culture and how culture defines and creates leaders”. Distributed Leadership is a systematic practice of interactions and synergies between leaders, followers, and the situation (Spillane 2013). It is different from traditional leadership, which involves top-down management because it tends to be more democratic and participatory, whereby influence and decision-making are shared among multiple individuals rather than being controlled by a single leader. (Xhemajli et al. 2022) MethodologyAdopting a naturalistic approach and guided by an interpretivist, constructivist epistemological stance, this research delves into the intricate interplay between leadership and culture within small businesses. The chosen research methodology employs Grounded Theory, recognized for its robust and systematic process facilitating the observation of patterns through intensive iterative and highly analytical procedures (Charmaz, 2015).In the initial stages of field research, characterized by 'responsive interviewing' (Rubin and Rubin, 2012), data sampling commenced with convenience sampling. This initial phase gradually evolved into a refined strategy incorporating selective sampling and theoretical sampling (Corbin and Strauss, 2015). The shift in sampling approaches reflects a deliberate focus on collecting data based on its specific contribution to the development and comprehension of the dimensions, properties, and characteristics of key concepts.The induction of theory is a product of successive comparative analyses, involving the systematic comparison of data points to identify both similarities and variations. Abductive reasoning is integral throughout the analytical process, with particular emphasis during constant comparative analysis of categories, leading to theoretical integration (Charmaz, 2006). This meticulous approach aligns with the assertion of Strauss and Corbin (2015) that grounding theory in data enhances the likelihood of deriving theories that authentically reflect reality.The key advantage of using this approach is that the analysis carried out goes beyond deep exploration and rich description and actually develops a framework that explains the phenomena observed (Parker and Roffey, 1997, p.218). This is very important if the findings are to contribute to the knowledge enhancement of any field of practiceThe culmination of this research effort is the presentation of a set of interconnected concepts, intricately woven together into an emergent parsimonious model (Corbin and Strauss, 2015). This model serves to explain the dynamic interplay between leadership and culture, elucidating its tangible impact on the effectiveness of small enterprises. Research Contribution:Implications for PracticeThis research contributes to the literature on leadership and organizational culture in small enterprises, particularly in the context of Malta where such studies are scarce. By providing an initial exploration of these phenomena and their impact on organizational behavior, this paper lays the groundwork for further inquiry into this important area. Furthermore, the study offers practical insights for leaders of small enterprises by elucidating leadership behaviors observed in established organizations. Through applied cases, it demonstrates how the abstract concept of culture can be translated into a practical instrument that supports leadership and organizational development. This not only enriches the understanding of leadership dynamics but also provides tangible strategies for implementation. Moreover, the research contributes empirical evidence on the development of leadership functions within small enterprises, emphasizing the potential of shared and distributed leadership models. By grounding its findings in data, the study equips leaders with a deeper understanding of how these leadership approaches can shape organizational development, fostering more effective management practices in small businesses. Methodologically, the adoption of Grounded Theory methodology showcases its efficacy in exploring complex management phenomena. By employing this approach, the researchers were able to gather rich descriptions and develop an explanatory framework, highlighting its applicability in studying nuanced organizational dynamics. Implications for Policy and TheoryThis research also holds significant implications for policymakers, academic researchers and institutions involved in supporting small businesses. By providing insights into distributed leadership and cultural influences on organizational effectiveness, it offers a basis for informed policy formulation and support mechanisms. The conceptual model emerging from this study serves as a foundation for designing more targeted policies that cater to the unique needs of small enterprises. By understanding the intricacies of leadership dynamics and cultural contexts, policymakers can develop interventions that foster a conducive environment for small business growth and sustainability. Additionally, academic researchers can use these findings to further refine theoretical frameworks and empirical studies in the field, contributing to ongoing scholarly dialogue and knowledge advancement.

Undoubtedly, there is a potential impact of large language models like ChatGPT in revolutionizing information retrieval and knowledge discovery, particularly in the context of the vast amount of electronic material available. On another strong note, the current work offers a first time ever mathematical approach to accurately fine tune perplexity AI, which sets a giant step ahead towards accurate next generations AI. Having started this world leading discovery, the upper and lower bounds of perplexity AI, namely , , respectively, are obtained for the first-time ever. Concluding remarks combined with emerging open problems and next research phase are provided.

Recently, polyacetylene (PA) and polyenes (polyvinylenes) are receiving renewed scientific attention due to their unique physical properties potentially useful also for a number of non-electrical applications (e.g., hydrogen storage, drug delivery, adsorbent of organic compounds in aqueous systems, gas and liquid separation, oxygen absorbent in gas analysis, etc.). These chemical compounds can be obtained in form of a polyacetylene-PVOH copolymers simply by chemical dehydration of poly(vinyl alcohol) (PVOH), which is a very common type of polymer, widely used in different technological areas. This simple chemical reaction for the large-scale synthesis of PA/polyvinylenes has been investigated by reacting PVOH with fuming sulfuric acid at room temperature. In this process, PVOH, shaped in form of film, is dipped in fuming sulfuric acid (i.e., H₂SO₄ at 95-97%) and, after complete chemical-dehydration, it has been mechanically removed from the liquid phase by using a nylon sieve. This reduction process leads to a substantial PVOH film conversion to PA as it has been proved by infrared spectroscopy (ATR-mode). Indeed, the ATR spectrum of the reaction product included all characteristic absorption bands of PA. The reaction product has been also characterized by differential scanning calorimetry (DSC) and absorption optical spectroscopy (UV-Vis) in order to evidence respectively its solid-state thermal isomerization (cis-PA conversion to trans-PA) and presence in the structure of conjugated carbon-carbon double bonds of various extensions.

In this article, many experimental similarities between electromagnetic fields (which are applied in systems of electrons, atoms, and liquids) and strong interaction fields (which are applied in systems of quarks, nucleons, and nuclei) are reviewed. The currently accepted theories describing electromagnetic fields differ fundamentally from those describing strong fields. Therefore, the many similarities are quite surprising, and some are not adequately explained even today. Several such unexplained similarities are listed among the top open problems in particle physics, such as the “EMC effect,” the “proton spin crisis,” and the “strong CP problem.” This paper highlights, for the first time, the underlying common characteristics of these problems, potentially offering valuable contribution to future explanations.

This study aimed to investigate the incidence of Esox lucius with bacterial septicemia in five fish ponds of Xinjiang, China. We analyzed the water quality of each fish pond as well as the clinical symptoms and pathological changes of fish with bacterial septicemia. Furthermore, we isolated and identified pathogenic bacteria from the liver, spleen, and kidney of the infected fish based on morphology and physiology, as well as using biochemical and molecular biology techniques. By detecting the water quality of the infected fish pond, the clinical symptoms and pathological changes of the infected fish were observed, pathogenic bacteria were isolated and purified from the liver, spleen, and kidney of the died illness fish. the pathogenicity of pathogenic bacteria was identified by morphology, physiology, biochemistry, and molecular biology, and the drug sensitivity of 16 antibiotics was studied by standard Kirby–Bauer disk diffusion method. The results showed that bacterial septicemia of Esox lucius occurred in water with high levels of ammonia nitrogen, nitrates, pH, total alkalinity, and total hardness, mainly caused by the mixed infection of Aeromonas hydrophila, Aeromonas veronii and Aeromonas sobria. A rapid multiplex PCR method for the detection of pathogenic Aeromonas hydrophila was established. The isolates were sensitive to enrofloxacin, flufenicol, and doxycycline and resistant to penicillin and neomycin. This study laid a foundation for the early prediction, warning, rapid diagnosis, and effective prevention and control of bacterial septicemia.

The paper is focused on researching the behavior of heating companies in connection with current developments in the electricity market and flexibility in the context of market behavior. The work assesses the increase in profitability through the creation of a technical-economic model using an objective function with profit maximization. The paper objective is presented the procedure and methodology for creating a model using the basic scheme of production processes integrated into the system platform. The result of the work is a comparative analysis of modelled cases of implemented operation deployment according to a defined period and modelling modes in selected time series. The description of individual outputs demonstrates the economic advantage of using combinations of modes of combined electricity and heat production, and non-combined electricity and heat production, including the use of the heat suppression mode because of overproduction of electricity.

The new slide roller burnishing (SRB) method has been developed to produce mirror-like surfaces. Unlike conventional roller burnishing (RB), SRB is implemented through a unique device that allows the axes of the deforming roller and the rotary workpiece to cross resulting in a relative sliding velocity that can be controlled (in magnitude and direction) by varying the crossing angle. In the present work, the effect of SRB on the fatigue behavior of AISI 316 steel fatigue specimens was investigated by comparing with a conventional RB using the basic correlation in surface engineering: finishing–surface integrity (SI)–operating behavior. To obtain a more representative picture of the comparison, we implemented each method (RB and SRB) with two combinations of governing factors: (A) a radius of the roller toroidal surface of 3 mm, a burnishing force of 250 N and feed rate of 0.05 mm/rev (RB-A and SRB-A) and (B) a radius of the roller toroidal surface of 4 mm, a burnishing force of 550 N and feed rate of 0.11 mm/rev (RB-B and SRB- B). Both SRB-A (crossing angle of –45°) and SRB-B (crossing angle of –30°) achieved mirror-finish surfaces. SRB-B lead to the greatest fatigue strength and, thus, the longest fatigue life among all tested processes. SRB-B created the deepest zone (&gt;0.5 mm) with residual compressive macro-stresses and a clearly defined modified surface layer, whose thickness of more than 20 m is about twice that created by the other three processes.

The fuel cell particularly the Proton Exchange Membrane Fuel Cell (PEMFC) is a new energy technology that plays a critical role in achieving market viability. The PEMFC has high power density, zero CO₂ emissions, and high conversion efficiency. However, with uniform distribution of fuel in PEMFC, we can improve the performance and reduce the cost. The PEMFC will be more efficient and effective in terms of renewable energy as a byproduct only water. Metal foam is used as an alternative for the channels in PEMFC. The model of PEMFC was established by using the ANSYS Fluent 3D Model. Then we proposed four configurations of PEMFC. The outcome demonstrates that Case IV provided outstanding performance if it is compared with the other three cases. In terms of power density, Case IV provided a high power density that is double that of Case I. The porosity of metal foam was investigated. The 0.6 porosity value is found as the optimal value. The uniform distribution of the fuel can be achieved at this value which leads to the outstanding current density.

EU politics on decarbonizing shipping is an argumentative struggle in which actors try to make others see the problem and policy solutions according to their views and seek to position other actors in a specific way. This article critically analyses, by means of argumentative discourse analysis, the politics and policy process related to the recent adoption of the FuelEU Maritime regulation, the world’s most ambitious legislation for decarbonizing maritime shipping. Different storylines and discourses as well as agency of policy actors on the problems and policy options defining the meaning of decarbonization are analysed. Two discourses framed the debates, focusing on (i) incremental change and technology neutrality to meet moderate emission reductions and maintain competitiveness, and (ii) transformative change and technology specificity to meet zero emissions and gain competitiveness and global leadership in the transition towards a hydrogen economy. Policy actors successfully used discursive agency strategies such as multiple functionality and vagueness to navigate between and resolve conflicts between the two discourses. Both discourses are associated with the overarching ecological modernization discourse and failed to include issue of a just transition. The heritage of the ecological modernization discourse creates lock-ins for a broader decarbonization discourse, thus stalling a just transition.

One of the most common complications of neurogenic bladder secondary to spinal cord injury (SCI) is urinary incontinence, possibly related to bladder-emptying methods and changes in quality of life. This study aimed to identify the occurrence of this complication in adults with SCI and analyze its relationship with bladder-emptying methods, satisfaction, and lifestyle. This is a quantitative, exploratory, cross-sectional study. The variables were collected using the Bowel and Bladder Treatment Index during a telephone interview with 290 participants from February to November 2021. According to the results, 70% of the participants were male and 74.1% performed clean intermediate catheterization (CIC) as the main bladder-emptying method. Moreover, 55.6% were considered incontinent in the last year. Emptying by normal urination and bladder reflex triggering had a statistically significant relationship with urinary incontinence. A statistical association was observed between all the variables of satisfaction and lifestyle with urinary incontinence. Although CIC reduced urine leakage, a considerable number of participants still presented with frequent urine leakage. Urinary incontinence negatively altered satisfaction with the bladder-emptying method, effectiveness of bladder management, quality of life, and personal and social relationships.

The marine environment is the largest ecological habitat on Earth, albeit one of the least explored, particularly in terms of its microbial inhabitants. The marine fish gut is host to a diverse microbial community from which diverse bioactive molecules can be sourced. Due to the unique environmental pressures these microbial communities experience, the bioactive molecules they produce often evolve unique adaptations that give them diverse structures and activities, differentiating them from terrestrial homologues. Of particular interest, due to their structural and functional diversity, are the ribosomally-synthesized antimicrobial peptides (bacteriocins). With increasing pressure from emerging antibiotic-resistant disease and industrial demand for novel therapeutics, the marine fish gut microbiome represents a relatively untapped resource of novel bacteriocins that could prove beneficial to human health and aquaculture. This review presents an overview of the marine fish gut microbiome and explores its potential as a source of bacteriocins for human health with considerations for applications and future research in this area.

In the rapidly evolving landscape of the AI era, the measurement of Computational Thinking ability among Computer Science (CS) University students is of paramount importance. This study introduces UniCTCheck, a novel method designed to assess the main components of Computational Thinking in CS students. Utilizing two key instruments, namely the web application CTScore and the psychometric scale CTProg, this research aims to precisely evaluate seven core components of Computational Thinking and six programming concepts skills essential for CS students. The study, conducted at Rey Juan Carlos University and Atlantic Technological University Galway, involved a diverse sample of students from different year levels and program specializations. Through a rigorous research design, including sampling strategies and data collection tools, this study seeks to address critical research questions related to measurement of variations in students' Computational Thinking and programming skills by gender, university level, and location. By shedding light on the significance of Computational Thinking and programming in the educational realm, this research contributes to the existing literature and underscores the essential role of computational skills in the modern era.

E-tourism is one of the most important modern services to achieve the development of the tourism sector while relying on sophisticated communication technologies. Certainly, these innovations in the field of tourism have disrupted this sector with new and different dimensions of tourism development. This has led various regions and countries to accelerate the search for the tools that are necessary to achieve excellence and sustainable development in tourism. This research aims to study the development factors of e-tourism to achieve excellence and sustainable development in the tourism of the Ha’il region. Therefore, we applied two analyses: in the first one, we used a statistical analysis based on the survey sent to employees from tourism companies. The objective of this analysis is to determine the main factors of e-tourism to achieve excellence in tourism in the Ha’il region. The results of the statistical analysis have given us the following elements: There are only four main factors that allow authorities to achieve excellence in tourism in the Ha’il region, namely ST, CP, HRS, and SC factors. Then we proceed with a second analysis, to determine the main factors of excellence in tourism that drive the achievement of sustainable development in tourism in the Ha’il region. For this reason, we applied an artificial intelligence method based on the unsupervised machine learning technique. The results obtained from the machine learning technique show that five factors determine the excellence in tourism and reveal the sustainable development of Ha’il’s tourism. These factors are ST, CP, FP, HRS, and GC. Consequently, we consider that all the factors found in both analyses are important to realise excellence in tourism and sustainable development in tourism. However, to achieve sustainable development in tourism in Ha’il, Saudi authorities must develop the three common factors found in the two analyses, namely ST, CP, and HRS.

This study presents a groundbreaking approach in the field of gaming AI, focusing on the classic game” Pacman” through the lens of DRL integrated with advanced optimization techniques. The core of the research involves adapting DRL models for” Pacman”, utilizing the ESO for hyper- parameter tuning. These novel adaptations significantly enhance the AI agent’s performance, demonstrating a remarkable improvement in adaptability, responsiveness, and efficiency within the game environment. A pivotal aspect of this research is the innovative integration of metaheuristic optimization techniques into the DRL framework, a first in the domain of Atari gaming AI. This integration has proven essential in fine-tuning the DRL models, leading to a more effective and dynamic gaming experience. The study thoroughly evaluates and compares the performance of these algorithms, providing empirical support for their effectiveness and setting a new benchmark in AI-driven game development. The implications of this research extend beyond gaming AI, opening up possibilities for future exploration in several directions. Expanding DRL models to other complex gaming environments, continuous algorithmic enhancements, real-time learning adaptations, and applying these principles to robotics and autonomous vehicles are examples. The research also emphasizes ethical and responsible AI use in gaming to address fairness and addiction issues.

A deterministic quantum mechanics theory is presented in a series of papers starting with the present one. The proposed theory is shown to be consistent with the current mainstream statistical quantum theory as well as with classical physics. It produces solutions, which demonstrate that causality, physical reality, and determinism are restored and can explain in simple form concerns that are raised by results from the current mainstream statistical quantum theory. The meaning of particle- wave duality and complementarity, the possibility of a particle, like the electron, to cross through the nucleus as it does when the angular momentum of the electron is zero at the ground state of the hydrogen atom, the possibility of a point-size particle to have an “intrinsic spin”, the possibility of “quantum jumps” as the electron transitions instantaneously from one stable orbital to another without passing through the space in between the orbitals and does that at irregular time intervals, and the natural collapse of the wave function as part of the solution are some of the results that emerge from the proposed deterministic quantum mechanics theory. The phenomenon of entanglement is shown to be consistent with the proposed deterministic quantum mechanics theory. The Bohr-Schrödinger energy levels leading to the experimentally confirmed spectral lines, as well as the fine structure constant emerge directly from the solution of the equations governing theproposed deterministic quantum mechanics presented in Part II of these series of papers.

Hydrogen sulfide (H₂S) is a novel gasotransmitter, sucrose (SUC) is both cellular energy and signaling molecule. However, their signaling interaction in maize thermotolerance is poorly known. In this study, using maize seedlings as materials, metabolic and functional interaction of H₂S and SUC in maize thermotolerance was investigated. Data show that under heat stress survival rate and tissue viability were increased by exogenous SUC, while malondialdehyde content and electrolyte leakage were decreased by SUC, indicating SUC could upraise maize thermotolerance. Also, SUC-upraised thermotolerance was enhanced by H₂S, while weakened by inhibitor (propargylglycine) and scavenger (hypotaurine) of H₂S and SUC-transport inhibitor (N-ethylmaleimide). To figure out underlying mechanism of H₂S-SUC interaction-upraised thermotolerance, redox parameters in mesocotyls of maize seedlings were measured before and after heat stress. The data display that activity and gene expression of H₂S-metabolizing enzymes were up-regulated by SUC, whereas H₂S had no significant effect on activity and gene expression of SUC-metabolizing enzymes. Besides, activity and gene expression of catalase, glutathione reductase, ascorbate peroxidase, peroxidase, dehydroascorbate reductase, monodehydroascorbate reductase, and superoxide dismutase were reinforced by H₂S, SUC, and their combination under non-heat and heat conditions in varying degrees. Similarly, content of ascorbic acid, flavone, carotenoid, and polyphenol was increased by H₂S, SUC, and their combination, whereas production of superoxide radical and hydrogen peroxide level were impaired by these treatments to different extent. These results imply that the metabolic and functional interaction of H₂S and sucrose exists in the formation of maize thermotolerance through redox homeodynamics.

Hydroponic farming systems play an increasingly important role in the sustainable production of nutrient-rich foods. The contamination of surfaces in hydroponic fresh produce production poses risks to the food safety of crops, potentially endangering public health and causing economic losses in the industry. While sanitizers are widely used in commercial hydroponic farms, their effectiveness against human pathogens on surfaces, and their impact on plant health and quality are not known. In this study, we evaluated the efficacy of chemical sanitizers in eliminating Salmonella Typhimurium from inanimate surfaces in commercial hydroponic Nutrient Film Technique (NFT) systems. Further, we assessed the impact of sanitizers on the yield, quality, and nutritional value of lettuce and basil. Sanitizers (Virkon, LanXess; Sanitate 12.0, Bi-oSafe Systems; Kleen Grow, Pace Chemical Ltd.; Green Shield, United Labs Inc.; Zerotol, BioSafe Systems; Bleach, Pure Bright), were tested against Salmonella Typhimurium inoculated on NFT surfaces (nutrient reservoirs, growing channels, top covers, drain lines). The effective treatments were then tested for their impact on lettuce and basil in a split-plot experiment conducted in commercial NFT units. Crop yield, color, and nutrient content were measured throughout the crop life cycle. While all quaternary ammonium compounds (QAC), SaniDate 12.0 (200ppm), Zorotol (5%), and Virkon (1%) eliminated Salmonella Typhimurium from commercial NFT surfaces, chlorine-based sanitizer treatments were statistically similar to water treatments on most surfaces. All chemical sanitizers impacted the yield, color, and nutritional value of lettuce and basil. SaniDate 12.0 (200ppm) was the least detrimental to crops and was identified as a potential candidate for further validation in commercial hydroponic settings. The findings of this study will be translated into recommendations for the industry and will contribute to the development of future food safety guidelines and policies.

Background: The laryngeal mask airway Vision Mask (LMA VM) is a supraglottic airway device (SAD) with vision guidance system. The ideal head and neck position for direct laryngoscopy is known, but the ideal position for placing a LMA is not. The objective of this study is to evaluate and compare the optimal position for placement of a video laryngeal mask airway. Methods: This was a prospective observational, transversal and analytical study of 72 consecutive patients. In the same patient, laryngeal vision was first assessed with the head and neck in the sniffing position and then with the head in the neutral position. Procedures were filmed and performed by the same investigator. Assessment of the laryngeal view was performed using two classifications: modified Cormack-Lehane classification and Brimacombe classification. Device placement was considered adequate when the Cormack-Lehane rating was scored from 1 to 2 and the Brimacombe rating was from 2 to 4. Results: 72 patients were included in this study. In the assessment of the glottis using the Cormack Lehane classification for fiberoptic view, laryngeal visibility was adequate in 64 (88.89%) patients in the neutral position and in 65 (90,28%) patients in the sniffing position (p>0,05). In the fiberoptic view of the glottis, assessed using the Brimacombe classification, laryngeal visibility was adequate in 68 (93%) patients in the neutral position and in 69 (95%) patients in the sniffing position, (p>0,05). There was no statistically significant difference in the rate of success between the sniffing position (70 patients, 97.22% success rate) and the neutral position (67 patients, 93.06% success rate) during the first insertion attempt. Two patients required a second attempt in the sniffing position, while five patients required a second attempt in the neutral position. Conclusion: An adequate sniffing position did not result in a better glottic view than neutral position, and the number and type of additional maneuvers were equal in both positions.

In the ambitiously evolving construction industry of Kuwait, characterized by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. This research paper introduces a groundbreaking deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector’s significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This meticulous analysis allows for the detection of deviations that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning’s pivotal role in construction and building management. Additionally, our findings proactively sketch the future market trajectory and facilitate anomaly detection, thereby contributing substantially to the academic discussion on integrating machine learning into construction management. This research offers valuable insights for professionals within Kuwait, aiming to harness deep learning techniques to boost operational efficiency and strategic foresight in the construction sector.

This article develops duality principles and numerical results for a large class of non-convex variational models. The main results are based on fundamental tools of convex analysis, duality theory and calculus of variations. More specifically the approach is established for a class of non-convex functionals similar as those found in some models in phase transition. Finally, in the last section we present a concerning numerical example and the respective software.

The relentless challenges posed by the COVID-19 pandemic have placed immense pressure on densely populated regions, such as Hong Kong, particularly in the context of an aging population. The burgeoning demand for healthcare services has necessitated the optimization of resource al-location within healthcare systems. This research presents an innovative analytical queueing model specifically tailored to address the intricate gridlock dynamics prevalent in healthcare systems, with a focused application in the challenging landscape of COVID-19 management. Hong Kong, like numerous other regions, has experienced a demo-graphic transition leading to heightened healthcare demands. Traditional methodologies for scrutinizing gridlock issues in healthcare settings, including simulation models, often prove suboptimal within the context of sophisticated optimization frameworks. This study, however, delves into analytical queueing models, offering a more efficient and effective approach. These models have been underexplored, primarily due to the intricacies involved in modeling gridlock propagation within restricted capacity, a critical concern magnified by the COVID-19 pandemic. The novel Queueing Model introduced in this research incorporates the maintenance of queue capacities and topological aspects as exogenous parameters, rendering it exceptionally adaptable for diverse gridlock-related applications. This adaptability is especially pertinent to healthcare management during the COVID-19 crisis. The model meticulously integrates the concept of gridlock, furnishing a comprehensive comprehension of its origins and repercussions. Validation of this model involves rigorous comparison with established methodologies, exact results, and simulation data, consistently high-lighting its versatility and precision. By applying the model to healthcare management, we scrutinize the dynamics of patient flow within a Hong Kong hospital, placing specific emphasis on the critical issue of bed blockage, further exacerbated by the COVID-19 pandemic. The model's gridlock decomposition unearths the nuanced impact of bed blockage on distinct hospital units, addressing the imperative requirement for quantifying in-patient bed blockage within healthcare systems during a pandemic.

Feature selection is predominantly used in machine learning tasks, such as classification and regression. It selects a subset of features (relevant attributes of data points) from a larger set that contributes as optimally as possible to the informativeness of the model. There are exponentially many subsets of a given set, and thus, the exhaustive search is only practical for problems with at most a few dozen features. In the past, there have been attempts to reduce the search space using dynamic programming. However, models that consider similarity in pairs of features besides the quality of individual features do not provide the required optimal substructure. As a result, algorithms, which we will call suboptimal dynamic programming algorithms, find a solution that may deviate significantly from the optimal one. In this paper, we propose a dynamic programming algorithm with an iterative upgrade where the order of feature processing is inverted in each iteration. Such an alternating approach allows for improving the optimization function by using the score from the previous iteration to estimate the contribution of unprocessed features. The iterative process is proven to converge and terminate when the solution does not change in three successive iterations or when the number of iterations reaches the threshold. Results in more than 90 % of tests align with those of the exhaustive search, being competitive and often superior to the reference greedy approach. The validation was done by comparing the scores of output feature subsets and examining the accuracy of different classifiers learned on these features. In the context of feature selection, the proposed algorithm can be characterized as a filter method. However, we expect that the idea of alternating suboptimal optimization will soon be generalized to tasks beyond feature selection.

Background: GaitSmart (GS) is a sensor-based digital medical device that can be used with the integrated app vGym to provide a personalised rehabilitation programme for older people undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA). This study aimed to determine whether the GS intervention used in the rehabilitation of older people undergoing THA or TKA is potentially cost-effective compared to the current standard of care (SoC). Methods: Decision-analytic modelling was conducted to estimate cost-effectiveness over a seventeen- week time horizon, from an NHS perspective. UK clinical and cost data from the GaitSmart randomised clinical trial was used to obtain input parameters, and sensitivity analysis was performed to address uncertainty. Results: Over a seventeen-week time horizon, GS incurred cost savings of £450.56 and a 0.02 gain in quality-adjusted life years (QALYs) compared to the SoC. These results indicate that GS is the dominant intervention because the device demonstrated greater effectiveness and lower costs. Probabilistic sensitivity analyses confirm the robustness of our results. Conclusion: GS appears to offer short-term efficiency benefits and demonstrates cost-effectiveness for the improvement of gait in people undergoing THA or TKA, compared to the SoC. These findings provided new evidence to inform policymakers.

Oil and gas refineries are highly water-intensive industrial settings, with effluent containing a significant level of pollution stemming from diverse organic and inorganic compounds. Besides adhering to discharge standards for industrial effluent, incorporating treated oil refinery effluent (ORE) into the production cycle can play a pivotal role in curbing water consumption. In recent years, there has been research into different approaches to reclaiming ORE. Yet, selecting treatment methods that are technically, economically, and environmentally effective is crucial to preventing resource waste. Therefore, this study aimed to examine the last two decades of literature on methods and technologies used for ORE treatment. Based on the inclusion criteria, the final screening included 82 studies, with acceptable agreement assessed using Cohen's inter-examiner kappa equal to 0.86. The included studies were of biological treatment (n = 27), physicochemical processes (n = 12), advanced purification processes (n = 16), membrane-based technologies (n = 15), and green technologies (n = 13). This comprehensive review showed that the advanced membrane-based techniques are effective in the removal of pollutants from ORE for several reasons, such as reducing the consumption of chemicals, high efficiency, and ease of setup and maintenance. However, combined methods with a focus on membrane-based processes (e.g. UF-RO) are the most promising options for the reclamation of ORE. Since some effluent treatment methods require the use of chemicals and energy to run, future research should focus on environmentally friendly methods and the use of renewable energy.

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

The study assessed the risk perception, attitude, and management tools of 260 randomly selected farmers and processors in the rice value chain using data elicited via a structured questionnaire. Descriptive statistics, Equally Likely Certainty Equivalent (ELCE) technique, Binary logistic regression, and a 5-point Likert scale were employed in the data analyses. Descriptive statistics show that the respondents are young, active, and in their productive age with an intermediate education level. Majority of the farmers and processors were risk averse. Risk aversion was common among farmers and processors. Farmers' risk attitudes and perceptions of risk are significantly influenced by a number of factors, including age, access to extension services, farm size, marital status, household size, education, income, training, and experience. Majority of rice farmers consider price risk as the most important risk while majority of processors perceive production risk as the most important risk. Pests and diseases, price fluctuation, drought, and lack of input were the most important source of risk to farmers, while processors perceived price fluctuation, technology adoption, and poor storage facilities as the most important source of risk. Off-farm investment, spraying for pests and diseases, cash contribution, market information, and cooperative were the important risk mitigating management strategies employed by the farmers/processors. The study suggests improvements in the identified risk sources, which could influence the attitude and perception of the farmers and processors.

Cancers, a large and heterogeneous group of malignancies, are becoming an increasingly important cause of premature mortality worldwide. 2% of these are primary CNS tumors, of which 24% are gliomas. These tumors are diverse, but what they have in common is one of the most unfavorable prognoses among all cancers. A patient's future varies depending on the grade of the diagnosed glioma. To date, however, no standard treatment for grade IV has been established. All glioblastomas eventually undergo progression or recurrence. The current standard of treatment, which includes surgical intervention, radiation therapy and chemotherapy, is therefore far from sufficient. Work is constantly underway to discover a new, effective form of glioma therapy. Photodynamic therapy (PDT) may be one of them. It involves the local or systemic application of a photosensitive compound - a photosensitizer (PS), which accumulates in the affected tissues. Photosensitizer molecules absorb light of the appropriate wavelength, initiating activation processes leading to the formation of reactive oxygen species and selective destruction of inappropriate cells. Currently, this method has been approved for the treatment of several cancers. Research focusing on the effective use of PDT in glioma therapy is already underway, with promising results. In our work, we present molecular insights into PDT of glioma. Based on the available literature, we analyze and systematize the impact of various molecules, proteins, transporters and transmitters on the efficacy of PDT and the effect of PDT on their expression. In addition, we highlight gaps in current knowledge and point out directions for future research that may contribute to the efficacy of PDT glioma.

The current investigation scrutinized the strategic approaches employed by the top four teams in the Brazilian Men's Volleyball Superliga, according to the match's opponent. The study encompasses the analysis of 22 matches, involving teams ranked first through fourth, competing against each of the 12 teams participating in the 21-22 season of the Brazilian Men's Volleyball Superliga, including one home and one away match for each team. Utilizing social network analysis facilitated the identification of the interconnections and particularities among all variables, offering a comprehensive perspective. The findings unveiled that during the offensive phase, the second, third, and fourth-ranked teams consistently exhibited higher eigenvector values, irrespective of the opposing team, notably when the middle-blocker positioned themselves in front and near the setter. Conversely, the championship-winning team displayed variations in offensive tactics. The team securing the second position demonstrated alterations in setting placement, whereas the other teams executed settings aimed at zones 3 and 4. Additionally, the initial setter's position at the commencement of a rally displayed varying eigenvector values based on the opponent, indicating team rotation as a performance determinant. Thus, barring the finalist team, the performance of the remaining teams is intricately intertwined with the individual characteristics of players.

Thalassemia syndromes are common monogenic disorders that represent a significant global health issue. No systematic epidemiological and molecular investigations on thalassemias in Croatian population have been reported to date. This prospective study included 70 children with a presumptive diagnosis of thalassemia, and their 42 first-degree relatives. Molecular characterization was performed using direct sequencing and gap-PCR methods. We identified 46 (30 children and 16 first-degree relatives) β-thalassemia heterozygous carriers from 24 unrelated families, carrying eight different mutations and one hemoglobin variant. Five variants account for approximately 85% of all affected β-globin alleles: Hb Lepore-Boston-Washington (32,6%), HBB: c.93-21G&gt;A (19,6%), HBB:c.315+1G&gt;A (13,1%), HBB:c.92+1G&gt;A (10,9%), and HBB:c.92+6T&gt;C variant (8,7%). A need for more detailed genetic profiling of β-thalassemia carriers is emphasized since genetic modifiers can significantly impact their phenotype. Our study provides important new insights into the relevance of β-thalassemia heterozygosity in the pediatric clinical practice.