This paper reviews the requirements for the production, quality, and quality assurance of solid recovered fuels (SRF) that are increasingly used for the substitution of fossil fuels in the indus-trial utilities. The economical, technological and environmental aspects should be considered before using SRF as an alternative fuel. In general, the most important factors determining the commercial production of SRF referred to the calorific values and their available amounts adopted in the starting materials. Furthermore, the low-melting impurities and undesired constituents/elements, including chlorine, sulfur, potassium and sodium, may pose negative impacts on air pollutant emission and slagging & fouling in the process utilities. In this regard, the vent emitted from the industrial utilities must comply with the effluent standards of station-ary sources like particulate, sulfur oxides, nitrogen oxides, heavy metals (e.g., lead, cadmium, and mercury) and dioxins. For these reasons, this work will summarize the legal or regulatory requirements under development in Asian countries (i.e., Japan, South Korea, and Taiwan) and European Union (EU). Finally, the outlook of solid recovered fuel (SRF) in the waste manage-ment and industrial sectors will be addressed to echo the sustainable development goals (SDGs).

We introduce two symmetric concepts to physics: “distance” (space and time in one) and “wavematter” (electromagnetic wave packet and matter in one). We claim that physics has chosen the wrong concept of time: It is not aware that the time of a moving object flows in a direction other than my time. We provide 15 proofs for our claim by solving 15 mysteries of physics graphically! For example, we prove that length contraction and time dilation are actually geometrical effects in a 4D manifold that we call “Euclidean spacetime” (ES). We prove that the discrepancy in calculating the Hubble constant stems from a systematic error in the redshift measurement. We prove that what I deem wave, deems itself matter, which solves wave–particle duality. We even untangle quantum entanglement without the issue of non-locality. We finally understand why alternative theories of Euclidean relativity have failed: They are all based on four dimensions of space rather than distance. A huge amount of energy was injected into ES at a point that we take as origin. Ever since has this energy been moving radially away at the speed of light. We live in the 3D hypersurface of an expanding 4D hypersphere. Hyperspherical coordinates have the great advantage that they reduce all that is ever happening to just one formula. So, it is the Theory of Everything in these coordinates: “Energy is covering radial distance which, divided by Euclidean time, is equal to the speed of light.” Acceleration and force like gravitation emerge from a rotation of objects in ES and a subsequent projection to 3D space. Matching the symmetry simplifies physics.

Quantum machine learning applications have become viable with the recent advancements in quantum computing. Merging ML with the power of quantum computing holds great potential for data-driven decision-making, as well as the development of more powerful models capable of handling more complex datasets with faster processing time. This area offers potential for improving the accuracy of real-time forecasting renewable energy production. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to design, implement, and evaluate the performance of a quantum neural network forecast model for solar irradiance up to 3-hours ahead. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. Using the best quantum neural network configuration found, the proposed framework could provide competitive results when compared to its competitors, considering forecasting intervals of 5- to 120-minutes ahead, where it was the fourth best-performing paradigm. For 3-hours ahead predictions, the QNN was able to overcome its clas-sical counterparts, but the XGBoost. This fact can be an indication that the quantum model may identify and retrieve relevant spatiotemporal information from the input dataset in such a manner not attainable by the current classical approaches.

Background: School nursing plays a crucial role in promoting the health and well-being of students in educational settings. Previous research has highlighted its protective effect across various health domains, including substance use, mental health, violence, and sexual health. Despite its importance, the job satisfaction of school nurses and the factors influencing it have been insufficiently addressed in scientific research, underscoring the need to better understand their challenges and barriers. Therefore, the objective of this research is to evaluate the perceived job satisfaction and professional quality of life of school nurses at a national level in Spain. Methods: A descriptive cross-sectional study was conducted using the Font-Roja Job Satisfaction Questionnaire and the CPV-35 Professional Quality of Life Questionnaire. The sample included 553 school nurses from different geographical regions in Spain, selected through school nursing groups and associations. Data were collected from November 2023 to February 2024. Results: The majority of the nurses were women (68%), with a median age of around 40 years and predominantly less than 5 years of work experience (56%). Significant differences were observed in job satisfaction and professional quality of life depending on the employing entity (healthcare and educational settings). Factors such as workload, institutional support, and work experience influenced the perception of job satisfaction. Conclusions: We affirm that this study, being one of the first of its kind at a national level in Spain, highlights the need to develop effective strategies to improve the job satisfaction and professional quality of life of school nurses. School nurses in Spain present a moderately high level of job satisfaction, although there are significant differences in factors such as status and job monotony depending on the sector of employment (Healthcare, Education, Others). Nurses in educational contexts report lower levels of job satisfaction, influenced by the nature of the work, interpersonal relationships, opportunities for professional development, and institutional support. Additionally, work experience impacts satisfaction, being more positive for nurses with greater experience. We also affirm that the professional quality of life of school nurses is homogeneous, with an average score of 6.000 on the CPV-35. Although professional competence is uniformly perceived, work pressure is a common concern. Working more than 37.5 hours per week is associated with higher professional quality of life, although also with lower job satisfaction. Only half of the schools have a school nurse, highlighting the need for policies that ensure equity in service provision. There is also a recognized need for health training for teachers, which could improve support for nurses and student health. Active interaction with the educational community reinforces the integral role of school nurses and emphasizes the importance of the socioeconomic context in interventions and resource allocation.

Foods are complex products consisting of various ingredients of diverse origins. Along the food supply chains, food products are prone to spoilage and safety issues, which could give rise to different health-related concerns and outcomes. Furthermore, the existing highly centralised food systems generate substantial greenhouse gas emissions and food waste. In order to strengthen the sustainable decentralised "farm-to-fork" food chains, a mindset shift supported by relevant education and policy initiatives is highly required. The ERASMUS+ KA220 project SafeFood4EU was conceived with a crucial mission- to develop an interdisciplinary and innovative toolbox of higher education and lifelong learning courses that reflect various aspects of sustainable agri-food systems. The thematic areas discussed within the SafeFood4EU project have covered food safety and supply chain management, sustainable food systems and design, food production and quality of raw materials, food authenticity and analysis, and digital and soft skills. The main objectives have been to develop a distinctive content package and make it available to the students via an e-learning management system. The project's results, aimed at creating a pool of young and capable experts, can significantly impact future food security, thereby increasing awareness about the future challenges regarding food systems transition, dietary shift and the next generation's food security.

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

Protein glycation impacts enzymatic activity, disrupts metabolic processes, and is associated with chronic degenerative diseases. The human glycolytic enzyme triosephosphate isomerase (HsTIM, TPI1, or HsTPI) is associated with various disorders, such as enzymopathies originating from mutations. It also possesses moonlighting functions and is now recognized as a cancer marker. Previous reports of HsTPI mutants have demonstrated a significant increase of methyl-glyoxal (MGO) due to trioses accumulation. We compared how catalytic activity exerts differen-tial kinetic and structural stability effects due to mutations by tracking fluorescent argpyrimi-dine adduct (ARGp) through interaction with Arginine-MGO. Circular dichroism and fluores-cence spectroscopy were explored in HsTPI-WT and mutants HsTPI-C217K “Like-WT”, HsTPI-N16D accumulated in cancer, and HsTPI-E104D human deficiency mutant, by cysteines accessibility and time loss of activity and unfolding propensity in mutant enzymes with aggre-gates refractory to proteolysis in the C217K. Our study sheds new light on the increased suscep-tibility of mutants to generate MGO adducts due to TPI isomerization of MGO precursor sub-strates. Nevertheless, mutations enhance a negative feedback cycle, in which accumulated MGO access promotes accelerated loss of activity that leads to unfolding and aggregation. The cumu-lative and accelerated increase in MGO adducts, which probably are responsible for the patho-logical state in this TPI variant, are partially reversed by adding MGO-scavenger molecules.

This Dataset reports the clinical information and measurements of 231 patients who underwent multifrequency low-level laser-assisted mastopexy. The Dataset was collected between February 2021 and November 2023. These data were used to assess the effect of this procedure on breast ptosis degree and the nipple to sternal notch distance to know the evolution of breast ptosis. This article presents demographic, personal and familial history, anthropometric measurements, pto-sis degree according to the Regnault system, Nipple-to-Sternal Notch distance (SNND), and complications according to the Clavien-Dindo System. The collected data were in English, and the responses were anonymised to ensure the Colombian legal framework. The data were stored in a .csv file.

Bacillus Calmette-Guérin (BCG) immunotherapy has been a cornerstone treatment for non-muscle invasive bladder cancer for decades and still faces challenges, such as severe immune adverse reactions, which reduce its use as a first-line treatment. This review examines BCG therapy’s history, mechanisms, and current status, highlighting how nanotechnology and bioengineering are revitalizing its application. We discuss novel nanocarrier systems aimed at enhancing BCG's efficacy while mitigating specific side effects. These approaches promise improved tumor targeting, better drug loading, and enhanced stimulation of anti-tumor immune responses. Key strategies involve using functional BCG cell wall components and co-delivering BCG with chemotherapeutics to achieve synergistic effects. While most studies remain experimental, this research direction offers hope for overcoming BCG's limitations and advancing bladder cancer immunotherapy. Further elucidation of BCG's mechanisms and rigorous safety evaluations of new delivery systems will be crucial for translating these innovations into clinical practice.

This paper presents a new optimization algorithm to control the voltage of power system generators by the simultaneous tuning of the parameters of Automatic Voltage Regulator (AVR) and Power System Stabilizer (PSS) using a new technique called Particle Swarm Optimization with Periodic Oscillating Exponential Decay (PSO - POED). The proposed algorithm was applied to both the Single-Machine Infinite-Bus (SMIB) Power System and the 9-Bus Multi-Machine Power System. The results of the simulation show that the proposed method is efficient and guarantees an improvement in the dynamic stability and convergence rate of the simulations.

Relationships of the three bryophyte lineages (liverworts, mosses, and hornworts) to vascular plants is a pivotal question in the study of origin and evolution of land plants. In recent decades, this question has been subject to intense phylogenetic analyses using morphological characters, multigene DNA sequences, and genome structural characters. A tentative consensus reached ten years ago suggested that bryophytes are a paraphyletic group, with liverworts being sister to all other land plants and hornworts being sister to vascular plants. However, several more recent nuclear phylogenomic studies have concluded that bryophytes represent a monophyletic group that is sister to vascular plants. A discussion is presented here on strengths and weaknesses of different types of characters (morphological traits, nucleotide sequences, and genome structural arrangements), and their suitability for resolving deep phylogenetic relationships. Moreover, several criteria for credible phylogenetic reconstruction are proposed. Strong statistical support for reconstructed relationships should be derived from high quality, independent characters selected for suitability to the particular question being addressed. The relationships inferred in a study should be congruent with those from as many other lines of phylogenetic evidence as possible. Any incongruities should be explicable by well-understood biological mechanisms. It is concluded that the relationships of the three bryophyte lineages to vascular plants should currently be viewed as unresolved. This is a difficult phylogenetic problem; the land plants underwent a rapid radiation a long time ago. Yet, further exploration of analytical methods and careful choice of characters should lead to eventual elucidation of diversification patterns among early land plants.

Previous studies have reported that clinical walk tests could not predict development of frailty. With advancements in wearable technology, it may be possible to predict the development of frailty using loading asymmetry parameters during clinical walk test. This prospective cohort study aimed to test the hypothesis that increased limb loading asymmetry predicts frailty risk in community-living older adults. Sixty-two independently ambulant community-living adults aged ≥ 65 years were recruited, and forty-seven subjects completed the one-year follow-up after baseline. Loading asymmetry index of net and regional (forefoot, midfoot, and rearfoot) plantar forces were collected using force sensing insoles (100 Hz) during a 10-m walk test with their maximum speed. Development of frailty was defined if the participant progressed from baseline at least one grading group of frailty at follow-up period using the Kihon Checklist. Fourteen subjects developed frailty at follow-up period. Increased risk of frailty was associated with each 1% increase in loading asymmetry of net impulse (Odds ratio 1.153, 95% CI 1.001 to 1.329). Loading asymmetry of net impulse significantly correlated with asymmetry of peak force in midfoot force. These results indicate the feasibility of measuring plantar forces of gait during clinical walking tests, and underscore the potential of using load asymmetry as a tool to augment frailty risk assessment in community-dwelling older adults.

Respiratory syncytial virus (RSV) is the most common cause of acute lower respiratory tract infection in young children globally. Following the COVID-19 pandemic, RSV infections have become more prevalent. Consequently, a new vaccine targeting RSV infections in pregnant women has been introduced worldwide. To estimate the effectiveness of this new vaccine for pregnant women, we investigated neutralizing antibodies against RSV in samples taken from 51 mothers and their babies. The prevalence rate of neonatal antibodies, defined as a titer of ≥16, was 39.2%. We found no significant differences in neonatal neutralizing antibodies based on factors, such as sex, gestational age at birth, birth weight, presence of siblings, neonatal problem, and usage of palivizumab. Neonatal neutralizing antibodies were equal to or higher than those of their mother. A new RSV vaccine targeting all pregnant women is promising, as approximately 60% of neonates had low antibody titers against RSV. However, predicting low antibody titer against RSV remains challenging. Moreover, our findings indicate efficient transmission of RSV antibodies from babies to their mothers.

A focussed library of pyridyl and 2-hydroxyphenyl chalcones were synthesized and tested for growth inhibitory activity against Mycobacterium tuberculosis H37Rv, and normal and cancer breast cell lines. Pyridyl chalcones bearing lipophilic A-ring, e.g., dichloro-phenyl- (14), pyrene-1-yl (20)- and biphenyl-4-yl (21) moieties, were found to be the most potent of the series inhibiting the growth of M. tuberculosis H37Rv with IC90 values ranging from 8.9 – 28 µM. Aryl chalcones containing a 3-methoxyphenyl A-ring and either p-Br-phenyl (25) or p-Cl- phenyl (26) B-rings showed an IC90 value of 28 μM. Aryl-chalcones were generally less toxic to HepG2 cells compared to pyridyl-chalcones. Dose-dependent antiproliferative activity against MDA468 cells was observed for trimethoxy-phenyl (16) and anthracene-9-yl (19) pyridyl-chalcones with IC50 values of 0.7 and 0.3 µM, respectively. Docking studies revealed that chalone 20 was predicted to bind to the M. tuberculosis protein tyrosine phosphatases B (PtpB) with higher affinity compared to a previously reported PtpB inhibitor.

Bicuspid Aortic valve Stenosis (BCAS) with an incidence of 3% of the world's population constitutes the most frequent Congenital Heart Disease whereas all other subtypes collectively constitute 1% of all life births. Most and the extreme severity spectrum of valvular Aortic Stenosis (VAS) in adulthood are cases of congenital bicuspid aortic valve. It seems that the congenital occurrence of the bicuspid aortic valve is a major prerequisite of the pathological calcifications and fibrosis seen in adults. Those progressive changes can't be explained by sole hemodynamic destructive forces but other factors inherent to the bicuspid valve tissue are also contributing. Therapeutic options of (VAS) are relatively stagnant and have been limited to invasive and surgical options. Prudent observation of the risk factors of some CHDs including BCAS was the epigenetic effect of planetary electromagnetic field on the delicate process of endothelial mesenchymal transformation (EndoMT). The incidence of BCAS is progressively increasing as we depart north from the equator to the north pole. A deeper understanding of the cellular and molecular mechanisms underlying EndoMT in congenital as well as acquired heart diseases is expected to open a new era in reversing heart disease in the human species. The potential to manipulate EndoMT and its signaling pathways or to reverse it with external electromagnetic power is an exciting therapeutic option for a wide array of congenital and acquired heart diseases including bicuspid aortic stenosis (BCAS) in the human species.

Rationale: A wide range of bioactive phytoconstituents from edible fungi, especially therapeutic mushrooms, have the potential to improve health, especially in the case of chronic illnesses. Known for its anti-cancer qualities, ganoderma lucidum has been the focus of multiple clinical studies. Existing systematic reviews imply that more clinical research and methodological refinement are necessary to support its usage as a primary cancer treatment.Objective: The objective of this research is to evaluate the anti-cancer potential of the bioactive phytoconstituents found in mushrooms, specifically Ganoderma lucidum, in a thorough manner. The aim is to conduct a critical assessment of the extant literature, pinpoint any deficiencies, and offer valuable perspectives on the methodological excellence and research requirements in this domain.Method(s): A thorough analysis of published clinical trials was carried out, with a focus on studies that examined the effectiveness of Ganoderma lucidum and other products derived from mushrooms in treating cancer. The study's design, stated results, and methodological rigor were all carefully examined to evaluate the validity and reliability of the conclusions.Results: Despite Ganoderma lucidum's potential against cancer, the evaluation finds insufficient data to support its widespread usage as a primary cancer treatment. Products made from mushrooms, which are frequently sold as dietary supplements, have anti-cancer claims. But since pre-approval is not required under US regulatory framework in order to market food supplements, these statements raise questions regarding their scientific validity.Conclusions/Recommendations: The study's result emphasizes the necessity of more methodological advancement in clinical studies examining Ganoderma lucidum's potential as an anti-cancer agent. It also highlights the significance of thorough scientific validation for goods made from mushrooms that make health claims. To establish accurate claims regarding their anti-cancer effects, recommendations include developing technological methods for the purification of bioactive substances and undertaking thorough studies of mushroom products from different geographical regions. The goal of this thorough analysis is to direct future investigations and support the ethical promotion and use of products derived from mushrooms for medical purposes.

Discoloration of endodontically treated teeth poses a significant aesthetic concern for many individuals, impacting their confidence and self-image. Ceramic veneers have emerged as a popular solution for the aesthetic restoration of such teeth. This paper explores the role of ceramic veneers in addressing tooth discoloration, examining their efficacy, durability, and aesthetic outcomes, via a clinical case. Various factors influencing the selection of ceramic veneers as a treatment modality, including shade matching, preparation techniques, and adhesive bonding, are discussed. Additionally, advancements in materials and techniques have enhanced the versatility and aesthetic appeal of ceramic veneers, making them a valuable option for achieving natural-looking and durable aesthetic restorations in individuals with discolored endodontically treated teeth.

The neurologic effects of long-COVID are a therapeutic challenge. This article discusses the immunologic manifestations of long-COVID and proposes a non-androgenic, synthetic analog of dehydroepiandrosterone (DHEA), bromo-epi-androsterone (BEA), as a candidate for clinical trials for this exigent collection of neurologic conditions. Moreover, because there is an association between long-COVID and Alzheimer’s disease, the evaluation of BEA in long-COVID may serve as a proxy for its effect on neuroinflammatory biomarkers of Alzheimer’s disease that are shared with long-COVID.

Glycogen storage disorders (GSDs) are a group of inherited metabolic disorders characterized by defects in enzymes involved in glycogen metabolism. Deficiencies in enzymes responsible for glycogen breakdown and synthesis can impair mitochondrial function. For instance, in GSD type II (Pompe disease), acid alpha-glucosidase deficiency leads to lysosomal glycogen accumulation, which secondarily impacts mitochondrial function through dysfunctional mitophagy, which disrupts mitochondrial quality control generating oxidative stress. In GSD type III (Cori disease), the lack of the debranching enzyme causes glycogen accumulation and affects mitochondrial dynamics and biogenesis by disrupting the integrity of muscle fibers. Malfunctional glycogen metabolism can disrupt various cascades thus causing mitochondrial and cell metabolic dysfunction through various mechanisms. These dysfunctions include altered mitochondrial morphology, impaired oxidative phosphorylation, increased production of reactive oxygen species (ROS), and defective mitophagy. The oxidative burden typical of GSDs compromises mitochondrial integrity and exacerbates the metabolic derangements observed in GSDs. The intertwining of mitochondrial dysfunction and GSDs underscores the complexity of these disorders and has significant clinical implications. GSD patients often present with multisystem manifestations, including hepatomegaly, hypoglycemia, and muscle weakness, which can be exacerbated by mitochondrial impairment. Moreover, mitochondrial dysfunction may contribute to the progression of GSD-related complications, such as cardiomyopathy and neurocognitive deficits. Targeting mitochondrial dysfunction thus represents a promising therapeutic avenue in GSDs. Potential strategies include antioxidants to mitigate oxidative stress, compounds that enhance mitochondrial biogenesis, and gene therapy to correct the underlying mitochondrial enzyme deficiencies. Mitochondrial dysfunction plays a critical role in the pathophysiology of GSDs. Recognizing and addressing this aspect can lead to more comprehensive and effective treatments, improving the quality of life of GSD patients. This review aims to elaborate on the intricate relationship between mitochondrial dysfunction and various types of GSDs. The review presents challenges and treatment options for several GSD

Neuromorphic computing, a brain inspired non-Von Neumann computing system, addresses the challenges posed by the Moore’s law memory wall phenomenon. It has the capability to increasingly enhance performance while maintaining power efficiency. Neuromorphic chip architecture requirements vary depending on the application and optimizing it for large-scale applications remains to be a challenge. Neuromorphic chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, and on-device learning. Widely used spiking neuron models include the Hodgkin-Huxley Model, Izhikevich model, integrate-and-fire model and spike response model. Hardware implementation platforms of the chip follow three approaches: analog, digital, or a combination of both. Each platform can be implemented using various memory topologies which interconnects with the learning mechanism. Current neuromorphic computing systems typically use the unsupervised learning spike timing-dependent plasticity algorithms. However, algorithms such as voltage-dependent synaptic plasticity have the potential to enhance performance. This review summarizes the potential neuromorphic chip architecture specifications and highlights which applications they are suitable for.

Previous studies on the water-energy nexus mainly focused on the calculation and comparison of resource consumption at the national or regional level, lacking interprovincial sector-sector transfer analysis. In this study, the intensity of water and energy consumption of various sectors in China were calculated, the key nodes and paths of resource networks were identified, and countermeasures for resource conservation were proposed from the new perspective of the “dual saving” and “bidirectional saving” of water and energy. The results showed that the metallurgical industry (Me) in Jiangsu and the chemical industry (Ch) in Hebei and Jiangsu had high node strength in the water and energy network and were key sectors in China with “dual saving” effects of water and energy. The construction industry, Ch, Me in Jiangsu, electricity and hot water production and supply industry in Beijing, and Me in Hebei had high node strength in the water-related energy network and energy-related water network, significantly supporting the “bidirectional saving” effect of water and energy in China. The electrical equipment industry (El) in Jiangsu→El in Zhejiang, El in Zhejiang→El in Shanghai frequently appeared in key paths, which could effectively reduce the resource consumption of the entire network.

Background: Heart failure (HF) remains a challenging healthcare issue, necessitating innovative therapies like cardiac resynchronization-defibrillation therapy (CRT-D). However, defining CRT-D responders lacks uniformity, impeding effective clinical evaluation. This study explores diverse CRT-D responder definitions, encompassing functional, echocardiographic, and laboratory criteria. Materials & Methods: A single-center study involving 132 CRT-D patients scrutinized responder criteria, including NYHA stage, LVEF increase, and proBNP decrease. Statistical analyses, such as Kaplan–Meier curves and Cox hazard regression, were employed to evaluate responder characteristics and survival outcomes. Results: Responder rates varied across criteria, revealing nuanced patient profiles. CRT-D responders, defined by NYHA decrease, LVEF increase, or proBNP decrease, exhibit improved survival rates after 2 and 3 years (p < 0.050). Young age, absence of recent myocardial infarction, and normal right ventricular, echocardiographic parameters emerge as predictors for positive response. In part, drug-based HF therapy correlates with increased responder rates. Cox regression identified LVEF ≥ 5% and proBNP decrease ≥ 25% as independent predictors of extended survival. Conclusions: CRT-D responder definitions exhibit considerable variability, emphasizing the need for a nuanced, patient-centered approach. Factors like right ventricular function, drug therapy, atrial fibrillation, and renal function influence responses. The study enriches our understanding of CRT-D response, contributing to the foundation for personalized HF management.

This article briefly shows how particle’s moving at escape velocity can be described in a quantum mechanical frameworks, this is achieved by deriving quantum mechanical equations that puts escape velocity into consideration. The equations are derived from Hamiltonians and Hamiltonian operators formulated from classical kinetic and potential terms which are then quantized. Equations were also presented for specific situations of the particle at escape velocity. Situations such as free-particles at escape velocity, Self-gravitating situations of a charged or neutral particle even while free of the gravitational influence of the large massive body due to its escape velocity, leaving only the self-gravitation as the only gravitational influence on the particle which in the case of a massless, charge less particle reduces back to the equation of free-particle at escape velocity. Other very brief expositions on trajectories and more are also present in this brief article.

This research focuses on developing a Student Performance Evaluation System to enhance effectiveness and oversight in higher education. The system aims to address challenges faced by instructors in evaluating student performance using a distributed system approach. Agile methodology is employed in development, and database replication techniques are applied for data synchronization. System features include registration, assessment form creation, assessment submission, statistical visualization, and access to assessment history. The system is designed to improve efficiency and transparency in the assessment process. Key features of the system include registration process, assessment form creation, assessment submission, statistical visualization, and access to assessment history. Its goal is to enhance efficiency and transparency throughout the assessment process. The article details the system workflow and the roles played by Admins, Instructors, and students in system usage. Additionally, it discusses the back-end development model and pathways, as well as the performance testing results. Performance testing indicates that the system achieves high throughput of up to 21.55 requests per second, with an average response time of only 34 milliseconds and an error rate of zero percent. These results affirm that the system efficiently handles user loads and nearly instantaneously responds to requests, providing an optimal user experience in the context of student performance evaluation in higher education.

This study analyzed the dynamic spatial and temporal distribution of natural disasters in Traditional Authorities Ngabu and Lundu in Chikwawa District in the Lower Shire Valley. The study assessed the vulnerability of people to disasters, evaluated the value of losses and damages incurred when disasters strike and to locate disaster hotspots. The mixed design that embraces qualitative and quantitative approaches was used. Data collection methods such as in-depth interviews, Focus Group Discussion, documentary review, structured interview and Participatory Geographical Information Systems (P-GIS) were employed. The study revealed that farming with 64%, 37% and 76% done before, during and after disasters respectively, was the main adaptive capacity to floods and drought in T/A Ngabu. In T/A Lundu, community members also valued farming with 58%, 55% and 70% done before, during and after disasters respectively. The study found that Traditional Environmental Knowledge Systems (TEKS) were highly valued in T/As Ngabu with 90% and Lundu 81%. The study also unveiled those disasters led to losses of human lives and livestock and damages to properties, environment and critical infrastructures. However, some community members still remained in the disaster-prone areas for farming, fishing, protecting the chieftaincy and ancestral reasons. The study also showed that CBDRM was highly adopted. However, disasters continued to occur and losses were increasing. Generally, CBDRM could be effective through multisectoral approach and good legal institutional framework. Therefore, the research recommends that further studies similar to this, should use GIS in assessing the efficacy of CBDRM approaches to produce hazard maps that will be used as models for disaster risk reduction in Chikwawa District in the Lower Shire Valley.

Background: Leishmaniasis is a significant global public health issue that is caused by parasites from Leishmania genus. With limited treatment options and rising drug resistance, there is a pressing need for new therapeutic approaches. Molecular chaperones, particularly Hsp90, play a crucial role in parasite biology and are emerging as promising targets for drug development. Objective: This study evaluates the efficacy of 17-DMAG in treating BALB/c mice from cutane-ous leishmaniasis through in vitro and in vivo approaches. Materials and Methods: We assessed 17-DMAG cytotoxic effect on bone marrow-derived mac-rophages (BMMΦ) and its effects against L. braziliensis promastigotes and intracellular amastigotes. Additionally, we tested the compound's efficacy in BALB/c mice infected with L. braziliensis via intraperitoneal administration to evaluate the reduction of lesion size and de-crease of parasite load in the ears and lymph nodes of infected animals. Results: 17-DMAG showed selective toxicity [selective index=432) towards Leishmania amastigotes, causing minimal damage to host cells. The treatment significantly reduced lesion sizes in mice and results in parasite clearance from ears and lymph nodes. It also diminished in-flammatory responses and reduced the release of pro-inflammatory cytokines (IL-6, IFN-g, TNF) and the regulatory cytokine, IL-10, underscoring its dual leishmanicidal and anti-inflammatory properties. Conclusion: Our findings confirm the potential of 17-DMAG as a viable treatment for cutaneous leishmaniasis and support further research into its mechanisms and potential applications against other infectious diseases.

Ambiguity exists over treatment and surveillance strategies after endoscopic submucosal dissec-tion (ESD) for esophageal squamous cell neoplasia (ESCN) with unfavorable histologic features. This study investigated the long-term outcomes of ESD in high-risk ESCN patients. We retro-spectively included early ESCN patients treated with ESD at two medical centers in Taiwan be-tween August 2010 and December 2023. Demographic, endoscopic, and pathological data were collected. Among 146 patients (mean age 59.17 years) with 183 lesions, 73 (50%) had a history of head and neck cancer (HNC). En bloc and R0 resection were achieved in 100% and 95.6% of the lesions, respectively. The 5-year overall survival (OS), disease-specific survival (DSS) and local recurrence rates were 42.7%, 94.7% and 11%. R0 resection was significantly associated with re-currence in univariate analysis (adjusted OR: 0.23, 95% CI: 0.05-0.99, P=0.05). Alcohol abstinence was independently associated with lower recurrence (adjusted OR: 0.20, 95% CI: 0.08-0.48, P

As the understanding of natural gas hydrates as a vast potential resource deepens, their importance as a future clean energy source becomes increasingly evident. However, natural gas hydrates tend to secondary generation during extraction and transportation, leading to safety issues such as pipeline blockages. Consequently, developing new and efficient natural gas hydrate inhibitors has become a focal point in hydrate research. Kinetic Hydrate Inhibitors (KHIs) offer an effective solution by disrupting the nucleation and growth processes of hydrates without altering their thermodynamic equilibrium conditions. This paper systematically reviews the latest research progress and development trends of KHIs for natural gas hydrates, covering their development history, classification, and inhibition mechanisms. It particularly focuses on the chemical properties, inhibition effects, and mechanisms of polymer inhibitors such as polyvinylpyrrolidone (PVP) and polyvinylcaprolactam (PVCap). Studies indicate that these polymer inhibitors provide an economical and efficient solution due to their low dosage and environmental friendliness. Additionally, this paper explores the environmental impact and biodegradability of these inhibitors, offering guidance for future research, including the development, optimization, and environmental assessment of new inhibitors. Through a comprehensive analysis of existing research, this work aims to provide a theoretical foundation and technical reference for the commercial development of natural gas hydrates, promoting their safe and efficient use as a clean energy resource.

Due to the manufacturing process of Additive Manufacturing (AM) Parts the mechanical properties cannot be predicted. The scope of the present work is to propose a predictive tool for the mechanical properties of AM parts through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). The Adaptive Neuro-Fuzzy Inference System (ANFIS) combines neural networks with fuzzy logic to generate a mapping between the inputs and the output. Real data experiments from different AM technologies and materials compose the initial dataset so a detailed investigation is conducted to gain knowledge of how different parameters affect the UTS, the elongation and the Young modulus of each experiment. Experimental data were also use to train, check and verify the methodology. The method is tested for FFF technology, PLA material and 3 point bending stress, with interesting results.

Camping offers invaluable experiences in nature exploration, but the expense of acquiring camping gear can deter potential enthusiasts. To mitigate this, camping gear rental services emerge as a practical solution, promoting accessibility and reducing financial barriers. However, existing rental systems often encounter issues such as limited payment options, inconvenient pickup/return processes, and opaque management. In response, this paper introduces "Rent-n-Play," a web-based camping gear rental platform built on Laravel framework. It enables cashless transactions, offers pickup/delivery services, and employs a real-time admin system for transparent management. User feedback guided platform enhancements, including review systems and responsive support. The "Rent-n-Play" platform enhances user experience, fosters trust, and promotes sustainable camping practices, thus contributing to a more accessible and enjoyable camping culture.

Artificial intelligence technologies based on deep convolutional neural networks and large lan-guage models have made significant breakthroughs in many tasks such as image recognition, target detection, semantic segmentation and natural language processing, but also face the problem of contradiction between the high computational capacity of the algorithms and the limited deployment resources. Quantization, which converts floating-point neural networks into low-bit-width integer networks, is an important and essential technique for efficient deploy-ment and cost reduction in edge computing. This paper analyze various existing quantization methods, showcase the deployment accuracy of advanced techniques, and discuss the future challenges and trends in this domain.

Recent omics technologies have revolutionized the study of molecular plant responses to air pollution, overcoming previous limitations. This review synthesizes the latest advancements in molecular plant responses to major air pollutants, emphasizing ozone (O3), nitrogen oxides (NOX) and particulate matter (PM) research. These pollutants induce stress responses common to other abiotic and biotic stresses, including the activation of reactive oxygen species (ROS)-scavenging enzymes and hormone signaling pathways. New evidence showed the central role of antioxidant phenolic compound biosynthesis via phenylpropanoids pathway in air pollution stress responses. Transcription factors like WRKY, AP2/ERF, and MYB, which connect hormones signaling to antioxidant biosynthesis, were also affected. To date, research has predominantly focused on laboratory studies analyzing individual pollutants. This review highlights the need for comprehensive field studies and the identification of molecular tolerance traits, which are crucial for the identification of tolerant plant species aimed to the development of sustainable Nature Based Solutions (NBS) to mitigate urban air pollution.

Arsenic trioxide (ATO) has been shown to inhibit pancreatic cell growth in vitro and to promote the inhibitory effects of gemcitabine on pancreatic cancer in vivo. However, the high toxicity of ATO associated with the required high doses and indiscriminate targeting has limited its clinical application. This study aimed to determine whether coupling arsenic to a tumor homing peptide would increase the inhibitory potency against pancreatic cancer cells. The effects of this peptide-linked arsenic compound and ATO on pancreatic cancer growth were tested in vitro and in a mouse model. The data demonstrated that the peptide-linked arsenic compound inhibited pancreatic cancer cell growth more potently with IC50 values 10 times lower than ATO. Like ATO, the peptide-linked compound induced cell death and cell cycle arrest. More importantly, the peptide-linked arsenic compound inhibited pancreatic cancer growth in mice and enhanced the inhibitory effect of gemcitabine on pancreatic cancer growth at 2 times lower molar concentration than ATO. These results indicate that the peptide-linked arsenic compound inhibited pancreatic cancer more potently than ATO and suggest a potential clinical use for the combination of gemcitabine with the peptide-linked arsenic compound for the treatment of pancreatic cancer.

Arthropods, the most abundant group in ecosystems, play crucial roles in human production. Soil physicochemical properties reflect soil health and are linked to arthropods through cascading effects. Gravel-sand mulching, commonly practiced in dry farming regions, initially aids in water retention and temperature regulation. However, long-term cultivation can lead to land degradation and habitat fragmentation. This study aimed to investigate field systems using multiple comparison, linear, and nonlinear correlation analyses to explore the effects of different tillage durations on soil properties and arthropod diversity under gravel-sand mulching. Results indicate that arthro-pod diversity was lower while the fields maintained better soil fertility than the desert steppe. Both soil fertility and arthropod diversity declined significantly with increased years of cultivation. Mantel tests revealed no significant response of desert steppe arthropods to the altered soil properties of gravel-sand mulched fields. However, the Generalized Additive Model analysis indicated significant linear correlations between arthropod occurrence and total nitrogen (positive), available phosphorus, and water content (negative). Arthropod abundance showed significant nonlinear correlations with total potassium, alkali hydrolyzable nitrogen, and organic matter (OM), with OM changes having the most pronounced impact. Thus, gravel-sand mulching can maintain soil fertility over time but at the expense of local arthropod diversity.

The statistical technique, the multiple regression model, is employed to analyze the relationship between the dependent variable and several independent variables. The multicollinearity problem is one of the assumptions in the multiple regression model that occurred in the relationship among independent variables. The ordinal least square is the standard method to evaluate parameters in the regression model, but the multicollinearity problem affects the unstable estimator. The Liu regression is proposed to approximate the Liu estimators based on the Liu parameter to overcome multicollinearity. For this paper, we have proposed the modified Liu parameter to estimate the biasing parameter in scaling options to compare the ordinal least square estimator with two modified Liu parameters and six standard Liu parameters. The performance of the modified Liu parameter is considered with the generating independent variables from the multivariate normal distribution in the Toeplitz correlation pattern as the multicollinearity data, where the dependent variable is obtained from the independent variable multiplied with a coefficient of regression and with the error from the normal distribution. The mean absolute percentage error is computed as an evaluation criterion of estimation. For application, the Hepatitis C patients dataset is a real dataset to investigate the benefit of the modified Liu parameter. Through the simulation and real dataset, it can be seen from the results that the modified Liu parameter outperforms the other Liu parameters and the ordinal least square estimator. It can recommend the user for estimating parameters by using the modified Liu parameter when the independent variable exits the multicollinearity problem.

The effect of Al or Cu content on the microstructure and mechanical properties of continuous casting and extrusion Zn alloys has been studied by room temperature tensile test, X-ray diffraction, and scanning electron microscope. With the increase of Al content, the microstructure of continuous casting and extrusion Zn alloys slightly coarsens and the lamellar eutectic structure increases. The changes in the above structural factors result in a slight decrease in strength and a significant increase in elongation of Zn-Al alloys. The strength of Zn alloys increases as the Cu content increases, due to the increased content and size of the second phase in the Zn alloys. This means that the mechanical properties of Zn alloys can be adjusted by continuous casting and extrusion process, and the improvement of equipment capacity can improve the structure and morphology of the alloys.

Aquaporins (AQPs) are a family of integral membrane proteins that selectively transport water and glycerol across the cell membrane. Because AQPs are involved in a wide range of physiological functions and pathophysiological conditions, AQP-based therapeutics may have broad potential for clinical utility, including disorders of water and energy balance. However, AQP modulators have not yet been developed as suitable candidates for clinical applications. In this study, to identify potential modulators of AQPs, we screened 32 natural products by measuring the water and glycerol permeability of mouse erythrocyte membranes using a stopped-flow light scattering method. None of the tested natural compounds substantially affected osmotic water permeability. However, several compounds considerably affected glycerol permeability. Stichoposide C increased the glycerol permeability of mouse erythrocyte membranes, whereas rhizochalin decreased it at nanomolar concentrations. Immunohistochemistry revealed that AQP7 was the main aquaglyceroporin in mouse erythrocyte membranes. We further verified the effects of stichoposide C and rhizochalin on aquaglyceroporins using human AQP3 expressing keratinocyte cells. Stichoposide C, but not stichoposide D, increased AQP3-mediated transepithelial glycerol transport, whereas the peracetyl aglycon of rhizochalin was the most potent inhibitor of glycerol transport among the tested rhizochalin derivatives. Collectively, stichoposide C and the peracetyl aglycon of rhizochalin might function as modulators of AQP3 and AQP7, and suggest the possibility of these natural products as potential drug candidates for aquaglyceroporin modulators

Synthetic Aperture Radar has been extensively used in numerous fields and can gather a wealth of information about the area of interest. This large-scene data-intensive technology puts a high value on automatic target recognition (ATR) which can free the utilizers and boost the efficiency. Recent advances in artificial intelligence have made it possible to create a deep learning-based SAR ATR that can automatically identify target features from massive input data. In the last 6 years, intensive research has been conducted in this area, however, most papers in the current SAR ATR field used recurrent neural network (RNN) and convolutional neural network (CNN)-varied models to deepen the regime’s understanding of the SAR images. To equip SAR ATR with updated deep learning technology, this paper tries to apply a lightweight vision transformer (LViT)-based model to classify SAR images. The entire structure was verified by an open- accessed SAR data set and recognition results show that the final classification outcomes are robust and more accurate in comparison with referred traditional network structures without even using any convolutional layers.

This research aims to explore and analyze the critical success factors (CSFs) that increase the likelihood of successful implementation of tourism PPP projects in China. As an important step toward developing such a protocol, this study identifies and examines 22 critical success factors for tourism PPPs based on a systematic research approach, which includes a rigorous literature review, and semi-structured interviews. A questionnaire survey was conducted to evaluate the relative importance of these 22 CSF. The results show that the top three critical success factors for China’s tourism PPPs are: adequate project evaluation and demonstration; political support and stability; clear and stable policy documents on PPP. Factor analysis indicated that the 22 CSFs could be grouped into five principal success factor groups. These insights are expected to profoundly influence the development of policies related to tourism PPPs, providing a structured framework to enhance the efficiency and success of these ventures.

Gastrointestinal inflammation is a key feature of several common disorders, such as cancer treatment–induced mucositis and ulcerative colitis. These conditions lead to severe symptoms, significantly affect the quality of life, and are often not well-managed. Research has suggested that the activation of toll-like receptor 4 (TLR4) plays a critical role in the inflammatory signalling pathways of the intestinal tract. Consequently, inhibiting TLR4 is proposed as a promising strategy to treat intestinal inflammation. Despite this, few studies have explored the potential of TLR4 antagonism as a therapeutic approach for intestinal inflammation. This review examined existing literature and evaluated the effects of TLR4 in various models of intestinal inflammation, including in vitro (lab-based), ex vivo (outside the organism), and in vivo (within living organisms) studies. The findings indicate that TLR4 may indeed have the potential to treat inflammation. However, only a small number of studies have directly investigated the use of TLR4 antagonists for treating intestinal inflammation. These preliminary results highlight the need for further research into the target of TLR4 for UC.

Background/Objectives: An age-associated increase in serum levels of sex hormone binding globulin (SHBG) is considered to be one of the factors leading to the development of male hy-pogonadism. Aim of the study: to study the relationship between the concentration of sex hor-mone-binding globulin in the blood serum and the age of the man, the level of total and free testosterone. A single-center retrospective cross-sectional study was conducted. The results of laboratory tests of 162 male patients aged from 18 to 56 years (median - 34 years) were analyzed. Methods: The level of total testosterone and sex hormone binding globulin (SHBG) was deter-mined by chemiluminescent immunoassay. The level of free testosterone was determined by the calculation method according to Vermeullen. Statistical analysis was performed using SPSS Sta-tistics v23. Results: Correlation analysis did not reveal a significant relationship between age and SHBG levels, as well as between age and total testosterone levels. Serum SHBG concentration showed a significant (p

This study uses machine learning to identify key factors influencing the cost-effectiveness of over-the-counter (OTC) medications. By developing a novel cost-effectiveness rating (CER) from user ratings and prices, we analyzed data from Amazon. The findings indicate that FSA/HSA eligibility, symptom treatment range, safety warnings, special effects, active ingredients, and packaging size significantly impact cost-effectiveness across cold, allergy, digestion, and pain relief medications. Medications eligible for FSA or HSA funds, treating a broader range of symptoms, and having smaller packaging are perceived as more cost-effective. Cold medicines with safety warnings were found to be cost-effective due to their lower average price and effective ingredients like phenylephrine and acetaminophen. Allergy medications with kid-friendly features showed higher cost-effectiveness, and ingredients like calcium, famotidine, and magnesium boosted the cost-effectiveness of digestion medicines. These insights help consumers make informed purchasing decisions and assist manufacturers and retailers in enhancing product competitiveness. Overall, this research supports better decision-making in the pharmaceutical industry by highlighting factors that drive cost-effective medication purchases.

This study explores the potential of digital technologies for preserving cultural heritage through the case study of the Raya Gopura, an incomplete monument in Melukote, India. Employing a combination of terrestrial photogrammetry, drone-based photogrammetry and terrestrial laser scanning, the research team captured highly detailed 3D models of the structure. This non-destructive approach offers a comprehensive digital record for future research and preservation. Furthermore, for the conjecture for the Raya Gopura two possible purposes: an entrance to a planned fort or a gateway to the temple are perceived through the collective intelligence (CI) approach. These insights inform a conjectural architectural drawing of the potentially completed monument. The 3D models hold significant potential for various applications, including tourism, education, and architectural research, with the potential for immersive experiences through VR and AR technologies. The study demonstrated the effectiveness of digital tools in heritage preservation, not only safeguarding the Raya Gopura but also offering valuable in-sights for future research and public engagements.

The aim of this article is to identify the best available evidences analyzing liver samples, normall and pathological. In the study were used liver samples, collected during necropsy, from healthy patients and from patients diagnosed with cirrhosis .This mentioned, are known as routinally practice for hepatitis C diagnosis, which conduct to hepatic cirrhosis. Were made permanent preparations and used Hematoxylin–Eosin staining and other special stainings for structural observations at optical microscope using lens with differents magnifications, such us x10 and x40.

Brittle fracture of iron and steel above twinning temperatures are caused by cementite grain boundary wall cracks. These were revealed by an Atomic Force Microscope. At temperatures below the Ductile-Brittle Transition, cracks must propagate longitudinally within the cementite walls until the stress is sufficient high for the cracks to propagate across ferrite grains. Calculations using these concepts correctly predict the stress and temperature at the Ductile-Brittle Transition required for fracture to occur. At temperatures above the Ductile-Brittle Transition for hypoeutectoid ferritic steels, dislocations must fracture these walls transversely for plastic deformation to continue. This is responsible for the upper yield point at the elastic limit in these steels followed by a large drop in stress to the lower yield point. Here the walls surround completely all of the grains. Where the walls are segmented such as in iron, dislocations can pass around the walls resulting in a gradual change from elastic to plastic deformation. The Cottrell atmosphere theory of yielding is not supported experimentally. It was the best available until later experiments including those using the Atomic Force Microscope were obtained. Methods are presented here giving yield strength versus temperature and also the parameters for the Hall-Petch and Griffith equations.

The issue of sufficient nutrition and appropriate distribution in school lunch programs presents a significant challenge that must be addressed. Many schools struggle to provide food that meets all of their students’ nutritional needs. Additionally, inconsistencies in nutritional information and administrative difficulties often hinder the efficient operation of these programs. Developing technology to better manage school lunch programs is critical to addressing these issues. The goal of this project is to create a web application that ensures the fair distribution of nutritional information while also increasing the efficiency of lunch program administration. The methodology used in this research is Agile. Agile approaches are employed throughout the development process, allowing for rapid iteration and responsiveness to user feedback. The end result is a web tool called Nourishify, designed to streamline the management and dissemination of nutritional information. Implementing Nourishify led to substantial improvements in access to nutrition information and operational efficiency, making it an innovative solution for school lunch programs. This project demonstrates that integrating technology can significantly enhance the administration and transparency of nutrition programs, ultimately benefiting students' health and well-being. Moreover, Nourishify’s user-friendly interface and real-time data capabilities ensure that all stakeholders, including parents, administrators, and nutritionists, can easily collaborate and access crucial information.

Geographic information systems (GIS) are critical for mapping the geographical distribution of illness prevalence, disease transmission patterns, and spatially modeling environmental elements of disease occurrence. This study was purposively to map and cluster five diseases such as cholera, diabetes, malaria, asthma and hypertension using GIS techniques in Southern Malawi. Getis-Ord Gi* statistic was used to determine spatial distribution of diseases. Data were acquired from Malawi Demographic Health Survey (DHS) and assessed geographically using spa-tial-statistical analytical techniques, including the kernel and hotspot models. These models were developed to investigate and exhibit the dissimilar patterns of the selected diseases, as well as to depict places of high concentration, and they may be useful in understanding local patterns of diseases and their geographical correlations. The study found that asthma is high in Mangochi, Balaka, Blantyre, Thyolo, Mulanje, Chikwawa and Nsanje districts. It was also revealed that hypertension patients were highly concentrated in Blantyre, Thyolo, Chikwawa, Zomba, Mangochi and Nsanje Districts. Mangochi, Phalombe, Nsanje and Chikwawa are high risk areas of Malaria and diabetes is highest in Blantyre. Cholera is high in Mangochi, Balaka, Blantyre, Thyolo, Mu-lanje, Chikwawa and Nsanje districts.

To address the issue of reduced orbit estimation accuracy resulting from discrepancies between in-orbit measured geomagnetic field values and IGRF model values in autonomous orbit estimation of microsatellites based on geomagnetic field measurements, this study introduces an adaptive cubature H-infinity filtering algorithm. Firstly, it presents the low-altitude orbital dynamics model and geomagnetic field measurement model for microsatellites. Subsequently, within the adaptive cubature H-infinity filtering algorithm, a spherical simplex rule and a second-order Gaussian-Legendre quadrature criterion are employed to compute the spherical surface integral and radial integral. Additionally, a spherical simplex-radial quadrature rule is proposed to enhance the approximation accuracy of nonlinear Gaussian weighted integrals. Furthermore, by integrating extended H-infinity filtering based on game theory with the cubature quadrature rule, an inverse proportional relationship between constraint level and filtering innovation is established. This allows for adaptive adjustment of the constraint level to enhance robustness against model errors. Finally, through semi-physical simulation experiments, it is demonstrated that compared with traditional algorithms, the proposed algorithm improves autonomous orbit estimation position accuracy of microsatellites by 7.49%, while also enhancing velocity accuracy by 3.1%, thus validating its effectiveness.

Website information security has become a critical concern in the digital age. This article explores the evolution of website information security, examining its historical development, current practices, and future directions. The early beginnings from the 1960s to the 1980s laid the groundwork for modern cybersecurity, with the development of ARPANET, TCP/IP, public-key cryptography, and the first antivirus programs. The 1990s marked a transformative era, driven by the commercialization of the Internet and the emergence of web-based services. As the Internet grew, so did the range and sophistication of cyber threats, leading to advancements in security technologies such as the Secure Sockets Layer (SSL) protocol, password protection, and firewalls. Current practices in website information security involve a multi-layered approach, including encryption, secure coding practices, regular security audits, and user education. The future of website information security is expected to be shaped by emerging technologies such as artificial intelligence, blockchain, and quantum computing, as well as the increasing importance of international cooperation and standardization efforts. As cyber threats continue to evolve, ongoing research and innovation in website information security will be essential to protect sensitive information and maintain trust in the digital world.

We explored an extension of the Cauchy-Riemann equations to the algebra of treons, recently described by Alejandro Bermejo, whose elements are ordered 3-tuples. We leveraged the isomorphism between the algebra of treons and algebra B, and deduced the Cauchy-Riemann equations for the algebra of treons, establishing the necessary conditions for analyticity in this algebraic structure. This work significantly broadened our horizons in complex analysis and introduced new possibilities for applications across various fields of advanced mathematics.

The distribution of zakat, which is still carried out directly through mosques or zakat amil bodies, can hinder the payment process due to inaccurate calculations and difficulties in finding zakat amil institutions. In the digital era, information technology is very important in the management of zakat, alms, and infaq. Therefore, a zakat information management system was created. The e-Zakat program, developed using the waterfall method, plays an important role in five main aspects of zakat management: muzaki and mustahiq data collection, zakat collection, zakat distribution, mustahiq development, and reporting. The use of information technology in this program is expected to increase efficiency, accountability, transparency, as well as the reach of zakat and muzaki involvement.

- Early and accurate detection of pneumonia from chest X-ray images is crucial for timely treatment and patient care. In study presents a robust computational framework leveraging the CheXnet algorithm, a deep learning model, to identify pneumonia from chest X-rays. Developed a neural network architecture trained on a substantial dataset of chest X- ray images labeled as 'Normal' and 'Pneumonia'. The user- friendly interface of the system allows for seamless uploading of images, with the model subsequently providing predictions, a confusion matrix, accuracy metrics, and layer activations visualizations. The algorithm's performance was rigorously evaluated, with a confusion matrix indicating a high number of true positives and true negatives. Specifically, the model detected pneumonia with an accuracy of 92.47%, as shown by the results displayed in the system's interface. The precision, recall, and F1-score for pneumonia detection were 0.90, 0.96, and 0.93 respectively, with the overall system achieving an accuracy of 0.91 across the test set of 624 images. Visualizations of the neural network layers reveal the complex feature mappings the model uses to differentiate between normal and pathological findings. These insights could be vital for enhancing the interpretability of deep learning models in medical diagnostics. The Results indicate that the CheXnet-based neural network is a promising tool for automating pneumonia detection in clinical settings, which can assist radiologists in the diagnostic process and potentially improve patient outcomes through earlier intervention.

Because data authenticity and validity are becoming more and more of a concern in the digital age file security and integrity must be guaranteed. This paper investigates the application of HMAC-SHA256 which combines the Secure Hash Algorithm 256 (SHA256) with a private key for transmission-side source file authentication and verification. The study creates a prototype application to evaluate the efficacy of HMAC-SHA256 in securing file transfers by examining previous investigations on SHA and HMAC. The findings demonstrate that SHA256 produces unique file signatures which successfully guards against unwanted changes. Through the use of cryptographic hashes and secret keys to create distinct digital signatures that guarantee file authenticity HMAC improves security. The study shows that HMAC-SHA256 protects data integrity and detects tampering effectively. It does however stress how crucial it is to comprehend cryptographic concepts and apply these strategies with caution in order to prevent vulnerabilities. Overall the study finds that when properly implemented and updated HMAC-SHA256 greatly enhances file security in the digital age.

Psychiatric disorders, characterized by complex and fluctuating patterns of thoughts, emotions, and behaviors, can be effectively modeled and visualized using mathematical wave equations. This study explores the representation of three major psychiatric conditions— Psychosis, Bipolar Disorder and Obsessive Compulsive Disorder (OCD)—through the application of differential equations, damping factors, and noise perturbations. Psychosis is modeled using Lissajous figures with high-frequency components, damping, and noise, symbolizing the irregular and erratic thought patterns characteristic of this condition. The inclusion of random perturbations and varying frequencies illustrates the chaotic and unpredictable nature of psychotic episodes, while damping effects reflect the temporary reductions in symptom intensity. For bipolar disorder, a system of partial differential equations simulates the cyclical nature of manic and depressive episodes. The model captures high-energy manic phases with periodic disturbances and the subsequent transition to lower-energy depressive states through damping effects. This representation highlights the oscillatory behavior and recurring episodes inherent to bipolar disorder. OCD is represented with Circumferential Complex Equations in Mult directions. The resulting graphical representations provide intuitive visual insights into the dynamics of these psychiatric disorders. By leveraging wave equations and mathematical modeling, this approach offers a novel perspective on understanding and analyzing the complex behaviors associated with OCD, bipolar disorder and psychosis. This method not only aids in the conceptualization of psychiatric phenomena but also underscores the potential of mathematical tools in the study of mental health conditions.

Remimazolam was derived from its parent compound by adding an ester linkage into its structure, so that the drug becomes a substrate for ester metabolism. As a result, it undergoes organ independent ester hydrolysis, although the clinical benefits in terms of shorter recovery are not uniformly observed in clinical practice. Remimazolam is mainly tested in procedural sedation. In comparison to propofol, the current gold standard for procedural sedation, its proposed attractiveness is shorter wake-up times and a clear-headed recovery Its clear advantage over propofol is better hemodynamic stability, lack of pain on injection and availability of a reversal agent in the form of flumazenil. Data on patient and proceduralist’s satisfaction are lacking. Remimazolam is also used for induction and maintenance of general anesthesia in Japan (where it is approved for this purpose). In this scenario, it is not clear if it can achieve the same degree of lack of recall as propofol. The use of remimazolam in obstetrics, pediatrics and in high-risk population is an emerging area.

Amid increasing deforestation, surface fires reaching the forest understory are one of the primary threats to Amazonian ecosystems. Despite extensive research on post-fire mortality in woody species, literature on palm resilience to fire is scant. This study investigates post-fire mortality in four understory palms—Bactris maraja Mart., Chamaedorea pauciflora Mart., Geonoma deversa (Poit.) Kunth, Hyospathe elegans Mart., and juvenile individuals of Euterpe precatoria Mart. Objectives included: a) comparing post-fire responses; b) developing mortality models based on severity variables; and c) evaluating if diameter protects bud stems from heat flux. Conducted at the edge of an Ombrophylous Forest in Alto Juruá Acre, Brazil (7°45'S, 72°22'W), the experiment subjected 85 individuals to controlled burning in a 1 m² area near the stipe, with temperature sampling using K thermocouples. Results showed varying mortality rates among species, with larger stipe diameter correlating to reduced mortality. Canopy burning patterns significantly influenced mortality, especially for Euterpe precatoria. Species exhibited diverse regrowth capacities, with B. maraja showing the highest number and tallest basal resprouts. This study underscores post-fire plant mortality as a critical indicator of fire severity, essential for understanding its ecological impacts.

The era of digital technology has brought about a substantial shift in language development, especially for younger generations who have grown up surrounded by it. Language development is impacted by digital media exposure in both positive and bad ways. Positively, users can interact with a variety of linguistic communities across the globe by using digital platforms, which offer a wealth of options for linguistic exposure and practice. This exposure can promote imaginative language use, increase vocabulary, and improve reading comprehension. Since the brevity and informality of digital communication frequently stress speed over correctness, there are worries that digital media could encourage shallow communication, shorter attention spans, and a deterioration in professional writing skills. This paper aims to gather literature about language development in the digital age. In order to make sure that technical breakthroughs support language development, standards for the responsible and balanced use of digital media in educational settings must also be developed. Teachers can better support their students' language development in a constantly changing digital environment by remaining knowledgeable and flexible.

This research investigates precision enhancement in regional quasi-geoid refinement through ADS80 tri-linear array scanning stereoscopic imagery for aerial triangulation, coupled with GNSS gravity-potential modeling. Acquiring stereoscopic imagery and analyzing triangulation accuracy with the ADS80 camera, we performed this study over the Qinghai-Tibet Plateau's elevated, desolate terrain, collecting 593 GNSS points following high-precision stereoscopic imagery model. Utilizing 12 gravity satellite models, we computed geoid heights and China 1985 Yellow Sea elevations for 28 benchmarks and GNSS points, thereby refining the Qinghai Province Quasi-Geoid Model (QPQM) using geometric techniques. The findings reveal that POS-assisted ADS80 stereoscopic imagery yields high-precision triangulation with maximal horizontal and elevation accuracies of 0.083/0.116 cm and 0.053/0.09 cm, respectively, across five control point arrangements. The RMSE of normal heights for 1985, processed via these GNSS points, achieved decimeter precision. By applying error corrections from benchmarks to the 1985 elevation data from gravity satellites and performing weighted averaging, the precision of EGM2008, SGG-UGM-2, and SGG-UGM-1 models improved to 8.61 cm, 9.09 cm, and 9.38 cm, respectively, surpassing the QPQM by 9.22 cm to 9.99 cm. This research demonstrates that the proposed methods can significantly enhance the precision of regional quasi-geoid surfaces. Additionally, these methods offer a novel approach for rapidly establishing regional quasi-geoid models in the uninhabited areas of the Qinghai-Tibet Plateau.

The paper explores using generative artificial intelligence (AI) in financial market data management and forecasting. By integrating multiple data sources and feature extraction techniques, such as fundamental analysis, technical indicators, global economic data, and sentiment analysis, generative AI constructs a comprehensive deep learning framework that significantly enhances financial data management efficiency and market forecasts' accuracy. Specifically, technologies like generative adversarial networks (Gans) and variational autoencoders (VAE) demonstrate substantial data augmentation and model optimisation potential. The application value of the model in real-time market prediction and trading strategy optimization is further amplified through reinforcement learning methods.

We propose an $E_8 \otimes E_8$ unification of the standard model with pre-gravitation, on an exceptional Lie algebra-valued space . Each of the $E_8$ has in its branching an $SU(3)$ for space-time and an $SU(3)$ for three fermion generations. The first $E_8$ further branches to the standard model $SU(3)_c \otimes SU(2)_L \otimes U(1)_Y$ and describes the gauge bosons, Higgs and the left chiral fermions of the standard model. The second $E_8$ further branches into a right-handed counterpart (pre-gravitation) $SU(3)_{grav}\otimes SU(2)_R \otimes U(1)_g$ of the standard model, and describes right chiral fermions, a Higgs, and twelve gauge bosons associated with pre-gravitation, from which general relativity is emergent. We account for 208 out of the 496 degrees of freedom of $E_8 \otimes E_8$ and propose an interpretation for the remaining 288, motivated by the trace dynamics Lagrangian of our theory. We explain how the two copies of $SU(3)_{spacetime}$ together give rise to a 6D spacetime with signature $(3,3)$ which upon symmetry breaking gives rise to our 4D spacetime and to a second 4D anti-spacetime with flipped signature.

Today, legislative framework addresses the metal alloys’ corrosion issues, demanding the re-striction of use of the probable carcinogenic, mutagenic and toxic for reproduction at humans (CMG) metals, like nickel, cobalt and chromium, and demand to develop new biomaterials. Therefore, the aim of this research is to evaluate and compare ion release of standard dental alloys and their hypoallergenic equivalents. Six types of orthodontic alloy wires (nickel-titanium (NiTi), coated NiTi, stainless steel (SS), Ni-free SS, cobalt-chromium (CoCr), and titanium-molybdenum (TMA) were immersed into artificial saliva of pH 5.5 and 6.6. Release of metal ions was measured by inductively coupled plasma-mass-spectrometry (ICP-MS) after 3, 7, 14 and 28 days. Data analyses were done using analysis of variance, results with P

For college students, speaking skills are a priceless commodity that can solve a wide range of social, professional, and academic problems. Proficiency in oral communication allows students to express themselves coherently and convincingly in academic and workplace contexts, which improves their involvement in group projects, presentations, and class debates. This paper determines to collect literature to prove that speaking skills can be a panacea for college students in the classroom and the workplace. Having speaking skills is, in fact, a college student's greatest asset, providing a plethora of advantages that go beyond scholastic achievement to encompass personal and professional growth. These abilities should be prioritized in colleges and institutions, with thorough curricula incorporating technological integration, practice opportunities, and public speaking courses. More investigation is required to look into creative approaches to improving speaking abilities and helping students get past obstacles. In essence, emerging strong speaking skills prepare college students with a multipurpose device that improves various aspects of their lives, making it a true panacea.

Organosilicate glass (OSG) films are a vital component in modern electronic devices, with their electrical properties playing a crucial role in device performance. This comprehensive review systematically examines the influence of chemical composition, vacuum ultraviolet (VUV) irradiation, and plasma treatment on the electrical properties of OSG films. Through an extensive survey of literature and experimental findings, we elucidate the intricate interplay between these factors and the resultant alterations in electrical conductivity, dielectric constant, and breakdown strength of OSG films. Key focus areas include the impact of diverse organic moieties incorporated into the silica matrix, the effects of VUV irradiation on film properties, and the modifications induced by various plasma treatment techniques. Furthermore, we discuss the underlying mechanisms governing these phenomena, shedding light on the intricate molecular interactions and structural rearrangements occurring within OSG films under different environmental conditions. This review not only consolidates existing knowledge but also identifies gaps in understanding and suggests avenues for future research, thereby advancing our comprehension of OSG film behavior and paving the way for enhanced performance in next-generation electronic and photonic devices.

Oxidation of a mixture of nonlabelled (14N2H4) and 15N labeled (15N2H4) hydrazine in aqueous solutions produces 14N15N molecules, indicating the intermediate existence of N4H6 or N4H4 dimers with subsequent transfers of H atoms and splitting of lateral N-N bonds. To explain the key part of hydrazine oxidation reaction, the structures, thermodynamics, and electron characteristics of N4H4 molecules in aqueous solution are investigated at CCSD/cc-pVTZ level of theory. We have not found any spontaneous splitting of the bond between lateral nitrogen atoms in tetrazenes N4H4 during geometry optimization. The most probable N4H4 oxidation products are H2N-NH2 and N2, which are obtained by splitting the bond between central nitrogen atoms and so only 15N2 and 14N2 molecules are formed. Additionally, the formation of H3N-NH and N2 oxidation products is also preferred to structures without any N-N fissions. The formation of H2N=N and HN=NH reaction products is energetically less advantageous. Cyclo-N4H4 structures are stable, without any N-N fissions, but their very high Gibbs energies indicate their vanishing abundance in aqueous solution, so their involvement in hydrazine oxidation is highly improbable. Hydrazine oxidation to 14N15N molecules cannot be explained by tetrazene N4H4 intermediates.

Increasing nitrogen use efficiency (NUE) by improving agricultural practices, soil knowledge and implementing precision agriculture, is essential to reduce the overuse of fertilisers and increase nutrient retention. This study aimed to optimise N management in agriculture by establishing a critical N dilution curve (CNDC) and analysing variations in NUE and N nutrition index (NNI) among different crops under various treatments. Using a Bayesian model, the CNDC was determined as %Nc = 3.63 * PDM-0.71. The results presented that plant dry matter (PDM) and plant N content (PNC) varied significantly with crop type and sampling moments. Strong positive correlations are presented by PDM with N uptake (NUp) (0.89) and NNI (0.88), along with an inverse correlation with critical N concentration (-0.95). The study found that crops under irrigation conditions had higher NUp and higher NNI. This study provides valuable insights into the influence of key management decisions on the N nutritional status of annual ryegrass-based forage crops. The results highlight the critical role of accurate and conscious decision-making in improving NUE and crop yields, emphasising the complex interactions between biomass production and N dynamics in crops. The conclusions allow significant benefits to be realised, contributing to the sustainability of agricultural systems.

Digital weight-loss services (DWLSs) combining pharmacotherapy and health coaching have the potential to make a major contribution to the global struggle against obesity. However, there is currently no evidence to counter the widespread concern that these services enable large numbers of unsuitable patients to obtain Glucagon-like peptide-1 receptor agonists (GLP-1 RA). This study retrospectively analysed the rate at which the partner pharmacy network of Australia’s largest DWLS provider committed GLP-1 RA dispensing errors over a 6-month period. The analysis found that 191 (0.14%) of the 135131 dispensed Semaglutide orders contained an error. Non-compliant dose escalation (44.5%) and dispatch errors (32.5%) were the two most common error types. Most errors (89%) were deemed to have been of medium urgency, with 6.8% considered high urgency. A disproportionate number of high urgency cases were from wrong medication (31%) and general complaint errors (15%). A Chi-square test revealed a statistically higher dispensing error rate among orders to female patients (0.16%) compared to males (0.076%). The results lay an important foundation in digital pharmacy literature and suggest that comprehensive DWLSs have the potential to safely collaborate with third-party pharmacies in dispensing GLP-1 RAs.

This article examines the integration of artificial intelligence (AI) and robotic process automation (RPA) in financial accounting and management, underscoring their role in driving the digital transformation of corporate finance. It discusses the shortcomings of traditional financial processes and highlights the potential of AI and RPA technologies to enhance efficiency, accuracy, and cost-effectiveness. The paper also explores the limitations of RPA, such as its challenges in processing unstructured data and handling complex decision-making scenarios. Looking forward, it considers the future trends in AI and RPA, emphasizing the benefits of cloud technology in scaling automated systems and addressing associated challenges.

Wildfires have short- and long-term impacts on the geoenvironment, including the changes to biogeochemical and mechanical properties of soils, landfill stability, surface- and groundwater, air pollution, and vegetation. Climate change has increased the extent and severity of wildfires across the world. Simultaneously, anthropogenic activities—through the expansion of urban areas into wildlands, abandonment of rural practices, and accidental or intentional fire-inception activities—are also responsible for a majority of fires. This paper provides an overall review and critical appraisal of existing knowledge about processes induced by wildfires and their impact on the geoenvironment. Burning of vegetation leads to loss of root reinforcement and changes in soil hydromechanical properties. Also, depending on the fire temperature, soil can be rendered hydrophobic or hydrophilic and compromise soil nutrition levels, hinder revegetation, and, in turn, increase post-fire erosion and debris flow susceptibility of hillslopes. In addition to direct hazards, wildfires pollute air and soil with smoke and fire suppression agents releasing toxic, persistent, and relatively mobile contaminants into the geoenvironment. Nevertheless, the mitigation of wildfires’ geoenvironmental impacts does not fit within the scope of this paper. In the end, in no exhaustive way, some areas of future need for research are highlighted.

In contrast to conventional non-destructive testing (NDT) and non-destructive evaluation (NDE) methodologies, including radiography, ultrasound, and eddy current analysis, the coplanar capacitive sensing technique emerges as a novel and promising avenue within the field. This paper endeavors to elucidate the efficacy of coplanar capacitive sensing, also referred to as capacitive imaging (CI), within the realm of NDT. Leveraging extant scholarly discourse, this review offers a comprehensive and methodical examination of the coplanar capacitive technique, encompassing its fundamental principles, factors influencing sensor efficacy, and diverse applications for defect identification across various NDT domains. Furthermore, this review deliberates on extant challenges and anticipates future trajectories for the technique. The manifold advantages inherent to coplanar capacitive sensing vis-à-vis traditional NDT methodologies not only afford versatility in application but also underscore its potential for pioneering advancements in forthcoming applications.

Sustainable development has become the aspiration of many developing countries, including Ecuador. The introduction of Bachelor of Tourism studies at the Technical University of Manabí aligns with this interest, implying a challenge for teachers regarding the application of integrative and innovative teaching methods that promote the training of professionals with high scientific rigor, both in technical and professional senses. To achieve this, the pedagogical group started from three fundamental principles based on the application of teaching work methods and pro-cedures, as well as the determination of interactions between the various factors that directly and indirectly influence the training process of professionals in the tourism sector. The results of comprehensive evaluation processes considering both the academic performance of the teachers and the level of satisfaction of students taking the course support the application of these teaching–learning methods, as well as highlighting the convenience of extending the experiences and re-search results of the pedagogical team to other vocational training centers for tourism activities.

Water supply policy is closely linked to sustainability as it directly influences the conservation and management of vital natural resources. This policy focuses on developing regulations and designing various activities related to the preser-vation of water resources, which can promote both ecological and economic sustainability. This study focuses on the Challenges of the Israeli Water Sector in Times of Emergency. In a time of growing climate crisis and despite the global warming trend, Israeli citizens routinely enjoy a regular constant supply of clean fresh water, thanks to local desalina-tion plants. Establishment of the desalination plants has become a model of water management for many countries in an era of growing climate crisis. At the same time, Israel’s water sector is faced with challenges and threats related to earthquakes and various states of warfare and security confrontations. In such times of emergency, Israel’s water sector is particularly vulnerable to disruptions of the water infrastructure and it’s adequate operation, by both contamination of the water sources and damage to the desalination plants. This study examines the challenges of the Israeli water sec-tor that require it to contend with these emergency situations in an era of reliance on desalination plants. The research method combines analysis of statistical data extracted from Israel’s Central Bureau of Statistics, Government Water and Sewage Authority, and Meteorological Service, in addition to reading and analyzing primary documents generated by various government ministries and government organs related to Israel’s water policy. The research findings lead to the conclusion that public policy on managing the water sector, manifested in the development and establishment of water desalination plants, has resolved Israel’s water crisis, put an end to its dependency on the amount of precipitation and on natural water sources, and allowed an increase in water production to match the rise in consumption. Nonetheless, as successful as this public policy may be, it does not consider the possibility of extreme scenarios and does not develop the entire range of steps necessary to confront them, and thus undermines the ability of the Israeli water sector to provide its citizens with water in times of emergency.

This research examines sustainable approaches in academic career development by analyzing the interactions between job satisfaction, professional commitment, and research performance among early-career researchers in Romanian institutions. Using a quantitative cross-sectional design, data were collected from 96 individuals classified as R1 and R2 by the European Commission. A detailed questionnaire and Web-Assisted Personal Interviewing (WAPI) identified key relationships. Findings show a significant positive correlation between job satisfaction and research perfor-mance, underscoring the role of professional satisfaction in fostering sustainable academic ex-cellence. Professional commitment is also a crucial predictor of research performance, aligning with existing studies and highlighting the importance of sustainable career practices. Statistical analysis via SPSS version 23.0 validated the initial hypotheses, emphasizing each dimension's significance in a sustainable academic career. Results reflect Romania's research environment, emphasizing the need for sustainable working conditions, effective research evaluation, sup-portive career frameworks, and strong professional commitment to motivate young researchers. This research proposes innovative, sustainable practices for enhancing academic performance and career management, advocating clear objectives, leadership development, continuous competency improvement, and a collaborative, inclusive work environment. Understanding these factors aids individuals and institutions in making informed decisions that support sustainable academic career trajectories and professional growth.

The presented work offers a novel process scheme for valorizing historical Pb-Zn slag, which involves crushing, grinding, and then using separation techniques to concentrate valuable components (nonferrous metals). This methodology could have a significant impact on the global beneficiation of metallurgical slags since, in theory, it is significantly more simple, environmentally friendly, and cost-effective than standard pyrometallurgical or hydrometallurgical procedures. According to previous physicochemical and mineralogical studies, Pb-Zn slag from "Topilnica" Veles is a valuable secondary raw material. This inhomogeneous technogenic resource contains substantial amounts of nonferrous metals (Pb, Zn, Cu, and Ag). However, laboratory tests have indicated that the Pb-Zn slag contains a very uneven valuable metals amounts, ranging from several g/ton to several tens of g/ton. The main issue is that traditional metallurgical procedures for releasing beneficial elements are not commercially viable since the valuable elements are "trapped" within amorphous aluminosilicates or intergrowths of alloy grains and glassy phases. Gravity concentration (Wilfley 13 shaking table) and magnetic separation (Davis separator and disk separator) were used to obtain the final concentrate following comminution and grindability testing. The gravity concentration proved more effective. This streamlined approach of valorizing Pb-Zn slag can reduce the need for hazardous chemicals used in hydrometallurgical refinement operations, as well as the extremely high temperatures required for pyrometallurgical processing. This is the first investigation into the feasibility of this novel approach, which includes direct investigations of the Pb-Zn slag feed using these alternative technologies. After extracting the valuable metals, the amorphous aluminosilicate part of the Pb-Zn slag can be reapplied as an alternative raw material in the building sector, adding to the circularity of the suggested approach..

Background/Objectives: Head and neck free flap reconstructions are often required to treat tumors or extensive post-traumatic jaw defects. The facial artery is the standard receiving vessel for intraoral microvascular anastomoses. However, its use is associated with several disadvantages, such as lesions of buccal nerve branches or the parotid duct and variability in course and diameter. This study aims to investigate whether sublingual artery branches can be considered an alternative intraoral supply vessel to the facial artery to avoid these drawbacks. Methods: 12 formalin-fixed cadaveric heads with 24 sides were dissected. The sublingual artery's origin, course, branching pattern, and distribution were examined. In addition, the diameters of the branches of the sublingual artery were assessed to identify potential supply vessels for anastomoses. Results: In ten of the 24 cases (41.7 %), the sublingual artery originated from the lingual artery and nine cases (37.5 %) from the facial artery. The main trunk of the sublingual artery was present in the floor of the mouth in all cases (100 %) with a diameter of ≥ 0.9 mm in vitro (1 mm in vivo). In 15 of the 24 half heads (62.5 %), branches of the sublingual artery with ≥ 0.9 mm were identified in this space. Conclusions: The large diameter of the sublingual artery on the floor of the mandible suggests that this vessel or its branches could be considered alternative pedicles for intraoral anastomoses in mandibular microvascular free flap grafts.

The unprecedented progress in Artificial Intelligence (AI), particularly in deep learning algorithms with ubiquitous internet connected smart devices, has created a high demand for AI computing on the edge devices. This review studied commercially available edge processors, and the processors that are still in industrial research stages. We categorized state-of-the-art edge processors based on the underlying architecture, such as dataflow, neuromorphic, and Processing in-Memory (PIM) architecture. The processors are analyzed based on their performance, chip area, energy efficiency, and application domains. The supported programming frameworks, model compression, data precision, and the CMOS fabrication process technology are discussed. Currently, most of the commercial edge processors utilize dataflow architectures. However, emerging non-von Neumann computing architectures have attracted the industry in recent years. Neuromorphic processors are highly efficient for performing computation with fewer synaptic operations, and several neuromorphic processors offer online training for secured and personalized AI applications. This review found that the PIM processors show significant energy efficiency and consume less power compared to dataflow and neuromorphic processors. The future direction of the industry would be to implement state-of-the-art deep learning algorithms in emerging non-Von Neumann computing paradigms for low power computing on the edge devices.

Background : The quantification of electromyographic activity using surface electrodes is inval-uable for understanding gait disorders in patients with central nervous system lesions. We propose to evaluate a commercially available low-cost system compared to a reference system in partic-ipants with stroke-related movement disorders in functional situations. Methods : Three hemiparetic participants performed three functional tasks: two treadmill walks at different speeds and a sit-to-stand test. The vastus lateralis and gastrocnemius medialis muscles were equipped with two EMG sensors. The comparison between the two EMG systems was based on 883 identified cycles. Spearman’s correlation coefficients (SC), linear correlation coefficients (LCC), and cross-correlation coefficients (CCC) were calculated. Results : The main results indicate good to very good similarity of the EMG signals collected from the two tested sEMG systems. In the comfortable walking condition, an SC of 0.894±0.091 and an LCC of 0.909±0.094 were noted. In the fast walking condition, an SC of 0.918±0.064 and an LCC of 0.935±0.056 were observed. For the 1-minute sit-to-stand test an SC of 0.880±0.058 and an LCC of 0.881±0.065 were noted. Conclusions : This study demonstrates good to very good similarity between the two sEMG systems, enabling the analysis of muscle activity during functional tasks.

The objective of this research was to study the drying kinetics of passion fruit seeds, which are a byproduct of the industrial processing of passion fruit, with the potential to produce byproducts such as oil and flour. After drying, the seeds were directed for cold-press oil extraction, and the quantification of fatty acids was performed. Following the oil extraction, the residues underwent a grinding process to produce flour, which was characterized in terms of its nutritional aspects. The drying process was conducted using an experimental forced convection dryer with controlled temperatures of 40, 50, 60, and 70 °C, and a drying air velocity of 1.5 m s-1. This work introduced a novel approach using mathematical models, all derived from Fick's equation. For each model, the activation energy and thermodynamic properties related to the drying procedure were determined. Fatty acids in the oils and physicochemical characteristics of the defatted residue's flour were also analyzed. The Cavalcanti Mata, Henderson &amp; Pabis, and Page models modified by Cavalcanti Mata were found to best fit the experimental data. The highest proportions of unsaturated fatty acids in passion fruit oil were linoleic acid (Omega-6) at 68.8% and oleic acid (Omega-9) at 16.1%. The predominant saturated fatty acid was palmitic acid at 10.61%, with no significant differences observed in relation to the drying temperatures. It can be concluded that the composition of the flour from the residue of passion fruit grain oil extraction varies in terms of crude fiber content, ranging from 56.36% to 58.8%, and protein content, ranging from 15.6% to 18.26%, with significant differences observed concerning the drying temperatures. The lipid content varied from 13.5% to 13.76%, with no significant differences observed across the evaluated drying temperature variations.

Deep inhalation breath hold (DIBH) has become a valuable technique in left breast cancer radiotherapy, offering the possibility to reduce radiation exposure to organs at risk (OARs) and minimize the risk of cardiac complications. The treatment method involves patients stopping breathing during irradiation to temporarily distance the heart from the radiation field, reducing potential cardiac and other complications. Research shows that DIBH reduces the radiation dose to the heart and lungs. However, the effectiveness of DIBH is determined by a variety of factors, including the patient's training, cooperation, anatomical features, age and choice of radiotherapy technique. Additionally, cardiovascular risk factors: diabetes, smoking, hypertension can affect the effectiveness and potential complications of DIBH. Moreover, lack of consent, fear, anxiety, depression and comorbid psychiatric disorders disqualify some patients from the DIBH treatment method. Careful patient selection, comprehensive training and optimization of treatment parameters are essential to maximize the benefits of DIBH while minimizing potential side effects. DIBH enhancement techniques, such as IMRT or VMAT, also have an important role to play. The purpose of this review article is to summarize the factors affecting the efficacy and side effects of DIBH in radiation therapy for left breast cancer, with the aim of optimizing its clinical application while minimizing side effects.

Successful strategies that can contribute to poverty reduction and improve the livelihoods of the poor, particularly in Sub-Saharan Africa (SSA), are critically needed to address the technology adoption constraints. The objectives of this study were to assess the adoption rate of improved technologies and management practices and to identify the key factors influencing their adoption in the major wheat-producing areas of Sudan's irrigated, dry, and heat-prone environments. A farm survey was conducted in 2021 using a structured questionnaire that included all recommended technological options for optimum and sustainable wheat production. A total of 300 farmers, 93, 101, and 106 from Northern (NS), Kassala (KS), and Gezira (GS) states, respectively, were selected and interviewed. Besides descriptive statistics, a binary logistic model was used to identify the socioeconomic and production factors affecting farmers' perceptions of improved and recommended technological options. The study found a wide range of adoption rates depending on the specific technology practice and the area surveyed. The lowest adoption rate was observed for land preparation (6.5%) in NS. Adoption rates ranging from 26-100% were observed for technologies such as sowing date, seed rate, seed treatment, awareness of released varieties, nitrogen and phosphorus fertilizers application, and chemical weed control. The difference in the productivity of technological options adopters was significant (P = 0.015) compared to non-adopters. The binary logistic regression results showed that five out of seven explanatory variables hypothesized to influence wheat farmers' perceptions on the decision to adopt improved and recommended technologies significantly influenced farmers' decision to adopt the technologies. In particular, access to quality seed, financial credit, and extension services were found to be the most critical determinants of adopting improved technologies. Approaches that bring together all stakeholders along the crop value chain, including policymakers, to jointly analyze, identify, and prioritize challenges, develop and apply solutions and work plans using feedback and learning mechanisms are expected to increase farmer awareness and adoption of improved technologies, ultimately leading to sustainable wheat production.

Fiscal control and monitoring of public management are issues of great importance and relevance, as they focus on preventing and mitigating corruption, thus benefiting governments in their management and performance, and thus citizens. Given the large amount of data involved in the management and functioning of a government, it is pertinent to apply data analysis methods to contribute to fiscal control. This paper aims to investigate data management for this purpose, starting with a bibliometric study to demonstrate the relevance and pertinence of the current study. It examines existing data collection techniques that may facilitate such control, data storage considering its nature and the possibilities that contribute to this end, and data processing through techniques that allow obtaining adequate information for further analysis and decision making. The necessary architecture and interoperability between systems is also discussed, as the existence of large amounts and types of data in governments makes it crucial to promote these practices for accurate and truthful analysis. Finally, data visualization is addressed since this topic is highly relevant as it allows for the identification of anomalies, where proper visualization promotes appropriate analysis and thus informed decision making. This work is useful for modern audit and fiscal control entities, since data capture, storage, processing, interoperability, and visualization are crucial elements that collectively enhance the ability to supervise public expenditure effectively, ensuring that public funds are managed with the highest standards of transparency, accountability, and efficiency.

Mesenchymal stem cells (MSCs) are one of the main residents in the bone marrow (BM), having an essential role in the regulation of hematopoietic stem cell (HSCs) differentiation and proliferation. Myelodysplastic syndromes (MDS) are a group of myeloid disorders impacting hematopoietic stem and progenitor cells (HSCPs) and characterised by BM failure, ineffective haematopoiesis, cytopenia, a high risk of transformation through the expansion of MDS clones together with additional genetic defects. It has been indicated that MSCs play anti-tumorigenic roles such as cell cycle arrest and pro-tumorigenic roles including induction of metastasis in MDS and leukaemia. Growing evidence has shown that MSCs have impaired functions in MDS, such as decreased proliferation capacity, differentiation ability, haematopoiesis support, immunomodulation function, and increased inflammatory alterations within the BM through some intracellular pathways such as Notch and Wnt and extracellular modulators secreted by MSCs abnormally, including increased expressions of inflammatory factors and decreased expressions of hematopoietic factors, contributing to the development and progression of MDS. Therefore, MSCs can be targeted for the treatment of MDS and leukaemia. However, it remains unclear what drives MSCs to behave abnormally. In this review, dysregulations in MSCs and their contributions to myeloid haematological malignancies will be discussed.

Knowledge of the genetic diversity of the available germplasm is an important foundation for crop conservation, management, and improvement. Successful breeding strategies rely on a sound understanding of the genetic diversity of the crop germplasm. This study was conducted to evaluate and compare the genetic diversity of 49 soybean [Glycine max (L.) Merrill] genotypes using SSR markers as well as to evaluate parental potential of these genotypes in cultivar improvement. The bulk of the genotypes were developed in the national soybean breeding program in Zimbabwe between 1940 and 2013. The genetic diversity and relatedness were estimated using 30 SSR markers and produced 5 clusters that generally matched the pedigree information of the germplasm. The SSR analysis detected a total of 135 alleles. The polymorphic information content ranged from 0.10 to 0.77 with a mean of 0.45. The SSR primer, Satt012 was the most informative. The SSR marker analysis showed a wide genetic diversity in the germplasm. To date, there are no reports regarding the genetic diversity analysis of soybean germplasm in Zimbabwe. The genotypes, EL41 and EL7; EL41 and EL1; as well as EL41 and EL42 were the most divergent thus, indicating their potential use as parental lines in the genetic improvement of soybean in the region.

Introduction: In Part II of this three-part series, we expose techniques that can be used by the U.S. Centers for Disease Control and prevention (CDC) and Food and Drug Administration (FDA) to manipulate and throttle the Vaccine Adverse Event Reporting System (VAERS), a vaccine safety monitoring system open to the public. The U.S. government’s recommendation of COVID-19 vaccines during pregnancy was more a product of propaganda than of science and plagued with conflicts of interest. The extreme breach of COVID-19 vaccine pregnancy complication safety signals documented in VAERS cannot be disregarded or concealed. As a result, CDC/FDA are moving to closed, government captured platforms, including V-safe, Vaccine Safety Datalink (VSD) system, and the Biologics Effectiveness and Safety (BEST) system. Public transparency of adverse events following COVID-19 vaccination in pregnancy should be increased, not thwarted. Methods: In Part I the CDC/FDA Vaccine Adverse Event Reporting System (VAERS) database was queried from January 1, 1990, to April 26, 2024 for adverse events (AEs) involving pregnancy complications following vaccination. In Part II we investigate tactics discovered in VAERS that concealed vaccine injuries and deaths. Conflicts of interest, as well as specific communications and publications from the CDC/FDA and the New England Journal of Medicine (NEJM) are analyzed. Results: Of all VAERS AE reports, 8.47% (138,983) of COVID-19 reports had no vaccination date and thus were missing from the data presented in Part I of this series. We unconcealed data presented in Part I of this series to include VAERS case reports that were excluded when using standard queries because of an “unknown vaccination date”. The unconcealed data in the 37 AE cases reported in this study (Part II) ranged from 0 - 38% with a mean of 8.2% (standard deviation 9.7%) and the significant breaches of VAERS safety signals in Part I of this series were largely underestimated. Another method of concealing vaccine associated AEs is to exclude the specific vaccine type rendering it as an “unknown vaccine type”. “Unknown vaccine type” has logarithmically increased since the rollout of the COVID-19 vaccines in 2021, ranging from 1 to 400 cases between 1990 to 2018 to 11,461 cases in 2021. CDC/FDA granted $67 million via a Cooperative Agreement grant to Massachusetts Department of Public Health (MDPH) between July 1, 2019, to June 30, 2025. MDPH then made subawards under this grant to 21 influential public and private health and civic organizations, including NEJM’s owner, Massachusetts Medical Society (MMS). As one of the subawardees under the grant, MMS as owner of NEJM received over $426,000 in federal HHS/CDC grant money from 2019 to 2020 for “public health and medical emergency preparedness projects”. Conclusions: CDC/FDA has failed to capture and account for a significant number of AE case reports following COVID-19 in VAERS by a variety of techniques in an apparent attempt to sanitize the data. CDC’s Rochelle Walensky and NEJM’s Editor-in-Chief Eric Rubin pushed novel untested COVID-19 vaccines in pregnant women despite clear evidence from multiple sources that safety signals had been breached.

Over the last fifty years, management plans have become more descriptive with regard to potential sustainability of forest systems, raising questions about the feasibility of implementing management activities and ensuring sustainability of a wide variety of ecosystem services. To assess this issue, we conducted a survey among forest planning and operation research communities to understand their perceptions regarding the potential of a sample of sustainability statements currently used in forest plans to be incorporated into optimization models or other mathematical operations. The results revealed that only a few statements from the sample were deemed to have relatively mature or firm methodologies and data to enable inclusion in modern mathematical models for land use optimization. These statements were mostly related to economic sustainability, offering quantifiable information such as a non-declining flow of wood products over time and limits on the amount of timber harvested per decade. In contrast, sociocultural and, to some extent, ecological statements regarding sustainability were generally perceived to be more difficult to translate into mathematical modeling efforts. Particularly challenging were statements corresponding to sustaining natural or scenic characteristics of a forest. These findings may be attributed to various factors, including a lack of measurable indicators for sustainability and a potential lack of understanding about the modeling components and their interactions with planned management activities.

Drug resistance in melanoma is a major hindrance in cancer therapy. Growth hormone (GH) plays a pivotal role in contributing to resistance to chemotherapy. Knocking down or blocking the GH receptor has been shown to sensitize the tumor cells to chemotherapy. Extensive studies have demonstrated that exosomes, a subset of extracellular vesicles, play an important role in drug resistance by transferring key factors to sensitize cancer cells to chemotherapy. In this study, we explore how GH modulates exosomal cargoes from melanoma cells and their role in drug resistance. We treated the melanoma cells with GH, doxorubicin, and GHR antagonist, pegvisomant, and analyzed the exosomes released. Additionally, we administered these exosomes to recipient cells. The GH-treated melanoma cells released exosomes with elevated levels of ABC transporters (ABCC1 and ABCB1), N-cadherin and MMP2, enhancing drug resistance and migration in recipient cells. GHR antagonism reduced these exosomal levels, restoring drug sensitivity and attenuating migration. Overall, our findings highlight a novel role of GH in modulating exosomal cargoes that drive chemoresistance and metastasis in melanoma. This understanding provides insights to the mechanisms of GH in melanoma chemoresistance and suggests GHR antagonism as a potential therapy to overcome chemoresistance in melanoma treatment.

Automatic emotion recognition using portable sensors is gaining attention due to its potential use in real-life scenarios. Existing studies have not explored Galvanic Skin Response and Photoplethysmography sensors exclusively for emotion recognition using nonlinear features with machine learning (ML) classifiers such as Random Forest, Support Vector Machine, Gradient Boosting Machine, K-Nearest Neighbor, and Decision Tree. In this study, we proposed a genuine window sensitivity analysis on a continuous annotation dataset to determine the window duration and percentage of overlap that optimize the classification performance using ML algorithms and nonlinear features, namely Lyapunov Exponent, Approximate Entropy, and Poincaré’s indices. We found an optimum window duration of 11 seconds and 16 seconds for valence and arousal, respectively, with 50% overlap, and achieved an accuracy of 0.76 in both dimensions. In addition, we proposed a Strong Labeling Scheme that kept only the extreme values of the labels, which raised the accuracy score to 0.92. Under certain conditions mentioned, traditional ML models offer a good compromise between performance and low computational cost. Our results suggest that well-known ML algorithms can still contribute to the field of emotion recognition, provided that window duration, overlap percentage, and nonlinear features are carefully selected.

Optimizing warehouse operations and leveraging data insights are driving forces behind the rise of automation. This research explores the development of the SWIF warehouse management system (WMS) using the Agile methodology, a departure from traditional software development approaches. Agile’s iterative and step-by-step approach facilitated a streamlined development process. It allowed for quick adjustments based on evolving needs and feedback from stakeholders. This resulted in a substantial decrease in development time and a noticeable improvement in the final product’s quality. The study emphasizes the positive effect of Agile on teamwork and customer satisfaction. Developers reported enhanced communication and collaboration, leading to a stronger sense of ownership and engagement. Customers expressed greater satisfaction with the features and functionalities delivered in stages. The successful implementation of Agile in SWIF’s development showcases its effectiveness in managing complex and ever-changing software development projects. The study encourages the adoption of Agile methodology for similar projects to boost responsiveness, quality, and overall project success.

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

In the age of digital revolution technology is being used more and more in education around the world to improve student learning. By enabling ongoing skill development and knowledge acquisition through digital platforms e-learning has become a crucial tool. Through E-Learning online learning is becoming increasingly popular and effective in addition to traditional methods of instruction as highlighted by the latest statistics from Badan Pusat Statistik Indonesia (BPS). In order to address the issues of scalability, interactivity and security in e-learning platforms. This study introduces a web-based learning environment that is focused on teaching programming. Leveraging the MERN stack, while also integrating TypeScript for improved code reliability, Next.js for accelerated React.js functionality, Tailwind CSS for responsive design. Backend features include media management with Cloudinary, secure video streaming with VdoCipher, data storage with MongoDB, and caching with Redis. Iterative development will be following agile methodology. In order to create a vibrant and encouraging learning environment the platform includes interactive course pages multimedia content real-time feedback mechanisms and community-building tools. Despite some early difficulties the implementation phase effectively achieved development goals. To further support international online learning initiatives further efforts will be directed toward improving platform security and functionality.

Recently concluded, large-scale cancer genomics studies involving multiregion sequencing of primary tumors and paired metastases appear to indicate that many or most cancer patients have one or more “clonal" mutations in their tumors. Clonal mutations are those that are present in all of a patient’s cancer cells. Achilles Therapeutics is currently the only company specifically targeting clonal mutations. However, they are doing so with tumor-derived T cells. To address the potential limitations of immunotherapy, I have devised another approach for exploiting clonal mutations, which I call “Oncolytic Vector Efficient Replication Contingent on Omnipresent Mutation Engagement” (OVERCOME). The ideal version of OVERCOME would likely employ a bioengineered facultative intracellular bacterium. The bacterium would initially be attenuated, but (transiently) reverse its attenuation upon clonal mutation detection.

Oxytetracycline (OTC) serves as a widely employed antibiotic in veterinary treatment and prevention of infections, potentially leaving residues in animal-derived food products, such as milk, that are consumed by humans. Given the detrimental effects of prolonged human exposure, it becomes imperative to develop precise and sensitive methods for monitoring the presence of OTC in food. This work reports on the fabrication of a label-free electrochemical aptasensor for the determination of OTC as a proof-of-concept.

Digital transformation, organizational resilience, and agility are now becoming one of the keys to meeting the competitive challenges of modern organizations. It is no surprise that digital transformation and digital technologies have also begun to significantly impact the energy industry, moving towards improving the sector's profitability and efficiency. However, to navigate through the difficult process of digital transformation in today's turbulent environment, organizations, including those in the energy sector, need to build organizational resilience. Nevertheless, the nature of the relationship between digital transformation and organizational resilience has not yet been sufficiently clarified. Focusing on the level of digital transformation, and more precisely within the two dimensions of digital maturity, i.e. digital intensity and transformation management intensity, as well as based on the perspective of dynamic capabilities, this study developed a configurational framework and proposed a theoretical model to study the equifinal paths through which digital transformation and dynamic capabilities influence organizational resilience in energy sector companies. Based on a fuzzy set qualitative comparative analysis (fs/QCA) conducted on selected companies in the energy sector, i.e. Polish CHP plants, the relationship between digital transformation, dynamic capabilities and organizational resilience was investigated. The results show that a high level of organizational resilience can be achieved through two main paths based on the dominance of dynamic capabilities and the dominance of digital maturity. The study found that digital maturity can significantly influence CHP resilience. Moreover, the transformation management intensity is strongly related to high organizational resilience. The paper concludes by describing theoretical and practical implications, as well as research limitations and prospects for future research.

This paper presents a comprehensive study on applying machine learning (ML) techniques for real-time credit card fraud detection. With the increasing prevalence of credit card fraud, financial institutions face the challenge of detecting fraudulent transactions promptly and accurately. This research evaluates various ML models, including Logistic Regression, Decision Trees, Random Forests, XGBoost, and Deep Convolutional Neural Networks, for their effectiveness in identifying fraudulent activities. Utilizing PySpark for processing large-scale transaction data, the study highlights the importance of real-time analysis, adaptive learning, and handling imbalanced datasets. Key findings reveal that XgBoost, with its balance of accuracy and complexity, emerged as the most promising model, achieving an accuracy rate of 98% in detecting fraudulent transactions. The paper further discusses the potential of ensemble methods, graph-powered systems, and intelligent sampling in enhancing fraud detection capabilities. Challenges such as overfitting, data access, and the need for real-time analysis are addressed. Future research directions include implementing models in live transaction environments, expanding model applicability across financial platforms, and developing advanced anomaly detection methodologies. This study underscores the pivotal role of machine learning in safeguarding financial transactions against fraud, offering significant implications for consumers, financial institutions, and the broader financial ecosystem.

Autism spectrum disorder (ASD) is characterized by diverse patterns of social interaction, communication, and behavior. This study explores the application of clustering and stochastic migration models to visualize and understand the social and neural connectivity patterns associated with autism. The clustering model, with its strong internal connections and limited external connections, mirrors the tight-knit social groups and communication barriers often observed in individuals with autism. In contrast, the stochastic model, characterized by more dispersed connections and greater adaptability, represents the variability and flexibility seen in social interactions across typical individuals. By comparing these models, we can gain deeper insights into the unique challenges and strengths of individuals with autism, highlighting the importance of personalized interventions. Additionally, these models provide a visual framework for understanding the differences in brain connectivity patterns observed in autism, with implications for both behavioral and neural correlates. This study underscores the value of computational models in advancing our understanding of autism and guiding effective support strategies.

In recent studies, the circulation of a (SLG) super-Lorentz-Gaussian beam in a non-turbulent ‎troposphere has been investigated and used for laser applications. This method uses the extended ‎Huygens-Fresnel integral technique and refers to the multiplication of the Gaussian beam by the ‎Lorentz function. Formulas are derived for the normal density and approximate field of the Lo-‎Rentz-Gaussian beam in a non-turbulent troposphere. The average density power and the propagation characteristics of the SLG jet in the non-turbulent troposphere are numerically represented. ‎The characteristics of the beam boundaries at atmospheric non-turbulence for the propagation ‎distance of an SLG beam in a non-turbulent troposphere are also discussed in detail. They are ‎practical in an optical communication system used in lasers.

The field of solar technology has experienced remarkable advancements, transforming from early photovoltaic discoveries to a diverse array of high-efficiency, cost-effective solutions. This review explores the evolution of solar technology, detailing its development from the initial discovery of the photovoltaic effect to contemporary innovations. The current generation of solar technologies, including silicon photovoltaic cells, thin-film solar cells, perovskite solar cells, bifacial panels, concentrated solar power, and building-integrated photovoltaics, is examined for their efficiencies, applications, and contributions to the renewable energy landscape. Future prospects highlight promising trends such as next-generation photovoltaics, advanced energy storage solutions, agrivoltaics, floating solar farms, and artificial photosynthesis. Despite the progress, challenges related to efficiency, stability, scalability, cost, grid integration, environmental impact, policy support, and public acceptance remain. Addressing these challenges through continued research, supportive policies, and collaborative efforts is crucial for realizing the full potential of solar technology. This review underscores the importance of solar energy in achieving a sustainable future and emphasizes the need for ongoing innovation and commitment to overcome existing barriers.

Mobile Edge Computing (MEC) integrated with Wireless Power Transfer (WPT) is emerging as a promising solution to reduce task delays and extend the battery life of Mobile Devices (MDs). Cooperative user communication, or relay technology, enhances communication efficiency for users located far from the base station by mitigating the double near-far effect caused by distance. This is considered a key technology in Beyond 5G (B5G) and future communication systems. In this paper, we focus on maximizing the long-term energy efficiency (EE) of a user-cooperation WPT-MEC system, while taking into account the uncertain load dynamics at the edge MD and the time-varying state of the wireless channel. The joint optimization of wireless charging time fraction, MD offloading duration, helper node processing time, and transfer power decision presents significant challenges due to the coupling of data offloading among cooperative users and a volatile system environment. To address these challenges, we formulate the problem as a stochastic programming problem and propose an online control algorithm, DOUCA, to solve it. Our approach utilizes Dinkelbach’s method and Lyapunov optimization theory to decouple the sequential decision problem into a deterministic sub-problem for each time slot. For the sub-problem, we use variable substitution to convert the non-convex problem into a convex one, containing only a small number of variables, which can be efficiently solved. Furthermore, we provide a mathematical analysis of our algorithm’s performance. Extensive simulation results demonstrate the effectiveness of our proposed algorithm, as evidenced by an impressive energy efficiency improvement of over 20% compared to benchmark methods. Our algorithm also achieves a trade-off between EE and system stability.

The large and interconnected nature of the Internet of Things (IoT) presents unique security challenges, even as it revolutionizes various sectors. With numerous devices, often limited in resources, traditional perimeter-based security methods struggle to keep pace. The "never trust, always verify" principle of zero trust security offers a viable solution. Zero trust security is a concept which has become increasingly popular, using key exchange techniques to ensure secure and authenticated communication within the network, especially in managing risks in critical infrastructure. Authentication is a process to identify an entity, a prerequisite for authorization, and essential for granting access control. It fundamentally relies on trust management and various methods to generate and manage cryptographic keys for authentication. The aim of this study is to enhance zero trust security in the context of the Internet of Things by investigating authentication methods and discussing several potential solutions for successful implementation. This study also presents the performance evaluation criteria for authentication in IoT and introduces advanced approaches for different scenarios, including lightweight cryptography, mutual authentication, and blockchain technology. Finally, we address challenges related to implementation and future directions for research.

Pulmonary arterial hypertension (PAH) is a chronic disease characterized by a progressive in-crease in mean pulmonary arterial pressure. Mutations in the BMPR2 and AQP1 genes have been described in familial PAH. The bone morphogenetic proteins BMP9 and BMP10 bind with high affinity to BMPR2. Administration of BMP9 has been proposed as a potential therapeutic strategy against PAH, although recent conflicting evidence dispute the effect of such practice. Considering the involvement of the above molecules in PAH onset, progression, and therapeutic value, we ex-amined the effects of modulation of BMP9, BMPR2, and AQP1 on BMP9, BMP10, BMPR2, AQP1, and TGFB1 expression in human pulmonary microvascular endothelial cells in vitro. Our results demonstrated that silencing the BMPR2 gene resulted in increased expression of its two main ligands, namely BMP9 and BMP10. Exogenous administration of BMP9 caused return of BMP10 to basal levels, while restored the decreased AQP1 protein levels and the decreased TGFB1 mRNA and protein expression levels caused by BMPR2 silencing. Moreover, AQP1 gene silencing also resulted in increased expression of BMP9 and BMP10. Our results might possibly imply that the effect of exogenously administered BMP9 on molecules participating in the BMP-signaling pathway could depend on the expression levels of BMPR2. Taken together, these results may pro-vide insight into the highly complex interactions of the BMP-signaling pathway.

Cocoa (Theobroma Cacao, L.) represents an important market that gained relevance and became an esteemed commodity thanks to cocoa powder, chocolate, and other related products. This work has analyzed 59 cocoa powder samples from the European market. Three distinct subgroups were identified: organic or conventional, alkalized or not alkalized, and raw or roasted processing. The impact of the technological process was evaluated on the pH, color, and compositional traits, as content of biogenic amines and salsolinol. The phenolic fraction was also investigated trough both common and emerging methods. Results depict that the influence of the agronomical practices (organic/conventional) did not affect significatively (p&lt;0.05) the composition of cocoa powders; similarly, the roasting process was not discriminative for the compounds traced. On the other hand, the alkalinization process greatly impacted on color, and pH, no matter of cocoa provenience, obtention, or other processes, also resulting reducing the phenolic fraction of treated samples. Principal components analysis confirmed that the alkali process acts on pH, color, and phenolic composition, but not on other bioactives molecules (BAs and salsolinol). All samples resulted safe while alkalized powders have a great reduction in beneficial biocompounds. A novel strategy could be to emphasize non-alkalized powders in the label, to meet the demand for more beneficial products.