Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational background, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational domain they are interested in working. Common vocational domains include agriculture, cooking, crafting, construction, and hospitality. The increasing amount of user-generated content in wikis and social networks provides a valuable source of data for data mining, Natural Language Processing and machine learning applications. This paper extends the contribution of the authors’ previous research on automatic vocational domain identification by further analyzing the results of the machine learning experiments with the domain-specific textual data set, considering 2 research directions: a. predictions analysis and b. data balancing. Wrong predictions analysis and the features that contributed to misclassification, along with correct predictions analysis and the features that were the most dominant, contributed to the identification of a primary set of terms for the vocational domains. Data balancing techniques were applied on the data set to observe their impact on the performance of the classification model. A novel 4-step methodology is proposed in this paper for the first time, consisting of successive applications of SMOTE oversampling on imbalanced data. Data oversampling obtains better results than data undersampling in imbalanced data sets, while hybrid approaches perform reasonably well.

The article delves into the crucial realm of compressing hydrogen in its gaseous state, a pivotal process for enhancing its viability in both civil and industrial sectors. The study initiates by providing a concise overview and comparison of diverse hydrogen storage methodologies, laying the groundwork with an in-depth analysis of hydrogen's thermophysical properties. It scrutinizes plausible configurations for hydrogen compression, aiming to strike a delicate balance between energy consumption, predominantly derived from the fuel itself, and the requisite number of compression stages. Notably, to render hydrogen storage competitive in terms of volume, pressures of at least 350 bar are deemed essential, albeit at an energy cost amounting to approximately 10% of the fuel's calorific value. Multi-stage compression emerges as a crucial strategy, not solely for energy efficiency, but also to curtail temperature rise, with an upper limit set at 200°C. This nuanced approach is underlined by the exploration of compression levels commonly cited in the literature, particularly 350 bar and 700 bar. Ultimately, the study advocates for a three-stage compression system as a pragmatic compromise, capable of achieving high-pressure solutions while keeping compression work below 10 MJ/kg, a threshold indicative of sustainable energy utilization.

Intraoperative load sensors have been developed with the aim of balancing the soft tissue in the knee during Total Knee Replacements (TKRs). Literature has shown success in soft tissue bal-ancing however, concerns regarding the accuracy of these sensors remain. For this reason, pre-vious research has attempted to create robust sensors for intraoperative use. The design of the sensor in this research has aimed to address the concerns outlined in the literature to create a new and novel smart-sensor for soft-tissue balancing. This new intraoperative load sensor included design features to increase accuracy while artificial intelligence allowed for comprehensive sensing across the entirety of the sensor, providing unparalleled insight during the operation. Notably, the sensor was designed to withstand loads of at least 450 N, ensuring robust perfor-mance of the sensor. To optimize the design process while minimizing cost, Finite Element Analysis was employed. This approach ensured the design features fulfilled their function while maintaining structural integrity to withstand loads. Moreover, the novelty of using training data from simulated data will reduce the iterative process of the labor-intensive collection of training data for the artificial intelligence while reducing human error associated with it. Results from the evaluation demonstrated the ability of this design to successfully bridge the current gap in the market by fulfilling all essential design criteria established within the literature. This innovative smart-sensing tool will in turn enhance patient outcomes and alleviate financial burdens to pa-tients and the healthcare systems by reducing the need for early revision surgeries associated with improper joint tension.

Present paper is dealing with the adaptive static balancing of robot or other mechatronic arms that are moving in vertical plane and whose static loads are variable, by using counterweights and springs. Some simple passive and approximate solutions are proposed and an example is shown. The active and exact solutions by using adaptive real time control in the case of unknown variation of static loads are simulated on VIPRO platform developed at Institute of Solid Mechanics of Romanian Academy.

With the progression of AI, embedding advanced AI technologies into small robotics and mobile devices has become essential, driving research towards lightweighting AI models. Our study enhances the EPINET depth estimation model for light field images, aiming for compactness and faster inference while preserving accuracy. We conducted two-step experiments aimed at enhancing inference efficiency: Initially, by adjusting input streams and convolution layers, we simplified the CNN model, achieving faster inference times at the cost of reduced accuracy. To address this reduction in accuracy, we then applied knowledge distillation, allowing the simplified model to learn from the original model’s more complex patterns. In our quantitative experiments using two error metrics, MSE (Mean Squared Error) and BadPix, we identified optimal knowledge positions and evaluated the required complexity for the student model. As a result, our method improved MSE by 21% and BadPix by 14% compared to training without it. Furthermore, the student model achieved an inference speed 13% faster than the teacher model and surpassed its accuracy by 10% in MSE. Additionally, we demonstrated that repeatedly applying our approach could further enhance both model compactness and accuracy.

This general review assesses the impact of physical exercise on oxidative and nitrosative stress and the counteractive role of antioxidants, drawing from a systematic literature search up to March 2024 in databases like PubMed and Web of Science. The review highlights studies on the effect of exercise on the production of reactive oxygen species (ROS) and reactive nitrogen species (RNS) and antioxidant responses in both human and animal models based on rigorous inclusion and exclusion criteria. Due to the variety of exercise types and participants' characteristics, a narrative synthesis approach was adopted. The findings indicate that moderate exercise boosts antioxidant defenses, known as hormesis, whereas excessive physical activity may increase oxidative and nitrosative damage. The review also explores the complex effects of antioxidant supplementation, revealing that while natural dietary antioxidants support health, high-dose supplements could potentially hinder positive adaptations to exercise. Recommendations for athletes and the general active population highlight the importance of balancing exercise and dietary intake to optimize health benefits. The review concludes with a call for more detailed research on tailored exercise and nutrition plans to dissect these intricate relationships further.

The success of Toxoplasma gondii (intracellular parasite) host cell invasion relies on the apical complex, a specialized microtubule cytoskeleton structure associated with secretory organelles. The genome encodes three isoforms of both α- and β-tubulin which are altered by specific post-translational modifications (PTMs), changing the biochemical/biophysical proprieties of microtubules, and modulating their interaction with associated proteins. Tubulin PTMs are a powerful and evolutionarily conserved mechanism to generate tubulin diversity, forming a biochemical ‘tubulin code’ that can be ‘read’ by microtubule-interacting factors. The T. gondii tubulin PTMs are: α-tubulin acetylation, α-tubulin detyrosination, Δ5α-tubulin, Δ2α-tubulin, α- and β-tubulin polyglutamylation, and α- and α-tubulin methylation. Tubulin glutamylation is a key candidate to assist microtubule remodeling in Toxoplasma, being involved in the regulation of microtubule stability, dynamics, interaction with motor proteins, and severing enzymes. The correct balance of tubulin glutamylation is achieved by the coordinated action of polyglutamylases and deglutamylating enzymes. In this work we will review and discuss the current knowledge on T. gondii tubulin glutamylation. By in silico identification of mammalian protein orthologs we explored and updated the identification of putative proteins related to glutamylation, contributing to a better understanding of the role of tubulin glutamylation in T. gondii.

In this paper, we investigate the Green measure for a class of non-Gaussian processes in $\mathbb{R}^{d}$. These measures are associated with the family of generalized grey Brownian motions $B_{\beta,\alpha}$, $0

This research focuses on the Hopf bifurcation occurring at a singular point with high-order degeneracy in a class of three-dimensional systems. Based on center manifold theorem, by expanding the technique for determining singular point values of degenerate singularities in two-dimensional systems, the formal series method to determine the singular point values at the high-order degenerate critical point is discussed. Furthermore, a class of specific three-dimensional differential systems with high-order degenerate singularities is explored. By computing the singular point values and determining center conditions, we proved that a minimum of 5 small-amplitude limit cycles in the vicinity of the origin. This research provides new perspectives on the center problem and limit cycle bifurcation in high-dimensional systems with high-order degenerate singular points.

Abstract Purpose: In this study, the Pre-implantation embryonic sex ratio in 125 couples who had three or more female children and underwent pre-implantation genetic diagnosis (PGD) for non-medical reason was included. Besides, we have aimed to find out whether these couples had more chances of getting a girl child once again. Methods: 125 couples who had three or more female offspring and those who underwent PGD for non-medical sex selection (XY) between 2015 and 2019 were included. Nuclear DNA was analyzed by Fluorescent in situ Hybridization (FISH). 2-chromosome (X, Y), 3-chromosome (21, X, Y), 5-chromosome (13, 18, 21, X, Y) probes were used for FISH. The standard protocol was followed for sperm processing and embryo culture for IVF and PGD. Result: Independent sample t-test showed that there is no significant difference between equal and unequal embryonic groups in patients’ age, husbands’ age, sperm count, sperm motility, total male embryos, total female embryos, normal male embryos, and normal female embryos. For patients with positive pregnancy outcome, 84.6% had unequal embryonic ratio while 15.4% had equal embryonic ratio. Similarly, patients those who were treated by short protocol had 85% of unequal embryonic ratio and 15% had equal ratio. Conclusion: Greater variability in the female to male embryos was found in these couples, confirms the fact that couples previously having girl offspring may predominantly not produce embryo of the same sex every time.

This study examines the balancing between urban transportation security and supply chain efficiency amid global disruptions like the COVID-19 pandemic, attacks on maritime lanes, and geopolitical conflicts. The authors combine bibliometric network analysis and natural language processing (NLP) to review scholarly literature from 2017 to 2023. This approach identifies and visualizes evolving thematic trends. Findings reveal a shift towards integrating advanced technologies such as blockchain, AI, and IoT to enhance security without compromising efficiency. This study highlights the significance of integrating urban planning principles and sustainable practices to enhance transportation security and supply chain efficiency within urban environments. Actionable insights for policymakers and supply chain managers emphasize the adoption of integrated, technology-driven security solutions to foster resilient and sustainable supply chains. This research contributes to academic discourse by refining the understanding of evolving priorities in transportation security and supply chain management, offering both theoretical and practical implications for secure and efficient global supply chains.

In the dynamic landscape of Sabah's socio-economic development, the integration of AI technologies holds both promise and challenge. While AI has the potential to drive economic growth, enhance productivity, and elevate quality of life, it also brings forth concerns regarding job displacement, skill gaps, and unequal distribution of benefits. The latest research problem in the realm of AI and employment in Sabah revolves around ensuring inclusive and sustainable development amidst this technological disruption. This encompasses two key dimensions: skill development and education, and labor market dynamics. Addressing the research questions necessitates a multifaceted approach. Firstly, in the realm of skill development and education, there is a critical need to assess the adequacy of existing programs in equipping the workforce with AI-driven skills. Strategies such as promoting STEM education, fostering digital literacy, and facilitating lifelong learning initiatives, particularly among marginalized communities, are essential. Additionally, fostering collaboration between stakeholders and implementing curriculum reforms are pivotal steps in enhancing the alignment between educational curricula and the demands of an AI-driven economy.

This paper investigates the critical factors for balancing environmental sustainability and economic development through the lens of New Structural Economics. Specifically, we utilize the congruence index to access the alignment between local factor endowment structure and firm production input structure. Using a comprehensive manufacturing firm-level pollutant emissions dataset from the Yangtze River Economic Belt (YEB) region in China, we find that firms demonstrating higher congruence exhibit improved economic performance and alignment with comparative advantages. This not only contributes to increased economic efficiency for these firms but also leads to a reduction in pollutant emissions, including wastewater discharge and Chemical Oxygen Demand (COD) emissions. Our study provides valuable insights into achieving mutually beneficial environmental and economic outcomes.

This work investigates how to balance the electricity supply and demand in a carbon-neutral northern Europe. Applying a cost-minimizing electricity system model including options to invest in eleven different flexibility measures, cost-efficient combinations of strategies to manage varia-tions are identified. The results of the model are post-processed using a novel method to map the net load before and after flexibility measures are applied to reveal the contribution of each flexi-bility measure. The net load is mapped in the space spanned by the amplitude, duration, and number of occur-rences. The mapping shows that, depending on cost structure, flexibility measures contribute to reduce the net load in three different ways; 1) by reducing variations of long duration but low amplitude, 2) by reducing variations of high amplitude but short duration and low occurrence or 3) by reducing variations of high amplitude, short duration and high occurrence. It is found that cost-efficient variation management is achieved by combining wind and solar power and by combining strategies (1-3) to manage variations. The cost-efficient combination of strategies de-pends on electricity system context where electricity trade, flexible hydrogen and heat production (1) manage the majority of variations in regions with good conditions for wind power while sta-tionary batteries (3) are the main contributors in regions with good conditions for solar power.

A defining feature of a productive infection is the co-opting of host cell resources for viral replication. Despite their repertoire of molecular functions, viruses subvert host defenses to take control of cellular factors such as RNA binding proteins (RBPs). RBPs are involved in virtually all steps of mRNA life forming ribonucleoprotein complexes (mRNPs) in a highly ordered and regulated process to control RNA fate and stability in the cell. Thus, the hallmark of this viral takeover is to reshape RNA fate in the cell to modulate host gene expression and evade immune responses. Here we provide an extensive review of work in this area, particularly how the host-viral interplay influences RBP functions to modulate the host cell. Overall, in this review, we highlight the myriad of ways RBPs can regulate RNA stability in either a pro-viral or antiviral manner by gathering novel insights gained from research studies in this field.

This paper proposes a new battery balancing control strategy based on state of charge (SOC) for addressing the problem of cell-to-cell differences in lithium-ion battery systems. The strategy utilizes a reconfigurable converter system to transfer energy from high-SOC battery modules to low-SOC battery modules and allows balancing the battery modules while they are powering the load. A MATLAB/Simulink simulation model with five batteries was built to validate the effectiveness of the proposed balancing strategy under unloaded and loaded conditions. The simulation results demonstrate that the proposed strategy achieves more efficient and accurate battery module balancing compared to the previous balancing modes, thus increasing the overall lifespan and safety of the battery pack.

The Blue Economy represents a sustainable development model emphasizing the responsible use of ocean resources to foster economic growth while preserving marine ecosystems. This approach aims to balance economic prosperity with environmental conservation, recognizing the oceans' crucial role in supporting life on Earth. Key sectors within the Blue Economy include fisheries, tourism, renewable energy, maritime transportation, and aquaculture. As global populations rise and climate change impacts intensify, the Blue Economy is increasingly seen as essential for addressing global challenges and promoting sustainable development. Responsible fisheries management, such as implementing fishing quotas and creating marine protected areas, is crucial to combat overfishing and ensure food security. Sustainable aquaculture practices further alleviate pressure on wild fish stocks. Marine tourism, or ecotourism, promotes responsible practices that benefit local communities and the marine environment. Activities like beach clean-ups, wildlife conservation, and responsible diving practices are integral to this sector, supporting local economies and conservation initiatives. The Blue Economy significantly impacts the global economy, with the potential to double its contribution to the world's GDP by 2030. Sustainable marine sectors create jobs, drive economic growth, and contribute to energy security by harnessing renewable ocean-based energy sources. Additionally, the Blue Economy fosters innovation and technological advancements, enhancing competitiveness across various industries. Despite its potential, the Blue Economy faces challenges like overfishing, pollution, and climate change, which threaten marine biodiversity and ecosystems. Addressing these issues requires international cooperation, effective governance, and public awareness to promote sustainable practices and protect marine resource.

In this paper, a new approach to retrieve semantic images based on shape and geometric features of image in conjunction with multi-class support vector machine is proposed. Zernike moment as shape feature is to verify the invariance of objects for silhouette image. In addition, a set of geometrical features is to explore the objects shape using two features of rectangularity and circularity. Then the extracted features are normalized and employed for multi-class support vector machine either for learning or retrieving processes. The retrieving process relies on three main tasks which namely Query Engine, Matching Module and Ontology Manger, respectively. Query Engine is to build the input text or image query using SPARQL language. The matching module extracts the shape and geometric features of image’s objects and employ them to Ontology Manger which in turn inserts them in ontology knowledge base. Benchmark mammals have been conducted to empirically conclude the outcome of proposed approach. Our experiment on text and image retrieval yields efficient results to problematic phenomena than previously reported.

In this paper, we study the semilinear pseudo-parabolic equations $\displaystyle u_{t} - \triangle_{\mathbb{B}}u - \triangle_{\mathbb{B}}u_{t} = \left|u\right|^{p-1}u$ on a manifold with conical singularity, where $\triangle _{\mathbb{B}}$ is Fuchsian type Laplace operator investigated with totally characteristic degeneracy on the boundary $x_{1} = 0$. Firstly, we discuss the invariant sets and the vacuum isolating behavior of solutions with the help of a family of potential wells. Then, we derive a threshold result of existence and nonexistence of global weak solution: for the low initial energy $J(u_{0})<d$, the solution is global in time with $I(u_{0}) >0$ or $\displaystyle\Vert \nabla_{\mathbb{B}}u_{0}\Vert_{L_{2}^{\frac{n}{2}}(\mathbb{B})} = 0$ and blows up in finite time with $I(u_{0}) < 0$; for the critical initial energy $J(u_{0}) = d$, the solution is global in time with $I(u_{0}) \geq0$ and blows up in finite time with $I(u_{0}) < 0$. The decay estimate of the energy functional for the global solution and the estimates of the lifespan of local solution are also given.

Considering the significance of thermodynamic cycles in the global energy system, it is necessary to develop new general classes of thermodynamic cycles to relieve current energy and environmental problems. Inspired by the relationship between power cycles and refrigeration cycles, we realize that general classes of thermodynamic cycles should occur in pairs with opposite functions. Here we reverse class 1 heating cycles to obtain another new general class of thermodynamic cycles named class 2 heating cycles (HC-2s). HC-2s have two basic forms, and each contains six thermodynamic processes. HC-2s present the simplest and most general approach to utilizing the temperature difference between a medium-temperature heat source and a low-temperature heat sink to achieve efficient high-temperature heating. HC-2s fill the gaps that have existed since the origin of thermal science, and they will play significant roles in the global sustainable energy system.

Thermodynamic cycles are not only the core concepts of thermal science, but also key approaches to energy conversion and utilization. So far, power cycles and refrigeration cycles have been the only two general classes of thermodynamic cycles. While diverse types of systems have been developed to perform thermodynamic cycles, no new general classes of thermodynamic cycles have been proposed. Based on the basic principles of thermodynamics, here we propose and analyze a new general class of thermodynamic cycles named class 1 heating cycles (HC-1s). Two basic forms of HC-1s are obtained by connecting six essential thermodynamic processes in the proper order and forming a thermodynamic cycle. HC-1s present the simplest and most general approach to utilizing the temperature difference between a high-temperature heat source and a medium-temperature heat sink to achieve efficient medium-temperature heating and/or low-temperature cooling. HC-1s fill the gaps that have existed since the origin of thermal science, and they will play significant roles in energy conservation and emission reduction.

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.

Wood is poised to become a material of choice for future construction. When appropriately managed, it is a renewable material with unique mechanical properties. Beyond its inherent sustainability, the use of wood has a crucial role in addressing climate change concerns due a significantly lower energy consumption and emissions during manufacturing and transportation, while simultaneously acting as a carbon sink. Thus, there has been a growing demand for hardwoods for structural applications, including Castanea sativa Mill., the focal point of this investigation. Albeit in a limited capacity, Eurocode 5-2 offers friction coefficients for softwoods, but it falls short for hardwoods which it is a gap to bridge with this research. These coefficients play a critical role in numerical simulations involving friction, enabling the optimization of joints and, by extension, the overall structural integrity. Test samples were evaluated at 18% moisture content, which is typical of Service Class 2, for various orientations of timber-to-timber and timber-to-steel friction. Considering previous friction coefficients at 12% moisture content, the linear variation was evaluated at an intermediate value of 15%. The results provide an experimental database for numerical simulations and highlight the influence of moisture content on the coefficients, which increase linearly along moisture percentages.

Short-range Internet of Things (IoT) sensor nodes operating at 2.4 GHz need to provide ubiquitous wireless sensor networks (WSNs) with energy-efficient, wide-range output power (POUT) and fully integrated on a single chip for wireless body area networks (WBANs) and wireless personal area networks (WPANs) using low-power Bluetooth (BLE) and Zigbee standards. The proposed fully integrated transmitter (TX) utilizes a digitally controllable current-mode class-D (CMCD) power amplifier (PA) with a second harmonic distortion (HD2) suppression to reduce VCO pulling in an integrated system while meeting harmonic limit regulations. The CMCD PA is divided into 7-bit slices that can be reconfigured between differential and single-ended topologies. Duty cycle distortion compensation is performed for HD2 suppression, and an HD2 rejection filter and a modified C-L-C low-pass filter (LPF) reduce HD2 further. Implemented in a 28 nm CMOS process, the TX achieves a wide POUT range of from 12.1 to -31 dBm and provides a maximum efficiency of 39.8% while consuming 41.1 mW at 12.1 dBm POUT. The calibrated HD2 level is -82.2 dBc at 9.93 dBm POUT, resulting in a transmitter figure of merit (TX_FoM) of -97.52 dB. Higher order harmonic levels remain below -41.2 dBm even at 12.1 dBm POUT, meeting regulatory requirements.

Load balancing to alleviate network congestion remains a critical challenge in Vehicular Ad-Hoc Networks (VANETs). During route and response scheduling, RSUs risk being overloaded beyond their calculated capacity. Despite recent advancements like RSU-based load transfer, NP-Hard hierarchical geography routing, RSU-based MAC schemes, simplified clustering, and network activity control, a significant gap persists in employing a load-balancing server for effective traffic management. We propose a server-based network congestion handling mechanism (SBNC) in VANETs to bridge this gap. Our approach clusters RSUs within specified ranges and incorporates dedicated load balancing and network scheduler RSUs to manage route selection and request-response scheduling, thereby balancing RSU loads. We introduce three key algorithms: optimal placement of dedicated RSUs, a scheduling policy for packets/data/requests/responses, and a congestion control algorithm for load balancing. Using the VanetMobiSim library of Network Simulator-2 (NS-2), we evaluate our approach based on residual energy consumption, end-to-end delay, packet delivery ratio (PDR), and control packet overhead. Results indicate substantial improvements in load balancing through our proposed server-based approach.

Daylighting design is not only a stage of dimensioning glazed surfaces to provide sufficient natural light to an occupied space, but also a method of analysing how this matter can be achieved without unwanted effects, such as: gains and losses of heat, glare, and variations of the daylighting intensity, at various indoor distances and levels. According to the international regulations in force, glazed surfaces must be designed with a careful balance between the admission of natural light and thermal performance, such as heat loss in winter and heat gain in summer. In a novel project, it is essential to determine the optimal ratio of window-to-wall surface area. This necessity arises because the materials constituting the glazed surfaces of windows typically exhibit lower insulation properties compared to those of the building's walls. This methodology could be applied to building renovations when envelope modifications are possible and suitable. The case study presented in the paper highlights the energy consumed due to a group of windows with a large glazed area in an existing building, located in a temperate continental climate area. The energy consumption results from supplementary artificial lighting required to maintain adequate illumination for indoor activities and that to counterbalance heat loss during colder periods are evaluated. The analysis performed by modifying the glazed surface led to the identification of an optimum value of windows dimensions for an energy consumed minimum.

We present a case report of a 26-years-old female suffering from Coffin Siris Syndrome, who underwent orthodontic treatment and surgery to solve her malocclusion and improve her aes-thetics and functional occlusion. Methods: The presurgical phase involved multibracket self-ligating attachments, Damon prescription. The patient underwent maxillofacial surgery to correct the severe skeletal malocclusion and relocate the bone bases in the right relationship. The post-surgical orthodontic treatment was performed to complete the alignment. Results: The pa-tient improved her aesthetics and functional abilities. Conclusions: Syndromic patients can un-dergo orthodontictreatment if comorbidities and collaboration allow it. Support and collaboration of families and psychotherapists must be considered, but clinical cases of syndromic patients can be faced and solved. Obviously, each syndromic patient is considered unique, and the risk-benefit ratio must be correctly assessed for each one.

The fetal splenic artery pulsatility index (PI) is a parameter that reflects fetal well-being and has been used as a predictor of adverse pregnancy outcomes. The aim of this study was to investigate the predictive value of splenic artery PI in GDM class A1 cases for NICU admission. In this prospective case controlled study, only sixty single pregnancy cases diagnosed with gestational diabetes mellitus class A1 were evaluated. Fetal splenic artery Doppler parameters Peak systolic velocity (PSV), Pulsatility index (PI), Resistivity index (RI) and End diastolic velocity (EDV) were measured in all cases. The rate of re-quirement of need of neanoatal intensive care unit was noted. In cases with need fetal intensive care, fetal splenic PI index was found to be statistically significantly lower than in healthy cases without it (0.94 ± 0.29 vs. 1.70 ± 0.53, respectively, p

Most cervical cancer cases are a result of persistent high-risk Human papillomavirus (HPV) infections. Cervical cancer is prevalent in LMICs especially in SSA where it is second most prevalent cancer. In South Africa, it is the second most common cancer affecting women aged between 15 and 44 years. The host immune response has been shown as an important factor in controlling the progression or regression of high-risk HPV infection of the cervix to cervical cancer. The risk of cervical cancer is known to be influenced by the host's genetic diversity, particularly by immune response-regulating genes such as the Human Leukocyte Antigen (HLA) class I and II genes. HLA class I genes present viral peptides to CD8+ T cells which are restricted and pre-programmed for cytotoxic functions. There is very little known about the HLA genes of South African women and how these influence outcome of HPV infections. This review aims to understand the role and influence of HLA class I genes in HPV clearance, persistence, and CD8+ T cell mediated immunity in African women by examining existing literature in this area. Understanding this role can influence and inform therapeutic vaccine design that is population specific.

The Internet of Things (IoT) signifies a revolutionary technological advancement, enhancing various applications through device interconnectivity while introducing significant challenges due to these devices' limited hardware and communication capabilities. To navigate these complexities, the Internet Engineering Task Force (IETF) has tailored the Routing Protocol for Low-Power and Lossy Networks (RPL) to meet the unique demands of IoT environments. However, RPL struggles with traffic congestion and load distribution issues, negatively impacting network performance and reliability. This paper presents a novel enhancement to RPL by integrating learning automata designed to optimize network traffic distribution. This enhanced protocol, the Learning Automata-based Load-Aware RPL (LALARPL), dynamically adjusts routing decisions based on real-time network conditions, achieving more effective load balancing and significantly reducing network congestion. Extensive simulations reveal that this approach outperforms existing methodologies, leading to notable improvements in packet delivery rates, end-to-end delay, and energy efficiency. The findings highlight the potential of our approach to enhance IoT network operations and extend the lifespan of network components. The effectiveness of learning automata in refining routing processes within RPL offers valuable insights that may drive future advancements in IoT networking, aiming for more robust, efficient, and sustainable network architectures.

Sick sinus syndrome (SSS) is a disease of sinus rhythm abnormalities. This study aimed to establish an SSS model by injecting 3% NaOH into the jugular vein(JV). Sprague-Dawley rats were randomly divided into sham, 3% NaOH, and 10% NaOH groups. Anesthetized rats underwent electrocardiogram recording before right JV exposure. Rats in the experimental groups received NaOH injections at 0.01 ml/s, while the sham group did not. Model success was evaluated after one hour. Heart rate(HR) and rhythm were documented at 1 hour, 1 week, and 2 weeks post-molding, including HE staining, membrane ion transcription and expression, and calcium ion content test. In the 3% NaOH group, the model establishment rate was 87%, and the mortality rate was 13%, whereas in the 10% NaOH group, they were 57% and 43% respectively (P < 0.01). The HR of the 3% NaOH group decreased at multiple time points post-molding (P < 0.001), presenting as arrhythmias such as sinus bradycardia. The JV sustained less damage, with necrosis of P cells, changes in membrane ion channels, and reduced calcium ion distribution. In conclusion, JV injection of 3% NaOH can establish the SSS model and benefit animals.

Passive balancing is a traditional choice for lithium-ion battery packs although it is regarded as highly inefficient. One opportunity to improve traditional balancing in sub-medium size battery packs is to combine two balancing methods: passive and active. Statistical analysis have been performed to rationalize battery pack division in smaller packs and use of higher level selective charging method to decrease losses of passive balancing. An experimental balancing prototype have been designed and tested for efficiency. Results indicate energy performance degradation if cells of the pack are with low capacity variation. Proposed balancing approach could improve performance under high cell mismatch conditions.

This article presents a review of resources for batteries with a specific focus on sustainability aspects related to raw materials, electrode and electrolyte chemistries suitable for the automobile industry. We emphasize the importance of prioritizing sustainability in battery technology evolution, which is expected to shift towards earth-abundant materials. One notable advancement in this direction is the utilization of glass solid-state electrolytes, known for their high ionic conductivity, low electronic conductivity, and high dielectric constant. However, our review suggests that further research should explore synthesis methods to modify anisotropic properties, enhancing the performance of solid-state electrolytes, particularly in facilitating the flow of ions.

The primary objective of this study was to identify a rapid and noninvasive methodology for effectively differentiating various herbal species cultivated in Eastern Poland. Typically, a plant's chemical profile is obtained using chromatographic methods; however, spectroscopic methods can serve as a complementary or independent tool for determining similarities in the analyzed samples. The investigation centered on leaves of plant species from the Lamiaceae family, encompassing basil, lavender, oregano, sage, and thyme. UV-VIS and ATR-FTIR spectra of dried plant samples' powder were recorded and subsequently analyzed using chemometric tools. This paper assesses the relative performance of different discrimination methods, including both traditional methods based on machine learning and those dealing with specimens associated with a specific category. Among the methodologies tested, SIMCA showed the best performance, particularly effective for small training sets.

This study investigates the relationship between energy metals and precious metals to assess their suitability as safe haven assets in clean energy investment portfolios. It focuses on the impact of events in 2020 and 2022, characterized by substantial investments in clean energy. The research reveals that, except for Nickel Futures (NICKELc1), energy metals are positively linked with clean energy indexes. This suggests that they can serve as a secure investment option for green investors looking to diversify their portfolios. As a result, the study dismisses the initial question, indicating that energy metals can indeed be considered safe havens within the context of clean energy investments. Additionally, the research reinforces prior findings that precious metals like gold, silver, and platinum possess safe haven characteristics in relation to specific clean energy stock indexes. These results have significant implications, particularly given the increasing investments in clean energy stocks and the recurring uncertainties in the market. In summary, this study supports the idea that both energy metals and precious metals can play valuable roles in clean energy portfolios, providing stability during turbulent times in the market.

The rationale of this study is to explore underlying sustainability assessment constructs in the context of WCSM implementation as a key focus by manufacturing industries in the journey of achieving world-class status in their manufacturing operations. This study primarily intends to collect primary data from 143 plant managers and supervisors through the structured questionnaires at select manufacturing companies in south India on the basis of area-cum-purposive sampling technique. The survey questionnaire is designed exclusively to reflect the sustainability assessment dimensions in terms of social, economic and environmental issues to examine the impetus of WCSM implementation on the competitive advantage of manufacturing companies. The empirical findings of this study clearly revealed that sustainable manufacturing practices, sustainability programs, creation of sustainable products, safety for employee, community and product, operational efficiency and integration of sustainability goals in day-to-day manufacturing activities were considered to be the key indicators by the manufacturing firms in their never-ending journey towards becoming the world class. Moreover, the WCSM practices & techniques adopted by sample manufacturers have a substantial positive influence on the achievement of competitive advantage in their manufacturing and business operations.

The article presents a methodology for modeling the impact of factors of both on-farm activity and environmental factors on the resulting indicators of an international company. The dataset includes the company's profit, revenue, valuation, share price, and market share from 2012 through 2022. This empirical period is optimal for such a type of modeling. Modeling the impact of carbon emissions factors on the resulting indicators is considered based on complex systems theory. Endogenous model variables include indicators of the development of the international company. Predefined model variables reflect the influence of both the external environment and internal factors on the development indicators of an international company. An approach of picture fuzzy rough sets based on time series of endogenous and exogenous variables can provide an opportunity to analyze and consider the consequences of feedback changes in the systems of which they are a part. Based on the results of the analysis of the model construction, it is concluded that picture fuzzy rough sets can be an excellent way to model interdependent social processes.

Introduction: maxillary hypoplasia and mandibular asymmetry may be corrected with orthognathic surgery at the completion of growth. Many patients require segmental Le Fort I osteotomies to obtain the most stable results. Unfortunately, if Invisalign is chosen for pre-surgical preparation, ClinCheck © software does not allow for prediction when these complex surgeries are planned. The objective of this paper was to show the potential of aligners beyond its software in most complex cases like multiple-piece maxillary osteotomies in cleft and non-cleft patients. Method: Thirteen patients who underwent pre-surgical treatment with clear aligners-Invisalign (study group) were matched in terms of diagnosis, surgical procedure, and orthodontic complexity with thirteen patients treated using fixed appliances (control group). A technique based on simple superimposition was devised to enhance the software for creating a suitable occlusion for segmental pre-surgical treatments. Virtual curves following the lower arch were employed to guide the correct pre-surgical positions of the upper teeth. The amount of impressions required in both groups to achieve satisfactory pre-surgical alignment of the segmented arches was compared. Results: one or no refinement phase were needed in the Invisalign group to reach an acceptable pre-surgical occlusion while the amount of pre-surgical impressions taken in order to reach adequate coordination with fixed appliance treatment was slightly higher. The average overall treatment duration did not show a statistically significant variation between the groups. Conclusions: it appears that clear aligner could serve as an effective treatment for individuals with skeletal discrepancies, including cleft and craniofacial anomalies, necessitating segmental Le Fort I osteotomies when aided by the suggested superimposition approach during digital pre-surgical movement simulations. More sophisticated softwares will certainly be implemented in the future.

Incorporating AI tools like ChatGPT into higher educational settings has been beneficial for education, yet the extent of user satisfaction with the information provided by these tools, known as user information satisfaction (UIS), remains underexplored. Our study introduces a UIS model specifically designed for ChatGPT's application in the educational sector. Drawing from established UIS theory, we crafted a model centered around seven essential factors that influence the effective use of ChatGPT, aiming to guide both educators and learners in overcoming common challenges such as plagiarism and ensuring the ethical use of AI. We gathered data from Indonesian university participants and applied Structural Equation Modeling using Smart-PLS 4.0 for our data analysis. The results reveal that completeness, precision, timeliness, convenience, and the format of information as the most influential factors driving user satisfaction with ChatGPT. Interestingly, our research indicated that the accuracy and reliability of the information, typically deemed paramount, were not the primary concerns in the academic use of ChatGPT. Our findings lead to a recommendation for a cautious approach to the integration of ChatGPT in higher education. We advocate for a strategic use that recognizes its innovative potential while also acknowledging its limitations, thus ensuring a responsible and effective application in educational contexts. This balanced perspective is crucial for integrating AI tools into the academic fabric without compromising educational integrity or quality.

This paper explores the challenges and opportunities of balancing cultural preservation and tourism investment in historic Syrian cities in the post-war period. The Syrian conflict has caused significant damage to the country's cultural heritage, including its ancient cities, which are UNESCO World Heritage sites. Key issues raised include what challenges local governments to face, the impact of the war on the preservation of historic sites, how tourism can be used as a tool for economic development, and how to ensure that cultural heritage is preserved while also allowing for tourism investment. To gain an in-depth understanding of the old Syrian cities, extensive research has been conducted, including a review of existing literature and interviews with key stakeholders. The researcher found that rebuilding historic Syrian cities is a complex task that requires balancing cultural preservation with tourism investment. It will require collaboration between government officials, preservationists, developers, and local communities to ensure that these cities are restored in a way that respects their history and culture while also promoting economic growth through tourism.

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

The convergence of Class Incremental Learning (CIL) and Machine Unlearning (MU) is a rapidly developing field in machine learning, especially relevant in adaptive and privacy-sensitive environments like finance. CIL enables models to learn new data classes over time without losing previously acquired knowledge, while MU focuses on selectively forgetting specific data to comply with privacy laws or mitigate security risks. In this paper, we examine the theoretical foundations and practical applications of both approaches, particularly in the financial domain. We explore how these two paradigms interact and complement each other, discuss key algorithms, and present examples to illustrate their applications in areas such as portfolio management, fraud detection, and data privacy. Finally, we explore challenges and potential future directions in achieving optimal synergy between CIL and MU.

Making computing machines mimic living organisms has captured the imagination of many since the dawn of digital computers. However, today’s artificial intelligence technologies fall short in replicating even the basic autopoietic and cognitive behaviors found in primitive biological systems. According Charles Darwin, the difference in mind between humans and the higher animals, great as it is, certainly is one of degree and not of kind. Autopoiesis refers to the behavior of a system that replicates itself and maintains its own identity and stability while facing fluctuations caused by external influences. Cognitive behaviors model the system’s state, sense internal and external changes, analyze, predict and take action to mitigate any risk to its functional fulfilment. How did intelligence evolve? what is the relationship between the mind and body? Answers to these questions should guide us to infuse autopoietic and cognitive behaviors into digital machines. In this paper we use recent advances in our understanding of general theory of information, and the role of structures in managing the transformations between information and knowledge to pave the path to infuse autopoietic and cognitive functions into digital computing and build a new class of intelligent machines going beyond the current state of the art.

Quantum mechanics could be studied through polynomial algebra, as has been demonstrated by a work (“An approach to first-principles electronic structure calculation by symbolic-numeric computation” by A. Kikuchi). We carry forward the algebraic method of quantum mechanics through algebraic number theory; the basic equations are represented by the multivariate polynomial ideals; the symbolic computations process the ideal and disentangle the eigenstates as the algebraic variety; upon which one canbuild the Galois extension of the number field, in analogy with the univariate polynomial case, to investigate the hierarchy of solutions; the Galois extension is accompanied with the group operations, which permute the eigenstates from one to another, and furnish the quantum system with a non-apparent symmetry. Besides, this sort of algebraic quantum mechanics is an embodiment of the class field theory; some of the important consequences of the latter emerge in quantum mechanics. We shall demonstrate these points through simple models; we will see the use of computational algebra facilitates such sort of analysis, which might often be complicated if we try to solve them manually.

Orthognathic surgery is used to modify anomalies in maxillomandibular position; this process can significantly affect the anatomy of the airway and cause functional changes. This study aims to define the impact of mandibular maxillary movement on the airway of subjects with dentofacial deformity. A retrospective study was conducted on subjects with Angle class II (CII group) and Angle class III (CIII group) dentofacial deformities. The subjects were treated by bimaxillary surgery; for all of them, planning was performed with software and 3D printing. Cone beam computed tomography (CBCT) was obtained 21 days before surgery and 6 months after surgery and was used for planning and follow-up with the same conditions and equipment. Was used the superimposition technique to obtain the maximum and minimum airway areas, and total airway volume. The data were analyzed with the Shapiro-Wilk test and Student’s t-test, while Spearman's test was used to correlate the variables, considering a value of p&lt;0.05. Seventy-six subjects aged 18 to 55 years (32.38 ± 10.91) were included: 46 subjects were in CII group, treated with a maxillo-mandibular advancement, and 30 subjects were in the CIII group, treated with a maxillary advancement and a mandibular setback. In CII group, a maxillary advancement of +2.45 mm (±0.88) and a mandibular advancement of +4.25 mm (±1.25) were observed, with a significant increase in all the airway records. In the CIII group, a maxillary advancement of +3.42 mm (±1.25) and a mandibular setback of -3.62 mm (±1.18) was noted, with no significant changes in the variables measured for the airway (p&gt;0.05). It may be concluded that maxillo mandibular advancement is an effective procedure to augment the airway area and volume in the CII group. On the other hand, in subjects with mandibular prognathism and Angle class III operated with the maxillary advancement and mandibular setback lower than 4 mm it is possible to not reduce the areas and volume in the airway.

Karst wetlands are being seriously damaged, and protecting it has become an important matter. Karst vegetation is the essential component of wetland and plays an important role in in the ecological functions of wetland ecosystems. Classifying karst vegetation is important for karst wetlands protection and management. This paper addressed to classify karst vegetation in Huixian National Wetland Park, located in China using the improved SegNet Deep-Learning Algorithm and UAV images. This study proposed a method to fuse single-class SegNet models using the maximum probability algorithm for karst vegetation classification, and compared with object-based RF classification and multi-class SegNet classification, respectively. This paper evaluated the performance of multi-class SegNet model and fusion of single-class SegNet model with different EPOCH values for mapping karst vegetation. A new optimized post-classification algorithm was proposed to eliminate the stitching traces caused by SegNet model prediction. The specific conclusions of this paper include the followings:(1) fusion of four single-class SegNet models produced better classification for karst wetland vegetation than multi-class SegNet model, and achieved the highest overall classification accuracy (87.34%); (2) The optimized post-classification algorithm was able to improve prediction accuracy of SegNet model, and it could eliminate splicing traces; (3) The karst wetland vegetation classifications produced by single-class SegNet model outperformed multi-class SegNet model, and improved classification accuracy(F1-Score) between 10%~25%;(4)The EPCOH values and textural feature important impact on karst wetland vegetation classifications. The SegNet model with EPCOH 15 achieved greater classification accuracy(F1-Score) than the model with EPOCH 5 or 10. The textural feature improved improves the capability of the SegNet model for mapping karst vegetation;(5) Fusion of single-class SegNet models and object-based RF model could provide high classifications results for karst wetland vegetation, and both achieved greater 87% overall accuracy.

This study explores individual and contextual risk factors for the onset of work interfering with private life (WIL) and private life interfering with work (LIW) among self-employed men and women across European countries. It also studies the relationship between interference (LIW and WIL) and wellbeing among self-employed men and women and the effect of macro level risk factors. Data from the fifth round of European Working Conditions Survey was utilized and a sample of self-employed men and women with active businesses was extracted. Applying multilevel regressions, results show that though business characteristics are important for level of WIL, time demand is the most evident risk factor for WIL and LIW. There is a relationship between wellbeing and WIL and LIW respectively, and time demands is the most important factor in this relationship. Gender equality on the labor market did not relate to level of interference, nor did it mediate the relationship between interference and wellbeing. However, the main and most important risk factor for experiencing WIL and LIW and for how interference relate to wellbeing is gender relation processes in work and life, both on individual and contextual level.

Due to the retrognathic mandible, an orthodontist frequently observes skeletal class II malocclusion in growing patients. The preferred technique for analyzing and monitoring the temporomandibular joint alterations caused by the functional device is magnetic resonance imaging [MRI]. The study aimed to assess and compare changes in the articular disc's dimension and location before and following the use of various class II correctors to correct the class II molar relation using MRI technology. Methods: 39 adolescent patients aged 15-23 years old [24 girls and 15 boys] who were diagnosed with skeletal Class II division 1 malocclusion based on clearly defined criteria underwent treatment with either the power scope II [PS], Sabbagh Advanced Repositioning Appliance [SARA] and or Dynaflex [DF] with fixed appliances. For fixed functional treatment, the average length of treatment was 5-7 months. The changes in the articular disc dimension and position and condylar position were assessed. The study was done at the Department of Orthodontics at the College of Dentistry, Hawler Medical University in Erbil City. Results: Group DF has a significant effect on the condylar position index and posterior joint space on the closed mouth and disc length on both open and close mouth techniques, and in the group SARA, disc position, and condylar position index on closed mouth technique have shown that significant differences were observed. Conclusion: Class II malocclusions can be effectively corrected by fixed functional appliances. The statistical significance forward movement of the condyle in the glenoid fossa and the posterior movement of the articular disc concerning the condylar head was observed. The mechanism of action of functional appliances appears to include the forward migration of the C-GF complex.

The Gulf of Mannar, a UNESCO World Biosphere Reserve, faces severe overfishing and habitat degradation threats. In this study, we investigate the pivotal role of ecosystem services in sustaining local livelihoods and overall well-being. By conducting a comprehensive survey of 480 respondents across two districts, we gathered extensive data on demographics, livelihoods, fishing practices, reliance on ecosystem services, and community management participation. The analysis reveals a critical dependence on the Gulf's resources (income, food security, traditions) with gender disparities (men fish, women in pre/post-harvest), but fishing is only allowed in the 10km buffer zone (not the core zone). The findings emphasize the promise of community-based strategies, such as Marine Protected Areas and reviving co-management committees, for achieving sustainable fisheries management. However, we also identify gaps, including the need for more nuanced well-being indicators and improved models for community management participation. To address these challenges, we advocate for sustainable fishing practices, tackling social inequities, especially gender disparities in resource access and decision-making, and investing in healthcare, education, and social safety nets for fishing communities. Promoting alternative livelihoods can alleviate pressure on fish stocks, and empowering local communities through capacity building and community-based management initiatives is crucial for ensuring the long-term sustainability of both the Gulf of Mannar ecosystem and the well-being of its dependent communities. This multifaceted approach holds significant promise for balancing ecological health with human prosperity.

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

Finnish government's carbon neutrality goal by 2035 requires integration of renewable energy sources into the power grid. Considering the stochasticity of these resources, additional sources of flexibility are necessary to balance supply and demand in the power grid. District heating network (DHN) operators in Finland plan to shut down fossil-fuel-based combined heat and power plants and electrify heating systems by deploying heat pumps (HPs) and electric boilers. Techno-economic analysis and optimal operation of DHN-connected HPs and electric boilers in providing ancillary balancing services considering the 15-minute granularity in the balancing markets in Finland were investigated. The objective was to maximize the potential revenue for DHN operators gained from the day-ahead electricity market and frequency containment reserve (FCR) balancing markets. Three inter-connected DHNs in the Helsinki metropolitan area were optimized considering the reference year 2019 and each operator's decarbonization strategies for 2025. HPs could gain the highest profit from FCR-D up-regulation market, while the electric boiler may gain considerable profits from FCR-D down-regulation market. Compared to other balancing markets studied, the FCR-N market had a limited profit margin. Sensitivity analysis indicated that spot electricity prices and CO2 emission allowance prices have a significant impact on the profit from balancing markets.

Drag-dominant tidal turbine energy holds tremendous clean energy potential but faces significant hurdles as unsuitability of Disk Actuator theory due to the varying swept blockage area, unaccounted bypass flow downstream interaction and rotor parasitic drag whereas Blade Element Momentum theory is computably effective for majorly 3-blade lift-dominated aerofoil. This study proposes a novel method to find the optimal TSR of any turbine with a cost-effective and user-friendly Moment Balancing algorithm to support robust tidal energy development. Performance analysis CFD study of Pinwheel and Savonius tidal turbines was carried out. Dynamic TSR matrix was developed with varying rotational speeds and fluid velocities for reliability, unlike previous works simulated at a fixed fluid velocity. Novel parameters such as thrust and idle moment are introduced as functions of only inlet fluid velocity and rotational speed respectively. These relationships are verified through regression analysis, and the turbines' net moment equations are established based on these parameters. Rotational speed was a reliable predictor for Pinwheel's idle moment, while inlet velocity was a reliable predictor for thrust moment in both models. The optimal (Cp, TSR) values for Pinwheel and Savonius turbines were (2.37, 0.223) and (0.63, 0.16) respectively, within an acceptable error range for experimental validation.

This study is designed to assess and improve apparel sewing section efficiency and productivity throughout line balancing. Apparel industry is one of the oldest and among the most global industry, being primarily concerned with the design and production of cloth and their supply. The central process in apparel manufacturing is the joining together of components which is known as the sewing process , which is the most labour intensive type of manufacturing process .Proper utilization of resources in garment sewing section is more critical to enhance the performance of the apparel industry by reducing production cost and minimizing wastage. For effective utilization of resources in the sewing section, good line balancing is important to increase productivity and production efficiency. This research was a design to analyse and improve the assembling line in the case of Telaje garment manufacturing and sales plc. The study was first conduct observations in the production floor and start work with the selection of sewing line in the garment production process. Among the nine lines of the factory select one on the production floor and one garment ordered product known as five pocket men’s jeans trousers are selected. For this study, both qualitative and quantitative research approaches were employed. Both primary and secondary data sources are used to detail the collected relevant data to understand the current efficiency scenario of the factory. The main challenges to minimizing line efficiency and productivity with the expected performance measurement are improper utilization of resources and improper implementation of line balancing in sewing section, therefore this thesis work shows that the bottleneck process and consequence solution will be searched, and finally significantly improving the productivity by 418 unit products/ day and, hence the efficiency will increase from 28.83% to 50.04% of the line

Based upon Maxwell's equations, it has long been established that oscillating electromagnetic (EM) fields incident upon a metal surface decay exponentially inside the conductor, leading to a virtual EM vacuum at sufficient depths. Magnetic resonance imaging (MRI) utilizes radiofrequency (r.f.) EM fields to produce images. Here we present the first visualization of a virtual EM vacuum inside a bulk metal strip by MRI, amongst several novel findings. We uncover unexpected MRI intensity patterns arising from two orthogonal pairs of faces of a metal strip, and derive formulae for their intensity ratios, revealing differing effective elemental volumes (voxels) underneath these faces. Further, we furnish chemical shift imaging (CSI) results that discriminate different faces (surfaces) of a metal block according to their distinct nuclear magnetic resonance (NMR) chemical shifts, which holds much promise for monitoring surface chemical reactions noninvasively. Bulk metals are ubiquitous, and MRI is a premier noninvasive diagnostic tool. Combining the two, the emerging field of bulk metal MRI can be expected to grow in importance. The fundamental nature of results presented here may impact bulk metal MRI and CSI across many fields.

As rotary machines have become more complicated, balancing processes have been classified as a vital step in condition monitoring to ensure machines operate both reliably and safely. This is especially important for flexible machines which normally work at rotations speeds above critical limits. Imbalance is a common problem in flexible rotating machinery that can lead to extreme vibration and noise levels. This is one of the major reasons for studying various balancing methods applied to the vibration response of rotating machines. Recently, the relation between acoustic and vibration response during a rotary machine balancing process based on the Four-Run method has been presented for constant speed machines. This method cannot be applied to machines in start-up or shut-off. Hence, by considering the acoustic and vibration responses of a machine between its critical speeds, this research presents a new innovative speed-variant balancing method based on the original Four-Run method, named as "Peak to Peak for Critical Speeds (PPCS)". The proposed method consists of two major types of application: the first is in the Run-up of the machine and the second is in Shut-down. Experimental laboratory results show that the PPCS method can be implemented for speed-variant and flexible rotary machines during run-up or shut-down transient processes based on acoustic and vibration measurements. As a phase-less and a contactless method, the PPCS can be employed as an innovative and readily available method for condition monitoring in the future.

Generalized Bayesian (GB) is a Bayesian approach based on the learning rate parameter (LRP) () as a fraction of the power of the likelihood function. In this paper, we consider the GB method to perform inference studies for a class of exponential distributions. Generalized Bayesian estimators (GBE) and generalized empirical Bayesian estimators (GEBE) for the parameters of the considered distributions were obtained based on the censored type II samples. In addition, generalized Bayesian prediction (GBP) and generalized empirical Bayesian prediction (GEBP) were considered using a one-sample prediction scheme. Monte Carlo simulations were performed to compare the performance of the GBE and GEBE estimation results and the GBP and GEBP prediction results for different values of the LRP.

G-protein coupled receptors (GPCRs), one of the largest superfamilies of cell-surface receptors, are heptahelical integral membrane proteins that play critical roles in virtually every organ system. G-protein-coupled receptors operate in membranes rich in cholesterol, with an imbalance in cholesterol level within the vicinity of GPCR transmembrane domains affecting the structure and/or function of many GPCRs, a phenomenon that has been linked to several diseases. These effects of cholesterol could result in indirect changes by altering the mechanical properties of the lipid environment or direct changes by binding to specific sites on the protein. There are a number of studies and reviews on how cholesterol modulates class A GPCRs, however, this area of study is yet to be explored for class C GPCRs, which are characterized by a large extracellular region and often form constitutive dimers. This review highlights specific sites of interaction, functions, and structural dynamics involved in the cholesterol recognition of the class C GPCRs. We summarize recent data from some typical family members to explain the effects of membrane cholesterol on the structural features and functions of Class C GPCRs and speculate on their corresponding therapeutic potential.

In this paper we introduce the Adomian decomposition method for a class of first order fuzzy dynamic equations on arbitrary time scales for existence of solutions. The results are provided with suitable numerical examples.

Towards smart batteries for electric vehicles (EVs) smart algorithms to control battery packs, mainly reconfigurable batteries, have to be developed. This work proposes a reinforcement learning (RL) algorithm to balance the State of Charge (SoC) of reconfigurable batteries based on the topologies half-bridge and battery modular multilevel management (BM3). As RL algorithm, Amortized Q-learning (AQL) is implemented, which allows enourmous numbers of possible configurations of the reconfigurable battery to be controlled, as well as the combination of classical controlling approach and machine learning methods. This enables safety mechanisms in control. As a neural network of the AQL a Feedforward Neuronal Network (FNN) is implemented consisting of three hidden layers. The experimental evaluation using a 12-cell hybrid cascaded multilevel converter illustrates the applicability of the method to balance the SoC and maintain the balanced state during discharge. The evaluation shows a 20.3% slower balancing compared to the classical. Nevertheless, AQL shows great potential to be applied for multiobjective optimizations as an applicable RL algorithm for control in power electronics.

The IDS serves as a security system that maintains constant surveillance over network traffic and host systems in order to identify any security breaches or potentially concerning activities. Recently. the rise in cyber-attacks has driven the necessity for the development of automated and intelligent network intrusion detection systems. These systems are designed to learn the typical patterns of network traffic, allowing them to identify any deviations from normal behaviour, which can be classified as anomalous or malicious. Machine learning methods are widely used to exhibit a satisfactory effectiveness in detecting malicious payloads in the network traffic. While the volume of the data generated from IDS is increasing exponentially results in the emergence of substantial security risks, it highlighted the imperative to strengthen network security. The performance of traditional machine learning methods depends on the dataset and the data balance distribution in it. while most of IDS datasets suffer from unbalancing, this limits the performance of the machine learning method used in the system and results in missed detections and false alarms in the conventional IDSs. To address this issue, this paper presents a new model-based Generative Adversarial Network (GAN) called TDCGAN to enhance the detection rate of less of minor class in the dataset while maintaining efficiency. The proposed model consists of one generator and three discriminators with an election layer at the end of architecture. The UGR’16 data set is used for evaluation purposes. In order to demonstrate the efficacy of our proposed model, various machine learning algorithms have been utilized for comparison. The experimental findings have determined that TDCGAN presents an efficient resolution for addressing imbalanced intrusion detection and surpasses the performance of other oversampling machine learning methods.

In modern inverter applications, multilevel converters are used thanks to their capability of reducing the passive fi lter volumes, improving the overall THD, sharing the power sources across the several converter cells, and providing a reconfi guration ability in case of failure. However, increasing the number of cells makes more complex the control of the converter. This article proposes a decentralized control technique based on the use of elementary modular controllers, associated with each converter cell and communicating with their close neighbors to obtain the balancing of the powers they supplied. Each modular controller can be dynamically removed or added to allow reconfi guration of the converter for functional safety purpose. This method is applied to a 240V/3A 5-cells Cascaded Full-Bridge Multilevel Converter. The response of the system to load transients and cell voltage disturbances demonstrates the robustness of the proposed decentralized control method. Thanks to the use of modular controllers, the number of levels of the converter can be extended to the order N without adding complexity to its control.

Tourism carrying capacity remains pivotal for ensuring sustainable development within protected terrains, accentuating the balance between conservation, socio-economic benefits, and visitor satisfaction. This study delves into the intricate dimensions of this balance, focusing on the Katon-Karagay National Park (KKNP). Merging comprehensive empirical data accrued from fieldwork within the park, calculations surrounding recreational loads, and a synthesis of relevant literature, the study designed a methodology. This included calculated techniques incorporating ecological and tourist social capacities, the psychocomfort approach, and an ongoing monitoring approach, supplemented by exclusive data from the park's administration. Key findings underscored the significance of an adaptive balance, revealing specific permissible recreational loads for different park zones. For instance, zones of quiet recreation evidenced up to 5 people/ha, while active recreation zones fluctuated between 20 to 100 people/ha. Monitoring evidenced critical environmental changes, shaping recommendations for effective tourism flow regulation, ensuring alignment with established norms and ecological preservation. The exploration substantiates the indispensability of a meticulously structured approach to ascertain tourism carrying capacity, emphasizing the harmony achievable between environmental sanctity and human pursuits. KKNP's empirical data serves as a paradigm, guiding sustainable tourism frameworks for similar ecologically sensitive regions.

Robot Operating System 2 (ROS 2) is a robotic software that uses a set of Quality of Service (QoS) policies to manage the quality of robot data transmissions in a network, such as the RELIABLE and KEEP_LAST options. In ROS 2 node communication, the RELIABLE connection guarantees that all message data can be properly sent from the publisher to the subscriber. However, strict reliability is not guaranteed if the RELIABLE connection uses the KEEP_LAST option to transmit the robot data in the publish-subscribe communication. This study aims to analyze the efficiency of local cache, cache control, and QoS balancing optimization to improve ROS 2 node communication when using the RELIABLE and KEEP_LAST options to transmit multi-robot data in Aggregated Robot Processing (ARP) architecture. Our idea in local cache and cache control is to streamline the sensor data output before processing it when the sensor device produces the data with the same value in a row. Furthermore, QoS balancing optimization aims to balance the DEPTH and DEADLINE QoS configuration to determine the rates and buffer size in ROS 2 node communication. This study shows that combining local cache and QoS balancing optimization improves multi-robot data transmission and cooperation in ARP architecture.

In this paper, we introduce a new class of Hermite-Fubini numbers and polynomials and investigate some properties of these polynomials. We establish summation formulas of these polynomials by summation techniques series. Furthermore, we derive symmetric identities of Hermite-Fubini numbers and polynomials by using generating functions.

A unified presentation of a class of Humbert’s polynomials in two variables which generalizes the well known class of Gegenbauer, Humbert, Legendre, Chebycheff, Pincherle, Horadam, Kinnsy, Horadam-Pethe, Djordjević , Gould, Milovanović and Djordjević, Pathan and Khan polynomials and many not so called ’named’ polynomials has inspired the present paper and the authors define here generalized Humbert-Hermite polynomials of two variables. Several expansions of Humbert- Hermite polynomials, Hermite-Gegenbaurer (or ultraspherical) polynomials and Hermite- Chebyshev polynomials are proved.

For evaluating probabilities of arbitrary random events with respect to a given multivariate probability distribution, specific techniques are of great interest. An important two-dimensional high risk limit law is the Gauss-exponential distribution whose probabilities can be dealt with based upon the Gauss-Laplace law. The latter will be considered here as an element of the newly introduced family of (p,q)-spherical distributions. Based upon a suitably defined non-Euclidean arc-length measure on (p,q)-circles we prove geometric and stochastic representations of these distributions and correspondingly distributed random vectors, respectively. These representations allow dealing with the new probability measures similarly like with elliptically contoured distributions and more general homogeneous star-shaped ones. This is demonstrated at hand of a generalization of the Box-Muller simulation method. En passant, we prove an extension of the sector and circle number functions.

Reliable hydrological monitoring is crucial for effective water resource management, but establishing observatories in remote areas with extreme weather presents significant challenges. This study aimed at identifying cost-effective methods for streamflow estimation in such environments. We evaluated the performance of the Soil and Water Assessment Tool (SWAT) model using various precipitation data sources: ground-based rain gauge networks with different densities (one-gauge (1RG), two-gauge (2RG), and five-gauge (5RG) configurations) and satellite-derived Integrated Multi-satellite Retrievals for GPM (IMERG) data. The study focused on the Sahafihitry catchment, a 200 km² area in northeastern Madagascar. The results demonstrated that denser rain gauge networks (5RG and 2RG) captured the spatial variability of rainfall more effectively than a single gauge or IMERG data. This translated into superior SWAT model performance. Denser networks achieved higher statistical metrics (R², slope of regression a, Nash-Sutcliffe efficiency NSE, root mean square error RMSE, Kling-Gupta efficiency KGE) indicating a better fit between simulated and observed streamflow. Specifically, 5RG: R² = 0.84, a = 0.92, NSE = 0.83, RMSE = 3.33, KGE = 0.82; 2RG: R² = 0.85, a = 0.95, NSE = 0.83, RMSE = 3.36, KGE = 0.88; 1RG: R² = 0.73, a = 0.80, NSE = 0.72, RMSE = 4.28, KGE = 0.88; IMERG: R² = 0.48, a = 0.63, NSE = 0.37, RMSE = 6.46, KGE = 0.66. Furthermore, rain gauge data outperformed IMERG in simulating both flood events and low-flow periods. While IMERG offers low cost, readily available data, its lower performance introduces significant uncertainty into hydrological modeling. In contrast, the two-gauge network (2RG) achieved satisfactory streamflow simulations and represents the most cost-effective option for establishing reliable observatories within the specific characteristics of this study area.

In this paper, we introduce a new class of Laguerre-based Hermite-Fubini numbers and polynomials and investigate some properties of these polynomials. We establish summation formulas of these polynomials by summation techniques series. Furthermore, we derive symmetric identities of Laguerre-based Hermite-Fubini numbers and polynomials by using generating functions.

In this study, we investigated the features of co-infection with SARS-CoV-2 and nonpathogenic strain LEV8 of coxsackievirus A7 or enterovirus A71 for Vero E6 cells and Syrian hamsters. The investigation of co-infection with SARS-СoV-2 and LEV-8 or EV-A71 in the cell model was shown that a competitive inhibitory effect for these viruses were especially significant against SARS-CoV-2. Pre-infection with enteroviruses in animals caused more than a 100-fold decrease in the levels of SARS-CoV-2 virus replication in the respiratory tracts and more rapid clearance of the lower respiratory tract from infectious SARS-CoV-2. Co-infection with SARS-CoV-2 and LEV-8 or EV-A71 also reduced the severity of clinical manifestations of the SARS-CoV-2 infection for animals. Additionally, the histological data illustrated that co-infection with nonpathogenic coxsackievirus decreased the level of pathological changes by SARS-CoV-2 in the lungs. Research into the chemokine/cytokine profile demonstrated that the studied enteroviruses efficiently triggered this part of antiviral immune response, which can be associated with significant inhibition of SARS-CoV-2 infection. These results demonstrate that there exists a strong viral interference between the studied nonpathogenic strain of coxsackievirus A7 or enteroviruses A71 and SARS-CoV-2 pathogenic for humans in vitro and in vivo.

Winter rapeseed (Brassica rapa) has good chilling and freezing tolerance. INDUCER OF CBF EXPRESSION1 (ICE) plays a crucial role in cold signaling in plant; however, its role in B. rapa remains unclear. In this study, we identified 41 ICE1 homologous genes from 6 widely cultivated Brassica species. These genes exhibited high conservation, with evolutionary complexity between diploid and allotetraploid species. Cold stress induced ICE1 homolog expression, with differences between strong and weak cold-tolerant varieties. Two novel ICE1 paralogs, BrICE1 and BrICE2 were cloned from B. rapa Longyou 6. Subcellular localization assays showed that they localized to the nucleus, and low temperature did not affect their nuclear localization. Overexpression of BrICE1 and BrICE2 increased cold tolerance in transgenic Arabidopsis, and enhanced reactive oxygen species (ROS) scavenging ability. Furthermore, our data demonstrate that overexpression of BrICE1 and BrICE2 inhibited root growth in Arabidopsis, and low temperatures could induce the degradation of BrICE1 and BrICE2 via the 26S-proteasome pathway. In summary, ICE1 ho-mologous genes exhibit complex evolutionary relationships in Brassica species, and are involved in the CBF pathway, and ROS scavenging mechanism in response to cold stress; these regulating mechanisms might be also responsible for balancing the development and cold defense of B. rapa.

This research addresses the challenge of integrating fairness considerations into optimization problems, focusing on the development of multi-objective optimization techniques that balance traditional efficiency metrics with fairness constraints. In many algorithmic decision-making processes, optimizing for efficiency alone can inadvertently perpetuate or exacerbate biases, leading to inequitable outcomes. This study explores how data analysis and machine learning can be leveraged to identify and mitigate these biases, ensuring that optimization models yield both efficient and fair solutions. The research presents a framework for incorporating fairness constraints into multi-objective optimization, utilizing various fairness metrics such as demographic parity, equal opportunity, and disparate impact. It investigates the application of this framework across different domains, including resource allocation in healthcare systems, loan approvals in finance, and personalized learning platforms in education. By analyzing real-world data sets, the study demonstrates how fairness-aware optimization can lead to more equitable outcomes without significantly compromising efficiency. Empirical results from case studies show that multi-objective optimization with fairness constraints can effectively balance the trade-off between efficiency and equity. The research also discusses the computational challenges and ethical considerations associated with implementing fairness constraints in optimization models. Strategies for addressing these challenges, such as regularization techniques and fairness-aware machine learning algorithms, are evaluated and presented. Findings highlight the importance of incorporating fairness into optimization processes to prevent algorithmic biases and promote social equity. The study concludes with practical recommendations for policymakers, practitioners, and researchers on adopting fairness-aware optimization techniques to ensure that algorithmic decision-making processes are both efficient and just.

In the paper, a coordinated control methodology of single-phase (1-P) end-users switching oper-ations on the phases of an active electric distribution network (AEDN) has been proposed to ob-tain a minimum unbalance degree at the coupling common point (CCP) level with the main dis-tribution system. The phase load balancing (PLB) process considers the smart devices that switch from one phase to another phase the 1-P end-users (consumers and prosumers) to compensate for the phase load unbalance. The proposed methodology has been tested successfully in an AEDN belonging to a Romanian Distribution Network Operator (DNO) containing 114 end-users (104 consumers/10 prosumers) integrated into the Smart Metering System (SMS). The op-timal solution leads to a value of the objective function by 1.00004, represented by the unbalance factor, very close to the ideal target, 1.00. A comparative analysis was conducted considering other possible PLB cases (the consumer-level PLB and prosumer-level PLB), obtaining similar values of the UF (1.027 vs. 1.028), slightly higher than in the hybrid-level PLB. Also, the signifi-cant technical benefits were quantified through an energy-saving of 58.73% and decreasing the phase voltage unbalance rate by 91 % compared to the initial case (without PLB). These results emphasized the positive impact of the proposed coordinated control methodology on the PLB process and evidenced its effectiveness and applicability in the AEDNs.

In the electric distribution systems, the “Smart Grid” concept is implemented to encourage energy savings and integration of the innovative technologies, helping the Distribution Network Operators (DNOs) in choosing the investment plans which to lead the optimal operation of the networks and increasing the energy efficiency. In this context, a new phase load balancing algorithm was proposed to be implemented in the low voltage distribution networks with hybrid structures of the consumption points (switchable and non-switchable consumers). It can work in both operation modes (on-line and off-line), uploading information from different databases of the DNO which contain: the consumers’ characteristics, the real loads of the consumers integrated into the Smart Metering System (SMS), and the typical load profiles for the consumers non-integrated in the SMS. The algorithm was tested in a real network, having a hybrid structure of the consumption points, on a time interval by 24 hours. The obtained results were analyzed and compared with other algorithms from the heuristic (Minimum Count of Loads Adjustment algorithm) and the metaheuristic (Particle Swarm Optimization and Genetic Algorithms) categories. The best performances were provided by the proposed algorithm, such that the unbalance coefficient resulted in the smallest value (1.0017). The phase load balancing led to the following technical effects: decreasing the average current in the neutral conductor with 94% and for the energy losses with 61.75 %, and increasing the minimum value of the phase voltage at the farthest pillar with the 7.14 %, compared to the unbalanced case.

Grid energy storage system for PV Applications is connected with three different power sources i.e. PV Array, Battery and the Grid. It is advisable to have Isolation between these three different sources to provide safety for the equipment. The configuration proposed in this paper provides the complete isolation between the three sources. A Power Balancing Control (PBC) for this configuration is proposed to operate the system in three different modes of operation. Control of a dual active bridge (DAB) based battery charger which provides a galvanic isolation between batteries and other sources is explained briefly. Various modes of operation of a Grid energy storage system are also presented in this paper. Hardware-In-Loop (HIL) Simulation is carried out to check the performance of the system and the PBC algorithm. Power circuit (comprises of inverter, dual active bridge based battery charger, grid, PV cell, batteries, contactors and switches) is simulated and the controller hardware and user interface panel are connected as HIL with the simulated power circuit through Real Time Digital Simulator (RTDS). HIL simulation results are presented to explain the control operation, steady state performance in different modes of operation and the dynamic response of the system.

Foot pain related to the plantar fascia is common in adults regardless of athletic background. Many pathologies can lead to the diagnosis of plantar enthesopathy, but the most relevant to this case study is related to structural defects within the plantar fascia. Due to the variety and sometimes unclear etiology of plantar fasciosis, no single standard of care treatment protocol is recognized. Often, patients receive NSAIDs, night splints, taping, physical therapy, foot orthosis, and extracorporeal shock wave therapies. This study presents Wharton Jelly (WJ) tissue allografts as an additional intervention for patients who fail standard-care treatments. WJ allografts are applied to supplement connective tissue defects directly, unlike other therapies, which aim to reduce swelling and symptomatic pain. This retrospective study includes seven patients who have previously failed standard-of-care treatment. Each received extracorporeal pulse-activated therapy (EPAT), WJ, laser therapy, and an orthotic. Patient progress was tracked using a visual analog scale, 0-10 scoring, reported by the patient at the initial visit and approximately thirteen weeks following, reporting an average improvement of 50%. The study's limitations include a small cohort size and a non-blinded retrospective design. These promising results provide evidence for a more extensive, randomized study to define dosage protocols further and confirm safety and efficacy.

Investigation into a modified deterministic rumour spread model using mathematical epidemiology approach as an extension of Daley and Kendall (D-K) model was carried out. It is proven that the spread of a rumour is fairly comparable to a contagious disease transmission, except that the spread depends solely on the fraction of individuals who are aware of the rumour at the early stage as well as the intrinsic rumour spread rate . Model simulation reveals a rumour diminishing strategy so far as is maintained.

Recently several papers have been published on the Liénard equation x'' +L x+m x^3+n x^5=0, where the authors studied their explicit solutions and their applications. Here we describe the complete dynamics of these differential equations in the Poincar\'e disc. The Poincar\'e disc is the closed disc centered at the origin of coordinates of radius one, the whole plane R^2 is identified with the interior of this disc, and its boundary, the circle $\sss^1$ is identified with the infinity of R^2.

We propose Reduced Collatz Conjecture that is equivalent to Collatz Conjecture, which states that every positive integer can return to an integer less than it, instead of 1. Reduced Collatz Conjecture should be easier because some properties are presented in reduced dynamics, rather than in original dynamics (e.g., ratio and period). Reduced dynamics is a computation sequence from starting integer to the first integer less than it, and original dynamics is a computation sequence from starting integer to 1. Reduced dynamics is a component of original dynamics. We denote dynamics of x as a sequence of either computations in terms of ``I'' that represents (3*x+1)/2 and ``O'' that represents x/2. Here 3*x+1 and x/2 are combined together, because 3*x+1 is always even and followed by x/2. We formally prove that all positive integers are partitioned into two halves and either presents ``I'' or ``O'' in next ongoing computation. More specifically, (1) if any positive integer x that is i module $2^t$ (i is an odd integer) is given, then the first t computations (each one is either ``I'' or ``O'' corresponding to whether current integer is odd or even) will be identical with that of i. (2) If current integer after t computations (in terms of ``I'' or ``O'') is less than x, then reduced dynamics of x is available. Otherwise, the residue class of x (namely, i module $2^t$) can be partitioned into two halves (namely, i module $2^{t+1}$ and $i+2^t$ module $2^{t+1}$), and either half presents ``I'' or ``O'' in intermediately forthcoming (t+1)-th computation.

Inducer of C-Repeat Binding Factor (CBF) expression (ICE), a well-known MYC-type bHLH transcription factor, plays a crucial role in the ICE-CBF-cold regulated (COR) cold signaling pathway. Brassica rapa (B. rapa), an important oilseed and vegetable crop, exhibits strong chilling and freezing tolerance. However, the exact molecular mechanisms by which ICE1 functions in B. rapa remain unclear. Here, 41 ICE1 homologous genes were identified from six widely cultivated Brassica species using a bioinformatics approach. These genes exhibit high conservation but reveal evolutionary complexity between diploid and allotetraploid species. Low-temperature stress induced ICE1 homolog expression, with patterns differing between strong and weak cold-tolerant varieties. Two novel ICE1 paralogs, BrICE1 and BrICE2, were cloned from Longyou 6. They positively regulate cold tolerance in B. rapa via a CBF-dependent pathway, contributing to reactive oxygen species (ROS) scavenfging and osmotic adjustment. Immunoblot analysis revealed that low temperatures induce BrICE1 and BrICE2 degradation via the 26S-proteasome pathway. In summary, ICE1 exhibits complex evolutionary relationships in Brassica species. BrICE1 and BrICE2 positively regulate cold tolerance via the CBF-dependent pathway and ROS scavenging mechanism and are also responsible for balancing the development and cold defense of B. rapa.

Class Incremental Learning (CIL) has emerged as a vital machine learning paradigm, particularly suited for environments with continuous data streams, such as those encountered in the financial industry. CIL enables models to progressively acquire and accumulate new knowledge without retraining the entire dataset, making it highly efficient for dynamic financial markets. In this paper, we explore the application of CIL to financial data streams, where high-frequency, high-dimensional data, such as stock prices and cryptocurrency transactions, evolve rapidly. The challenges inherent in CIL, including catastrophic forgetting—where the model loses previously learned knowledge—and intransigence—where it resists learning new classes—are addressed through both regularization-based techniques and dynamic architecture expansion. We present a comprehensive analysis of the algorithms designed to mitigate these challenges and highlight their relevance in financial applications like algorithmic trading, portfolio management, risk assessment, and fraud detection. By demonstrating how CIL enables models to adapt to new market conditions and asset classes while retaining past knowledge, we illustrate its critical role in advancing real-time financial decision-making. Through a detailed examination of the methodologies and their applications, this paper aims to provide insights into the future of adaptive financial models using Class Incremental Learning. We have also summarized methodologies into a model optimization-based class and a data focused class.

Mature B cells notably diversify immunoglobulin (Ig) production through class switch recombination (CSR), allowing the junction of distant “switch” (S) regions. CSR is initiated by activation-induced deaminase (AID) which targets cytosines adequately exposed within single-stranded DNA of transcribed targeted S regions, with a specific affinity for WRCY motifs. In mammals, G-rich sequences are additionally present in S regions, forming canonical G-quadruplexes (G4s) DNA structures which favor CSR. Small molecules interacting with G4-DNA (G4 ligands), proved able to regulate CSR in B lymphocytes, either positively (such as for nucleoside diphosphate kinase isoforms) or negatively (such as for RHPS4). G4-DNA is also implicated in the control of transcription, and due to their impact on both CSR and transcriptional regulation, G4-rich sequences likely play a role in the natural history of B cell malignancies. Since G4-DNA stands at multiple locations in the genome, and notably within oncogene promoters, it remains to be clarified how it can more specifically promote legitimate CSR in physiology, rather than pathogenic translocation. The specific regulatory role of G4 structures in transcribed DNA and/or in corresponding transcripts and recombination hereby appears as a major issue for understanding immune responses and lymphomagenesis.

In this paper we study solutions and drift homogenization for a class of viscous lake equations by using the method of semigroups of bounded operators. Suppose that the initial value i.e.,for some Hölder continuous function onwith smooth function value satisfying and Then the initial value problem (2) for viscous lake equations has a unique smooth local strong solution. Using this result we study the drift homogenization for three-dimensional stationary Stokes equation in the usual sense

The ultimate aim of dynamic load balancing in cloud computing systems is to maximise the efficiency with which resources are utilised and workloads are distributed. Given that load balancing is a multi-objective process and that response time is a priority, the Harris hawk optimisation (HHO) algorithm was developed as a unique solution for dynamic load balancing. Based on burden distribution and resource utilisation, the HHO algorithm is responsible for dynamically assigning workloads to virtual machines (VMs). Through iterative interactions and position updates, the hawks investigate the solution space, determine the optimal method for dividing the work, and adapt to the ever-changing conditions of the workload. The HHO algorithm has been demonstrated to be effective and efficient in the management of dynamic load balancing via a series of experimental evaluations and comparisons with other load-balancing approaches. These discoveries have led to quicker response times and more efficient resource utilisation. Utilising the collaborative search behaviour of hawks, this technique provides a solution that is both practicable and effective for addressing load balancing concerns in dynamic scenarios.

Classification predictive modeling involves the accurate assignment of observations in a dataset to target classes or categories. There is an increasing growth of real-world classification problems with severely imbalanced class distributions. In this case, minority classes have much fewer observations to learn from than those from majority classes. Despite this sparsity, a minority class is often considered the more interesting class yet developing a scientific learning algorithm suitable for the observations presents countless challenges. In this article, we suggest a novel multi-class classification algorithm specialized to handle severely imbalanced classes based on the method we refer to as SAMME.C2. It blends the flexible mechanics of the boosting techniques from SAMME algorithm, a multi-class classifier, and Ada.C2 algorithm, a cost-sensitive binary classifier designed to address highly class imbalances. Not only do we provide the resulting algorithm but we also establish scientific and statistical formulation of our proposed SAMME.C2 algorithm. Through numerical experiments examining various degrees of classifier difficulty, we demonstrate consistent superior performance of our proposed model.

Online courses in horticulture increase the breadth of students who may be able to enroll. However, it is challenging to create hands-on learning experiences in online classes that are valuable for student learning. In an online introduction to horticulture class at the University of Maine, we created a hands-on project that is appropriate for students to work on independently at home. Students built an environmental monitoring system using a relatively inexpensive Raspberry Pi microcomputer and sensors for monitoring environmental factors that impact plant growth with a particular focus on monitoring temperature and humidity. They monitored the growing environment in their homes while growing house plants and used the information from their environmental monitoring system to determine whether their home environment was suitable for growing plants. Students were asked to use a pre-existing computer program in the Python language to monitor the environment. They also learned about how components of the code function and changed some simple parts of the code. A majority of students working on this project felt moderately confident, somewhat more confident, or very confident about their ability to use a Raspberry Pi microcomputer in the future. This project provides students with valuable hands-on experience in building environmental monitoring systems and provides them with a deeper understanding of the impact of the environment on plant growth.

The present paper introduces a new class of Stancu-Kantorovich operators constructed in the King sense. For this class of operators we establish some convergence results, error estimations theorems and graphical properties of approximation for the cases considered, namely, operators that preserve the test functions $e_{0}(x)=1$ and $e_{1}(x)=x$, $% e_{0}(x)=1$ and $e_{2}(x)=x^{2},$ as well as $e_{1}(x)=x$ and $e_{2}(x)=x^{2} $. The new class of operators introduced in this paper is a genuine generalization of the class introduced by Indrea et al. (see Indrea A., Indrea A., Pop O. A New Class of Kantorovich-Type Operators, Constructive Mathematical Analysis).

G-protein coupled receptors (GPCRs) are membrane proteins that convey extracellular signals to the cellular milieu. They represent a target for more than 30 % of currently marketed drugs. Here we review the effects of membrane cholesterol on the function of GPCRs of Class A. We review both the specific effects of cholesterol mediated via its direct high-affinity binding to the receptor and non-specific effects mediated by cholesterol-induced changes in the properties of the membrane. Cholesterol binds to many GPCRs at both canonical and non-canonical binding sites. It allosterically affects ligand binding to and activation of GPCRs. Also, it changes the oligomerization state of GPCRs. In this review, we consider a perspective of the potential for the development of new therapies that are targeted at manipulating the level of membrane cholesterol or modulating cholesterol binding sites on to GPCRs.

We give an operator solution to an advanced-retarded differential equation. The application of the operators involved produces a solution in terms of Bessel functions.

In this article, authors introduce a new class of degenerate Hermite poly-Genocchi polynomials and give some identities of these polynomials related to the Stirling numbers of the second kind. Some implicit summation formulae and general symmetry identities are derived by using different analytical means and applying generating functions. These results extend some known summations and identities of degenerate Hermite poly-Bernoulli numbers and polynomials studied by Khan [8].

The Chebyshev collocation method (ChC) implemented as Chebfun is used in order to solve a class of second order one-dimensional singular and genuinely nonlinear boundary value problems. Efforts to solve these problems with conventional ChC have generally failed, and the outcomes obtained by finite differences or finite elements are seldom satisfactory. We try to fix this situation using the new Chebfun programming environment. However, for toughest problems we have to loosen the default Chebfun tolerance in Newton's solver as the ChC runs into trouble with ill-conditioning of the spectral differentiation matrices. Although in such cases the convergence is not quadratic the Newton updates decrease monotonically. This fact, along with the decreasing behaviour of Chebyshev coefficients of solutions, suggests that the outcomes are trustworthy, i.e., the collocation method has exponential (geometric) rate of convergence or at least an algebraic rate. We consider first a set of problems that have exact solutions or prime integrals and then another set of benchmark problems that do not possess these properties. Actually, for each test problem carried out we have determined how Chebfun solution converges, its length, the accuracy of the Newton method and especially how well the numerical results overlap with the analytical ones (existence and uniqueness).

In the last years, the Distribution Grid Operators (DGOs) assumed transition strategies of the distribution grids towards an active area associated with the "Smart Grids" concept. They are considering the use of Artificial Intelligence techniques, combined with advanced technologies and real-time remote communication solutions of the enormous data amounts, to develop smart solutions into the small size distribution grids, also called microgrids (μGs). These solutions will provide support for the DGOs to ensure an optimal operation of the technical infrastructure of the μGs. In this context, a bi-level methodology for solving the phase load balancing problem in the μGs with complex topologies and a high number of single-phase consumers, considering a clustering-based selection criterion of the consumers for placement of the switching devices, was proposed in the paper. A real μG from a rural area, with 114 consumers integrated into the Smart Metering System (SMS), belonging to the DGO from Romania, was considered in testing the proposed methodology. An implementation degree of 17.5%, corresponding to the phase load balancing equipment installed to only 20 consumers from the μG, led to a faster computational time with 43% and reducing the number of switching operations by 92% than in the case of a full implementation degree (100%). The performance indicators related to the unbalance factor and energy-saving used in the evaluation of the technical benefits highlighted the efficiency of the proposed methodology.

The article discusses the validation process of a certain method of balancing gas contained in the pore space of rocks. The validation was based upon juxtaposition of the examination of rocks' porosity and the effects of comminution in terms of assessing the possibility of opening the pore space. The tests were carried out for six dolomite samples taken from different areas of the 'Polkowice-Sieroszowice’ copper mine in Poland. Before the rock material was ground, it was examined using the porosimetric method and subsequently subjected to the SEM analysis. After grinding, the research material was subjected to the granulometric analysis. Prior to the grinding process, the rocks' porosity fell in the range of 0.3-14.8%, while the volume of the open pores was included in the 0.01-0.06 cm3/g range. The research material was also characterized by inhomogeneous distribution of the pore volume. The grinding process was performed using an original device – the GPR analyzer. The ground samples were characterized by similar particle size distribution and mean diameters D_3.2 of about 4.0-4.5 µm. The SEM analysis revealed pores of various size and shape on the surface of the rock cores, while at the same time demonstrating lack of pores following the grinding process. The grain size distribution curves were compared with the cumulative pore volume curves of the cores before grinding. The resulting intersection points of both curves were seen as testifying to the presence of openings in the rock pores and release of the accumulated gas. The opening percentage of the pore space was associated with the coordinates of the curves' intersections. In order to confirm the argument put forward in this paper – i.e. that comminution of a rock to grains of a size comparable with the size of the rock's pores results in the release of gas contained in the pore space – the amount of gas released as a result of the comminution process was studied. The results of gas balancing demonstrated that the pore space of the investigated dolomites was filled with gas in amounts from 3.19 cm3/kg to 45.86 cm3/kg. The obtained results of the rock material comminution to grains comparable – in terms of size – to the size of the pores of investigated rocks, along with asserting the presence of gas in the pore space of the studied dolomites, were regarded as a proof that the method of balancing gas in rocks via rock comminution is correct.

Background: The major histocompatibility complex (MHC) plays a key role in the adaptive immune response to pathogens due to its extraordinary polymorphism. However, the spatial patterns of MHC variation in the striped hamster remain unclear, particularly regarding the relative contribution of balancing selection in shaping MHC population and diversity compared to neutral forces. Methods: In this study, we investigated the immunogenic variation of the striped hamster in four wild populations in Inner Mongolia, which experience heterogeneous parasitic stress. Our goal was to identify local adaptation by comparing the genetic structure at MHC with that at seven microsatellite loci, taking into account neutral processes. Results: We observed significant variation in parasite pressure among sites, with parasite burden showing a correlation with temperature and precipitation. Molecular analysis revealed a similar co-structure between MHC and microsatellite loci. We observed lower genetic differentiation at MHC loci compared to microsatellite loci, and no correlation was found between the two. Conclusions: Overall, these results suggest a complex interplay between neutral evolutionary forces and balancing selection in shaping the spatial patterns of MHC variation. Local adaptation was not detected at a small scale but may be applicable at a larger scale.

Literature circles create an interactive and participatory learning atmosphere that greatly increases student participation in the classroom. Through literature circles, students can exchange ideas and gain knowledge from one another in a nurturing learning atmosphere. Students feel more at ease taking chances and voicing their opinions in the classroom when there is a strong sense of community among the students. This research study used a narrative literature review as a qualitative method. Relevant literature in this review showed that literature circles can increase students' reading passion and enthusiasm. Students are more likely to be engaged when they can select their own reading materials and facilitate discussions. Thus, by implementing literature circles, teachers may create a more engaging and encouraging learning environment in the classroom, which will ultimately improve student performance and foster a deeper understanding of literature.

Ransomware attacks have risen alarmingly, with encryption techniques becoming more complex. This paper introduces a novel detection model tailored for ransomware's distinctive characteristics. The Intel PIN tool extracts Windows API invocation sequences related to file operations. These sequences are used to construct n-grams, forming feature vectors enhanced by a new Class Feature Weighting (CFW) metric to improve malware detection. Preliminary results demonstrate elevated accuracy and precision versus existing methods. The major contributions are: (1) Introducing an innovative deep learning model for few-shot ransomware classification using entropy features and transfer learning. (2) Achieving high weighted F1-score in classifying ransomware variants into families with limited training data. (3) Demonstrating the potential of entropy-based features to capture intricacies lost in image-based approaches, improving detection of new strains.

With the continuous development of network technology, complex network systems generate massive unbalanced attack traffic. Due to the severe imbalance in the quantities of normal samples and attack samples, as well as among different types of attack samples, intrusion detection systems suffer from low detection rates for rare class attack data. In this paper, we propose a geometric synthetic minority oversampling technique based on optimized kernel density estimation algorithm. This method can generate diverse rare class attack data by learning the distribution of rare class attack data while maintaining similarity with the original sample features. Meanwhile, the balanced data is input to a feature extraction module built upon multiple denoising autoencoders, reducing information redundancy in high-dimensional data and improving the detection performance for unknown attacks. Subsequently, a soft voting ensemble learning technique is utilized for multi-class anomaly detection on the balanced and dimensionally reduced data. Finally, an intrusion detection system is constructed based on data preprocessing, imbalance handling, feature extraction, and anomaly detection modules, and validated on the NSL-KDD and N-BaIoT datasets. Comparative experiments with baseline models and other state-of-the-art methods demonstrate that the proposed system improves the detection rate of rare class attack data. Furthermore, it achieves a good overall detection rate on the Internet of Things dataset (N-BaIoT), indicating its strong applicability.

Tolman VII solution \citep{ref-journal 2} is an exact analytic solution to the Einstein field equations describing the space-time of a static spherically symmetric distribution of matter. The solution has been shown to be capable of describing the interior of compact objects like neutron stars. Generalized \cite{ref-journal 13} and modified \cite{ref-journal 10} versions of the solution are also available in the literature, which have been subsequently developed to accommodate more realistic descriptions of neutron stars. The stability of the modified Tolman VII solution has recently been analyzed by Posada {\em et al} \cite{ref-journal 9 }, who evaluated a critical value of the adiabatic index above which the stellar configuration becomes unstable against radial oscillations. In this paper, making use of the generalized version of the Tolman VII solution, we prescribe an upper bound on the compactness ($M/R$) beyond which the star becomes unstable. Our investigation is based on the stability analysis of a star against radial oscillations developed by Chandrasekhar \cite{ref-journal 1}. The analysis brings out to attention the role of a particular model parameter in the generalized Tolman VII solution which can be linked to the inhomogeneity of the matter distribution vis-a-vis equation of state (EOS).

Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware is gaining increased attention due to its ability to expose plaintext passwords or key encryption files. This paper presents an enhanced classification model based on One class SVM (OCSVM) classifier that can identify any deviation from the normal memory dump file patterns and detect it as malware. The proposed model integrates OCSVM and Principal Component Analysis (PCA) for increased model sensitivity and efficiency. An up-to-date dataset known as “MALMEMANALYSIS-2022” was utilized during the evaluation phase of this study. The accuracy achieved by the traditional one-class classification (TOCC) model was 55%, compared to 99.4% in the one-class classification with PCA (OCC-PCA) model. Such results have confirmed the increased performance achieved by the proposed model.

On the basis of a limited number of reasonable axioms, we discuss the classification of all the possible universality classes of diffeomorphisms invariant metric theories of quantum gravity. We use the language of the renormalization group and adopt several ideas which originate in the context of statistical mechanics and quantum field theory. Our discussion leads to several ideas that could affect the status of the asymptotic safety conjecture of quantum gravity and give universal arguments towards its proof.

The growing number of the accessed energy-efficient frequency conversion air conditioners is likely to generate a large number of harmonics on the power grid. The following shortage in the reactive power of peak load may trigger voltage collapse. Hence, this conflicts with people’s expectations for a cozy environment. Concerning the problems mentioned above, an active management scheme is put forward to balance the electricity use and the normal operation of air conditioning systems. To be specific, schemes to suppress the low voltage ride through (LVRT) and harmonic are designed firstly. Then to deaden the adverse effects caused by nonlinear group load running on the grid, and to prevent the unexpected accidents engendered from grid malfunction, the dynamic sensing information obtained by an online monitor is analyzed, which can be seen as an actively supervise mechanism. The combined application of active and passive filtering technology is studied as well. Thirdly, the new energy storage is accessed reliably to cope with peak-cutting or grid breaking emergencies, and the fuzzy control algorithm is researched. Finally, system feasibility is verified by functional modules co-operation simulation, and active management target is achieved under scientific and reasonable state-of-charge (SOC) management strategy.