This work aims to investigate the effect of process temperature and catalyst content by thermochemical degradation of municipal solid waste (MSW) fraction (organic matter + paper + plastic) on the yield of reaction products (bio-oil, biochar, H2O and gas), physi-cochemical properties and chemical composition of bio-oils, as well as on the morphol-ogy and crystalline phases of biochar in laboratory scale. The organic matter, paper and plastic segregated from the gravimetric composition of total waste sample were subject-ed to the pre-treatments of drying, crushing and sieving. Thermal pyrolysis was per-formed at 400, 450 and 475 °C and 1.0 atmosphere, and catalytic pyrolysis at 450°C and 1.0 atmosphere, using 5.0, 10.0 and 15.0% (wt.) of FCC zeolite, bath mode, using a laboratory-scale borosilicate glass reactor. The bio-oil was characterized for acidic val-ue. The chemical functions present in the bio-oil identified by FT-IR and the composi-tion by GC-MS. Biochar was characterized by SEM/EDS and XRD. Thermal pyrolysis of the MSW fraction shows bio-oil yields between 9.44 and 9.24% (wt), aqueous phase yields between 21.93 and 18.78% (wt), solid phase yields between 67.97 and 40.34% (wt) and gas yields between 28.27 and 5.92% (wt). The bio-oil yield decreased with in-creasing process temperature. For the experiments using FCC, the biochar and gas yields slightly increased with increasing FCC content, while that of bio-oil decreased and the H2O phase remained constant. GC-MS of bio-oils identified the presence of hy-drocarbons and oxygenates, as well as nitrogen-containing compounds including am-ides and amines. The acidity of the bio-oil increased with increasing temperature and with the aid of FCC as a catalyst. The presence of hydrocarbons in bio-oil was identified by the addition of FCC catalyst due to the deoxygenation of carboxylic acids, followed by decarboxylation and decarbonylation reactions, producing aliphatic and aromatic hydrocarbons.

Sinonasal carcinomas are aggressive neoplasms that present a high morbidity and mortality rate with unfavorable prognosis. This group of tumors exhibits morphological and genetic diversity. Genetic and epigenetic alterations in these neoplasms are currently targets for diagnosis and treatment. The most common type of cancer originated in the sinonasal tract are sinonasal squa-mous cell carcinomas (SNSCC), which presents different histological patterns and variable his-tological aggressiveness. A significant number of alterations have been reported in sinonasal tu-mors, including deficiencies in the SwItch/Sucrose non-fermentable (SWI/SNF) chromatin remod-eling complex. In the sinonasal tract, deficiencies of the subunits SMARCB1/INI1, SMAR-CA4/BRG1 and SMARCA2 have been noted in carcinomas, adenocarcinomas, and soft tissue tu-mors with distinctive high-grade morphology and fatal prognosis. Objective: To identify the status of the SWI/SNF complex using immunohistochemistry in sinonasal squamous cell carci-nomas and its association with morphology and survival. Methods: A total of 1582 SNSCC cases were analyzed, selecting those with high-grade malignancy morphology, which were then evalu-ated immunohistochemically for SMARCB1 and SMARCA4 proteins. Their expression was compared with the biological behavior and survival of the patients. Results: Among the SNSCC, 47% corresponded to the non-keratinizing squamous cell carcinoma (NKSCC) type with high-grade characteristics, 40% were keratinizing squamous cell carcinomas (KSCC), 9% were SMARCB1-deficient carcinomas, and 4% were SMARCA4-deficient carcinomas. Mosaic expres-sion for SMARCB1 (NKSCC 33%, KSCC 21.9%) and SMARCA4 (NKSCC 14.6%, KSCC 12.2%) was identified, showing an impact on tumor size and progression. Conclusions: We identified that the mosaic expression of INI1 in NKSCC exhibited a worse prognosis compared to intact carcinomas and was similar to SMARCB1-deficent carcinoma.

Background: In recent years, the integration of robotic systems into various aspects of daily life has become increasingly common. As these technologies continue to advance, ensuring user-friendly interfaces and seamless interactions becomes more essential. For social robots to genuinely provide lasting value to humans, a favourable user experience (UX) emerges as an essential prerequisite. Aim: This article aimed to evaluate the usability of the MINI robot, highlighting its strengths and areas for improvement based on user feedback and performance. Method: In a controlled lab setting, a mixed-method qualitative study was conducted with ten individuals aged 65 and above diagnosed with mild dementia (MD) and mild cognitive impairment (MCI). Participants engaged in individual MINI robot interaction sessions, completing cognitive tasks as per written instructions. Video and audio recordings documented interactions, while post-session System Usability Scale (SUS) questionnaires quantified usability perception. Ethical guidelines were followed, ensuring informed consent, and the data underwent qualitative and quantitative analyses, contributing insights into the MINI robot's usability for this demographic. Results: The study addresses the ongoing challenges that tasks present, especially for MD individuals, emphasizing the importance of user support. Most tasks require both verbal and physical interactions, indicating that MD individuals face challenges when switching response methods within subtasks. These complexities originate from the selection and use of response methods, including difficulties with voice recognition, tablet touch, and tactile sensors. These challenges persist across tasks, with individuals with MD struggling to comprehend task instructions and provide correct answers and individuals with MCI struggling to use response devices, often due to the limitations of the robot's speech recognition. Technical shortcomings have been identified. The results of the System Usability Scale (SUS) indicate positive perceptions, although there are lower ratings for instructor assistance and pre-use learning. The average SUS score of 68.3 places device usability in the "good" category. Conclusion: Our study examines the usability of the MINI robot, revealing strengths in quick learning, simple system and operation, and integration of features while also highlighting areas for improvement. Careful design and modifications are essential for meaningful engagement with people with dementia. The robot could better benefit people with MD and MCI if clear, detailed instructions and instructor assistance are available.

Due to the rapid development of unmanned aerial vehicles (UAVs) communication technology, UAVs are gradually competing with primary users (PUs) for spectrum resources. Cognitive radio (CR) is a promising solution to meet the needs. Cooperative spectrum sensing (CSS) is considered as an effective method to detect the PU signal and identify available spectrum resources for UAVs in a cognitive UAV network (CUAVN). However, the cooperative mode among multiple UAVs may incur a high overhead, resulting in performance degradation. Therefore, we introduce a differential sequential 1 (DS1), which incorporates a differential mechanism and leverages the sequential idea based on the classical voting rule to enhance cooperative performance and efficiency. In view of this, we formulate three scenarios to characterize the PU activity and introduce a multi-slot cooperative mode within a single UAV to realize cooperative gain. Further, only the information change about the PU status is sequentially calculated in DS1, and combined with a sequential idea, the efficiency of the voting rule is greatly improved. Moreover, the application of support vector machines (SVM) in dynamic selection enables the selection of the most suitable voting rule based on diverse sensing parameters. This dynamic selection process ensures optimal performance and efficiency by adapting the voting rule to the specific characteristics of the given scenario. Finally, simulation results demonstrate that the superiority of our proposal with respect to the detection performance, sample size and the energy efficiency is evident, which proves the high performance of the proposed policy.

Black soldier fly larvae (BSFL) can use food by-products or residues as a growth substrate, and their use is considered a strategic solution to improve the sustainability and efficiency of animal production. BSFL need to be reared under controlled environmental conditions, as temperature can affect the welfare of the larvae and the efficiency of their bioconversion. The goal of this experiment was to test different diets: control, vegetable, carnivorous, omnivorous and to assess their effect on larval growth performance, chemical composition, and substrate temperature. BSFL (6-days-old) were randomly selected and allocated to one of the four diets for the entire experiment (8 days). The temperature in each container was measured twice daily using a thermal imaging camera. The vegetable diet led to significantly lower growth performance (p

Thin people who undergo implant insertion into the chest wall face a high risk of implant exposure to the external environment. Five months after an 8-year-old boy underwent implantable cardioverter-defibrillator (ICD) implantation in a subcutaneous pocket in the left anterolateral chest wall to manage long QT syndrome, ICD replacement became necessary owing to exposure risk from distal and lateral thinning of the ICD pocket. Pocket rupture and exposure would increase the risk of infection; therefore, we performed ICD removal and primary pocket closure. Two weeks later, a new suprafascial pocket was created, acellular dermal matrix (ADM) was attached to the inner wall to prevent ICD protrusion, and a new ICD was inserted. One year postoperatively, the ADM was engrafted, and no complications were observed. A thin subcutaneous layer increases the risk of ICD implantation complications. Inner wall strengthening with ADM can help prevent pocket rupture.

This study investigates the compressive strength and workability of concrete incorporating coconut shells and bamboo strips as alternative aggregates. The research compares traditional concrete to concrete mixtures with varying percentages of coconut shells and bamboo strips. Specifically, the study examines the effects of adding 10% coconut shell, 10% bamboo strip, and a combination of 5% coconut shell and 5% bamboo strip by volume to the concrete mix, tested in both C20 and C30 grades. To prepare the materials, coconut shells and bamboo strips were first crushed and then sieved. The sieving process retained particles that passed through a 10mm sieve but were retained on a 4.7mm sieve. The primary focus of the experimental tests was on measuring the compressive strength of the resulting concrete mixtures. The findings indicate that concrete incorporating coconut shells showed moderate workability. In contrast, concrete with bamboo strips exhibited lower workability. Regarding compressive strength, the concrete mix containing a combination of 5% coconut shell and 5% bamboo strips demonstrated a reduction in strength compared to the control group of normal concrete. This research highlights the potential of using coconut shells and bamboo strips as sustainable alternatives in concrete construction. While the workability varies between the different materials, the results provide valuable insights into their suitability for specific applications. This encourages further exploration of eco-conscious building practices and the development of sustainable construction materials.

The glucocorticoid receptor alpha (GRα), a pivotal component of the ancient glucocorticoid (GC) signaling system that emerged nearly 450 million years ago, has been instrumental in directing vertebrate evolution and is essential for survival, regulating fertility and fetal development, the function of every organ and circulating cells, metabolism, immune responses, and adaptation to stress. While its anti-inflammatory properties have been recognized since the mid-20th century, GRα’s broader role as the master regulator of homeostatic corrections, particularly in health and critical illness, has only recently been fully appreciated.GRα orchestrates three key phases of homeostatic correction: the Priming Phase, where it rapidly activates early immune responses, mobilizes energy reserves, and prepares the body to mount an immediate defense against various physiological stressors, including infection and injury. During this phase, GRα enhances glucose metabolism, supports mitochondrial function, and facilitates the release of key immune cells to the site of damage, ensuring the body is equipped to handle the initial assault. The Modulatory Phase follows, in which GRα fine-tunes the inflammatory response to prevent excessive tissue damage. By balancing pro- and anti-inflammatory signals, GRα helps contain the immune response, preventing the harmful effects of prolonged inflammation while maintaining immune surveillance. This phase also involves the regulation of oxidative stress, vascular tone, and the preservation of cellular integrity to protect vital organs from injury. Finally, in the Restorative Phase, GRα plays a critical role in resolving inflammation and initiating tissue repair. During this phase, GRα supports cellular regeneration, promotes the clearance of apoptotic cells and debris, and restores anatomical and physiological balance. It aids in rebuilding tissue architecture and returning the neuroendocrine-immune system to its baseline state, ensuring long-term recovery and stability.Throughout these phases, GRα manages complex interactions, including recently discovered co-regulation with pro-inflammatory transcription factors, while also regulating mitochondrial function to ensure bioenergetic and metabolic stability during stress. In critically ill patients, depletion of bioenergetic and micronutrient reserves severely impairs GRα’s capacity to regulate immune responses, heightening the risk of morbidity and mortality.This review, in light of this new understanding, underscores the need to re-evaluate current glucocorticoid (GC) treatment strategies, emphasizing the incorporation of micronutrient support to optimize GRα function across all phases of homeostatic correction. This integrated therapeutic approach has the potential to enhance both immediate immune responses and long-term recovery in critically ill patients, offering a more comprehensive strategy for managing the complex pathobiology of critical illness. Furthermore, this integrative approach could decrease glucocorticoid dose and duration requirements and potential side effects.

The sustainable development of public open spaces (POS) in suburban areas is crucial to enhancing neighborhood livability, especially in the face of uncontrolled urban sprawl. Therefore, the question was asked: what forms of gathering and recreation places are in suburbs that are subjected to mature urban planning? The research focuses on the Netherlands (Bunnik commune), a country known for its sustainable suburban areas. By conducting field research that involved analyzing physical traces and engaging in non-participatory observation, the study identified and categorized 18 distinct spatial-functional units of POS looking from a top-down design and planning lens. The types of gathering and recreation places identified through top-down development included: estates with recreational areas for neighbors; communal playgrounds; greenery sites by pedestrian routes; social places in central zone; open sports grounds; public allotment gardens; multi-purpose recreational areas at schools, communal facilities generating informal meetings. POS types created as a result of both top-down and bottom-up development were: entrance zones to public facilities with a social value; viewpoints accompanying pedestrian routes; network of green walks for recreation in rural and natural landscapes. Other POS types (resulting from bottom-up development): diner's garden and urban farm.

The rapid growth of the global population is causing a significant increase in waste production, leading to serious environmental and public health challenges. To address these issues, waste management systems are incorporating advanced technologies. Machine learning and computer vision are used to predict waste patterns, optimize collection schedules, and improve sorting accuracy. Deep learning automates the sorting process, provides predictive analytics, and enhances recycling rates. Robotics, combined with AI and computer vision, improves sorting efficiency, while the Internet of Things (IoT) monitors waste levels and optimizes collection routes. Despite these benefits, challenges such as data scarcity, high computational demands, and the need for substantial infrastructure investments must be addressed. This research explores the integration of advanced technologies into waste management and evaluates their effectiveness using waste datasets. It highlights the potential to tackle environmental challenges and lay the groundwork for more intelligent waste management solutions.

To accurately determine the refractive error of the eye, sufficient relaxation of eye accommodation is essential, either through the fogging technique or pharmacological cycloplegia. This study aimed to evaluate the clinical information revealed after cycloplegia and assess how age and non-cycloplegic refractive status influence the classification of eye refractive error. We analyzed the records of 472 non-population-based subjects aged 3–28 years (mean ± SD: 9.1 ± 4.6) who attended a private ophthalmology practice from January 2018 to December 2020. Cycloplegia was induced with one drop of cyclopentolate 1% in each eye, and refraction was measured 30 ± 5 min later using an objective autorefractometer. The average spherical equivalent (SE) difference (mean ± SD) induced by cycloplegia was 0.72 ± 0.73 D, with the highest difference observed in the 3-5 years age group (1.18 ± 0.85 D). Gender had no significant effect on the SE values before or after cycloplegia (p > 0.05). Cycloplegia caused a hyperopic refractive shift in 83% of participants, no shift in 12%, and a myopic refractive shift in 6%. This study highlights the importance of cycloplegia use in children and young adults.

A method for estimating river water surface elevation (WSE) from Landsat imagery using the river inundation area - water surface elevation (RIA-WSE) rating curve constructed from the U.S. Geological Survey Topobathymetric Elevation Model (TEM) data was developed and tested at six gauging stations along the upper Mississippi River. The Otsu's automatic threshold selection algorithm was employed for the image classification and estimation of inundation areas within each pre-defined polygon around each gauging station. In addition to the commonly used green-band based water indices, Landsat 8 and 9 OLI's ultra-blue, blue, and red band-based water indices were also tested in this study, which resulted in twenty different water indices: NDWIv (Normalized Difference Water Index), MNDWI1v and MNDWI2v (Modified Normalized Difference Water Index), AWEIsv (Automatic Water Extraction Index with shadow), and AWEInsv (AWEI without shadow), where v represents the visible light band used in the water index. At each station, about 60-80 Landsat 8 or 9 images during 2013-2023 were used for assessing the performances of the twenty water indices through comparing the estimated WSEs with the measured WSEs. Results showed that the ultra-blue or red band-based AWEIs yielded the most accurate estimations of WSEs among the twenty tested water indices.

Observations of the X-ray binary system Her X-1 by the AstroSat Soft X-ray Telescope 1 (SXT) were carried out in 2020 through 2023 with the goals of measuring X-ray spectrum changes with the 35-day disk rotation phase and measuring eclipses at different 35-day phases. The four multi-day long observations were scheduled during: turn-on to Main High (MH) and MH rise; MH state; Short High (SH) state; and Low State (LS). 35-day phase was determined using monitoring observations with the Swift Burst Alert Telescope (BAT). Nine eclipses were observed at a range of 35-day phases, with at least one eclipse during each observation. Data with dips were separated from data without dips. The variation of X-ray spectral parameters vs. 35-day phase shows the following: eclipse parameters are nearly constant, showing that the scattering corona does not change with 35-day phase; dips show an increase in covering fraction but not column density compared to non-dip data; the 1 keV line normalization indicates an origin in a small region near the continuum emission region, likely the magnetospheric accretion flow from the inner disk onto the neutron star; and the change in blackbody normalization (area) shows that the emission region is much larger than the 1 keV emission region, and consistent with the inner edge of the accretion disk.

This article argues that philosophy and its influential narrative should take and think "food" - the gastro-world - in order to radically renew itself and develop a gastrocentric view of the world and the future of humanity. It discusses first some of the basic ideas of a gastrocentric understanding of the world, second the adventure of becoming a Terran, and third the daily practice of what I will call "terra madre forming". It tells us this: If there is to be a future of human survival and well-being on Earth, if we are to achieve the goals of sustainable development, then we have a clear agenda for tomorrow with the re-humidification of the soil - madre terra - that nourishes us. What needs to be done is quite simple and down-to-earth: We, each of us, if possible, must participate in the regeneration of the earth. In conclusion, it is time for the Slow Food movement to blossom into a geo-gastro-activism. The future task of this organization is to make itself electable as a political party to support the much-needed transformation of humans into Terrans.

Photonic sensors are innovative devices that leverage the principles of light-matter interaction to enable precise and efficient detection of various physical parameters. These sensors operate by exploiting the interaction between photons, the fundamental particles of light, and matter. The light propagates through an optical waveguide, and a portion of the electromagnetic field extends beyond the waveguide surface, forming the evanescent field. This evanescent field interacts with the surrounding medium, allowing for highly sensitive detection of changes in the refractive index or properties of nearby substances. The underlying concept involves the modulation of light properties, such as intensity, wavelength, or phase, when it interacts with a target substance or undergoes changes in the surrounding environment. The advancements in photonic sensor technology have paved the way for enhanced sensitivity, selectivity, and miniaturization, making them invaluable tools in diverse industries. The ability to harness light-matter interactions for sensing purposes not only facilitates highly sensitive measurements but also allows for non-intrusive and remote monitoring, contributing to the evolution of smart and connected systems. In this overview, the material platforms and waveguide structures are explored that can make highly sensitive photonic devices applied to gas and biosensing applications.

The sleeping and grazing behaviors of horses play a crucial role in their health and welfare. In the horse industry, with the prevalent trend of individual single-stall farming in stable, accurately identifying various behaviors of horses within stables is imperative. This study has established a spatio-temporal dataset for horse postures and behaviors, proposed an algorithm utilizing a SE-SlowFast network with an enhanced loss function. This algorithm achieves automatic recognition of multiple postures and behaviors of horse simultaneously. The model developed in this study demonstrates high accuracy in identifying three postures within the stable: Standing, Crouching, and Lying, achieving respective accuracies of 92.73%, 91.87%, and 92.58%. Additionally, it accurately recognizes two behaviors: Sleeping and Grazing, achieving best accuracies of 93.56% and 98.77%. The overall best accuracy of the model is 93.9%. Experiments on video recognition confirm that the suggested model recognizes horse video data accurately. This study marks the first time computer vision technology has been used in the horse farming industry, achieving non-intrusive recognition of multiple horse postures and behaviors. This study provides valuable data support for livestock managers to evaluate the health conditions of horses, enhances the welfare of horses in stables, and contributes to the advancement of modern horse husbandry practices.

Bladder cancer (BC) is one of the most lethal genitourinary malignancies owing to its propensity for recurrence and poor survival. The biochemical pathway, signal transducer and activator of transcription 3 (STAT3), has gained significance as a molecular pathway that promotes proliferation, invasion, and chemoresistance. In this study, we explored the targeting of STAT3 with TTI-101 and SH5-07 in bladder cancer (BCa) and elucidated the mechanisms in three-dimensional (3D) spheroid and tumoroid models. We optimized the growth of spheroids from human, rat, and mouse BCa cell lines (J82, NBT-II, and MB49, respectively), and tumoroids from BBN (N-butyl-N-(4-hydroxybutyl)-nitrosamine) induced rat bladder tumors. Cell viability was assessed using MTT and trypan blue assays. ATP production, ROS production, and calcium AM (CA)/EtBr staining were used to measure the spheroid and organoid inhibition and mitochondrial function. Western blot analysis was performed to evaluate the pharmacodynamic markers involved in cell proliferation, apoptosis, cancer stem cells (CSCs), and STAT3 signaling in BCa. We found that targeting STAT3 (using TTI-101 and SH5-07) significantly reduced the proliferation of BCa spheroids, and tumoroids, which was accompanied by decreased expression of pSTAT3, Cyclin D1, and PCNA. Our data also demonstrated that treatment with STAT3 inhibitors induced ROS production and cell death in BCa spheroids and organoids. STAT3 inhibition induced cell death was associated with the activation of caspase 3/7 and PARP cleavage. Moreover, TTI-101 and SH5-07 targets cancer stem cells by downregulating the expression of CD44 and CD133 in 3D models. This study provides the first evidence for the prevention of bladder cancer with small-molecule inhibitors TTI101 and SH5-07 via suppression of CSCs and STAT3 signaling.

Approximately 20% of patients with colorectal cancer (CRC) are diagnosed with a mucinous subtype of this tumor, have a worse prognosis, and often show resistance to available therapies. Molecules from the mucin family are involved in the regulation of epithelial-mesenchymal transition (EMT), which significantly determines the cancer aggressiveness. This study aimed to examine the diagnostic and prognostic significance of mucinous histology and EMT markers in patients with early-onset CRC and their association with disease severity and tumor characteristics. The study included tumor tissue samples from 106 patients diagnosed with CRC before the age of 45, 53 with mucinous and 53 with non-mucinous tumors. The EMT status was determined by immunohistochemical analysis of E-cadherin and Vimentin in tissue sections. Mucinous tumors had a significantly higher cytoplasmic E-cadherin score (P=0.043), they were significantly less differentiated (P=0.007), more advanced (P=0.005), and predominately affected right colon (P=0.030) in comparison to non-mucinous tumors. Epithelial tumors were significantly better differentiated (P=0.034) and with less prominent tumor budding (P < 0.001) than mesenchymal tumors. The process of EMT appears to be more prominent in tumors of younger patients regardless of mucinous histology. EMT status may represent a useful prognostic tool in early-onset CRC.

The study used the data from emerging markets to examine the impact of restrictive macroprudential policies on income inequality from 2000–2019 using Bayesian panel vector autoregression and Bayesian panel dynamics generalized method of moments models. The chosen models are suitable for addressing multiple entity dynamics, accommodating a wide range of variables, handling dense parameterization, and optimizing formativeness and heterogeneous individualspecific factors. The empirical analysis utilized various macroprudential policy proxies and income inequality measures. The results show that when the central banks tighten systems using macroprudential policy instruments to sticker debt-to-income and financial instruments for lower-income borrowers (the bottom 40% of the income distribution) promote income inequality in these countries while reducing income inequality for high-income borrowers (the high 1 per-cent of the income distribution). The impact loan-to-value ratios were found to be insignificant in these countries. Fiscal policy through government expenditure and economic development reduces income inequality, while money supply and oil price shocks exacerbate it. The study suggests implementing a progressive debt-to-income (DTI) ratio system in emerging markets to ad-dress income inequality among lower-income borrowers. This would adjust DTI thresholds based on income brackets, allowing lenient credit access for lower-income borrowers while stricter limits for higher-income borrowers. This would improve financial stability and reduce income disparities. Additionally, targeted financial literacy programs and a petroleum-linked basic income program could be implemented to distribute oil revenue to lower-income households. Monetary Supply Stabilization Fund could also be established to maintain financial stability and prevent excessive inflation.

The research of object classification and part segmentation is a hot topic in computer vision. A considerable number of studies have been carried out about deep learning on 3D point clouds. However,it is challenging to achieve effective feature learning due to sparsity of point clouds. Recently, a variety of Transformers have been adopted to improve point cloud processing and display great potential. Nevertheless, large numbers of Transformer layers tend to incur huge computational and memory costs. PointNet++ is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet++ has been largely surpassed by recent networks, this does not mean that PointNet++ has no potential.Thus, this paper offer two major contributions that significantly improve PointNet++ performance. Firstly, we introduce a novel contextual feature extraction (CFE) block that significantly enhances the feature extraction capabilities of PointNet++ networks. Secondly, to further enhance feature fusion, we seamlessly integrate a mutual learning (ML) block into the network architecture. By embedding these two innovative blocks within each layer of the network, we not only enrich the network's functionality but also impart it with greater robustness and adaptability. The specific experiments were conducted on the S3DIS (6-fold cross-validation) and Modelnet40 datasets with 86.5% and 92.7% accuracy, respectively, which proved that our model is comparable or even better than most existing methods for classification and segmentation tasks.

This review deals with the developmental origins of extraocular, jaw and laryngeal muscles, the expression, regulation and functional significance of myosin heavy chains (MyHCs) they express and changes in MyHC expression during phylogeny. Myogenic progenitors from mesoderm in the prechordal plate and branchial arches specify craniofacial muscle allotypes with different repertoires for MyHC expression. To cope with very complex eye movements, extraocular muscles express 11 MyHCs, ranging from the superfast extraocular MyHC to the slowest, non-muscle MyHC IIB. They have distinct global and orbital layers, singly- and multiply-innervated fibres, longitudinal MyHC variations, and palisade endings that mediate axon reflexes. Jaw-closing muscles express the high-force masticatory MyHC, cardiac or limb MyHCs depending on the appropriateness for the acquisition and mastication of their food. Laryngeal muscles express extraocular and limb muscle MyHCs but shift towards slower MyHCs in large animals. During postnatal development, MyHC expression of craniofacial muscles is subject to neural and hormonal modulation. The primary and secondary myotubes of developing EOMs are postulated to induce, via different retrogradely transported neurotrophins, the rich diversity of neural impulse patterns that regulate the specific MyHCs they express. Thyroid hormone shifts 2A MyHC towards 2B in jaw, laryngeal muscles and possibly extraocular muscles.

Groundwater flow and transport models are essential tools for assessing and quantifying the mi-gration of organic contaminants at polluted sites. Uncertainties in the hydrodynamic and transport parameters of the aquifer have a significant effect on model predictions. Uncertainties can be quantified with advanced sensitivity methods such as Sobol’s High Dimensional Model Reduction (HDMR) and Variogram Analysis of Response Surfaces (VARS). Here we present the application of VARS and HDMR to assess the global sensitivities of the outputs of a transient groundwater flow model of the Gállego alluvial aquifer which is located downstream the Sardas landfill in Huesca (Spain). The aquifer is subject to the tidal effects caused by the daily oscillations of the water level in the Sabiñánigo reservoir. Global sensitivities are analysed for hydraulic heads, aquifer/reservoir fluxes, groundwater Darcy velocity and hydraulic head calibration metrics. Input parameters in-clude aquifer hydraulic conductivities and specific storage, aquitard vertical hydraulic conductiv-ities, and boundary inflows and conductances. VARS, HDMR and graphical methods agree to identify the most influential parameters which for most of the outputs are the hydraulic conduc-tivities of the zones closest to the landfill, the vertical hydraulic conductivity of the most permeable zones of the aquitard, and the boundary inflow coming from the landfill. The sensitivity of heads and aquifer/reservoir fluxes with respect to specific storage change with time. The aquifer/reservoir flux when the reservoir level is high shows interactions between specific storage and aquitard conductivity. VARS and HDMR parameter rankings are similar for the most influential parameters. However, there are discrepancies for the less relevant parameters. The efficiency of VARS was demonstrated by achieving stable results with a relatively small number of simulations.

In today's interconnected global business landscape, companies face numerous risks, particularly from their supply chains. Supplier disruptions can cause financial damages, inefficiencies, and reduced competitive edge, highlighting the need for robust supplier risk management. This paper scrutinizes recent studies on supplier risk management spanning 2020 to 2023. This review examines supplier risk management, focusing on supplier evaluation, payment terms, contract duration, and buyer-supplier collaboration as critical factors influencing risk mitigation and control in the procurement of goods and services. Through a systematic literature review, the study identifies the multifaceted nature of supplier risks, including production delays, quality issues, and financial challenges. It underscores the importance of proactive risk identification, comprehensive supplier evaluation, strategic payment terms, appropriate contract durations, and robust buyer-supplier collaboration. Despite the benefits, challenges remain, such as understanding regulatory impacts and ensuring methodological consistency. This review contributes to the discourse on supplier risk management, offering insights for future research and practical applications to enhance resilience and operational efficiency in global supply chains. Based on the findings, future studies should concentrate on formulating holistic supplier risks management approaches that nurture buyer-supplier collaboration and harness innovative technologies to enhance risk management practices.

This research paper investigates the enhancement of strength and performance of self-compacting concrete (SCC) for integration in Department of Public Works and Highways (DPWH) projects using a value engineering approach. The study explores key elements of SCC's strength and performance, including mix design optimization, construction practices, and durability considerations. Strategies to optimize SCC's strength, durability, and workability are discussed, emphasizing the benefits of SCC in improving construction efficiency and quality control within DPW infrastructure. The study also identifies challenges and gaps in SCC utilization, such as standardized performance verification and cost considerations, and proposes future directions for advancing SCC technology in civil infrastructure development.

The amount of solar energy produced is directly proportional to the surface area of solar panels exposed to sun rays. To increase the productivity of solar panels, a number of techniques are proposed by curving the surface or protruding the panel surface to maximise the surface area, e.g. Protruding panel surfaces with shapes such as pyramidal, cone, spherical cap, sinusoidal wavy curve, etc. These techniques are analysed to determine whether the panel surface area increases. The geometrical design analysis showed that, depending on the chosen surface curving method, the panel can achieve a remarkable increase in surface area.The system advisor model was used to assess a sample of solar cells with standard measurements of 6 by 6 inches that were organised in a zigzag pattern with different angles of inclination (10°, 15°, 20°, 25°, 30°, and 35°). Overall, the maximum power pump and annual AC energy performance increase by 1.4%, 4.2%, 6.8%, 10.8%, 14.9%, and 21.6%, respectively, over a one-year period of productivity. However, by applying the concept of solar panel efficiency degradation due to tilting solar panels at an angle by Mamun et al. (2022), for each 5° increase in tilt angle beyond the ideal 10°, the efficiency decreases by approximately 0.76%. In conclusion, the overall efficiency is greater than that of standard flat solar panels, provided the tilt angle is within a reasonable range.

The rapid expansion of Bangladesh's financial infrastructure, coupled with the increasing digitization of banking services, underscores the critical importance of cybersecurity. Machine learning and deep learning methodologies have emerged as potent tools in combating cyber threats such as intrusion, malware, and fraud. By harnessing artificial intelligence and data-driven analysis, these techniques enable real-time scrutiny of vast banking data, facilitating proactive defense against cyberattacks. Bangladesh's evolving financial landscape, characterized by governmental digitalization initiatives and a national financial inclusion strategy, heightens the imperative for robust cybersecurity measures. This paper explores the application of machine learning and deep learning techniques in bolstering banking cybersecurity within the context of Bangladesh's burgeoning digital economy.

In the last few decades, we have witnessed an unprecedented transformation in the way information is created, disseminated, and consumed. The advent of the digital age has revolutionized not only our access to information but also the very fabric of our social, political, and cultural lives. Digital media, a term encompassing everything from online news portals and social media platforms to streaming services and mobile applications, now forms an integral part of the daily lives of billions of individuals worldwide.

Barium monofluoride (BaF) is a polar molecule of interest to measurements of the electron electric dipole moment [1, 2]. For this purpose efforts are under way to investigate this molecule embedded within cryogenic matrices, e.g., in solid Ne [3]. For a theoretical understanding of the electronic structure of such an embedded molecule the need arises for efficient methods which are accurate, but also can handle a number of atoms which surround the molecule. The calculation for gas-phase BaF can be reduced to involve only outer electrons by representing the inner core of Ba with a pseudopotential [4] while carrying out a non-relativistic calculation with an appropriate basis set [5]. In this work we demonstrate to which extent this can be achieved using coupled-cluster methods to deal with electron correlation. As a test case the SrF(X 2Σ+ → B 2Σ+) transition is investigated and excellent accuracy is obtained with the EOM-CC3 method. For the BaF(X 2Σ+ → A′ 2∆, X 2Σ+ → A 2Π, X 2Σ+ → B 2Σ+) transitions various coupled-cluster approaches are compared with very good agreement for EOM-CC3 with experimentally derived spectroscopic parameters except for the excitation to the A′ 2∆ state for which the excitation energy is overestimated by 200 cm−1.

(1) Background: The rapid development of information technology has changed the traditional concept of space, gradually shifting the "container space" to the "relational space" . The constraints of geographical spatial elements are diminishing, and the theory of virtual agglomeration is gaining attention. (2) Methods: In this study, we constructed an evaluation system for virtual agglomeration, comprising four primary indicators: virtual infrastructure level, virtual output level, virtual input level, and virtual service level. The TOPSIS method was used to calculate the virtual agglomeration level for each province, and the DEA-Malmquist method was employed to measure provincial total factor productivity (TFP) in China. Using panel data from 30 provincial regions in China from 2013 to 2021, we examined the mechanism and impact path of virtual agglomeration on TFP. (3) Result: There is an inverted U-shaped relationship between virtual agglomeration and TFP in China. The overall development level of virtual agglomeration is relatively low, within the range where the marginal effect of virtual agglomeration on TFP is positive but diminishing, indicating significant potential for further development. The impact of virtual agglomeration on TFP is stronger in the Yangtze River Economic Belt compared to non-Yangtze River areas, and the highest stable point of TFP is higher in central provinces than in eastern and western provinces. (4) Conclusions: This study provides a theoretical basis and practical reference for improving the level of virtual agglomeration.

Partial Differential Equations (PDEs) constitute a fundamental framework for modeling various physical phenomena across diverse fields such as physics, engineering, and finance. Solving PDEs accurately and efficiently remains a significant challenge, particularly when dealing with complex systems or high-dimensional data. In this paper, we present a novel approach based on deep learning techniques to investigate the effective factors in solving PDEs. Our methodology leverages the expressive power of deep neural networks to learn and represent the underlying relationships between input parameters and the solutions of PDEs. By incorporating domain knowledge into the network architecture and training process, our approach aims to provide insights into the key factors influencing the solution of PDEs. We demonstrate the effectiveness of our method through numerical experiments on various PDEs, showcasing its capability to identify important features and improve predictive accuracy. Our findings suggest that deep learning techniques offer a promising avenue for understanding and analyzing the complex dynamics inherent in PDEs, paving the way for enhanced computational methods in scientific and engineering applications.

This paper introduces an innovative energy circuit that challenges conventional understandings of energy conservation, marking a significant departure from traditional frameworks. Seeking to enhance energy generation within existing paradigms, this innovation holds promise for addressing pressing global issues such as the energy crisis and environmental sustainability, while also stimulating scientific inquiry. In contrast to prevalent misconceptions rooted in philosophical and scientific constraints, this study refutes the notion of generating energy from nothing and questions the viability of perpetual motion machines as conceptual models. Instead, it proposes a novel circuit design derived from an unconventional electrical short circuit, which is shown to disrupt the established principles of energy conservation. This circuit offers a transformative approach with diverse applications, including self-recharging capabilities for electric vehicles, bolstering microgrid infrastructure, and facilitating the integration of renewable energy sources. Transcending conventional scientific boundaries, this paper prompts philosophical reflections on the evolving nature of scientific exploration. Ultimately, the energy circuit represents a pivotal step towards mitigating the global energy crisis by reducing reliance on finite resources and fostering sustainability. The paper concludes by illustrating the circuit's potential to revolutionize energy systems and contribute to a more resilient and sustainable future.

The goal of this work is to show that the underlying symmetries of effective field theory can be traced to the onset of self-similarity. In particular, we argue that the scale-free structure of fractal geometry lies at the heart of invariance principles in classical and Quantum Field Theory.

A diagnostic method is presented for analysing the large-scale behaviour of the Met Office Unified Model, which is a comprehensive atmospheric model used for weather and climate prediction. Outside the boundary layer, on scales larger than the radius of deformation, semigeostrophic theory will give an accurate approximation to the model evolution. In particular, the ageostrophic circulation required to maintain geostrophic and hydrostatic balance against prescribed forcing and a rate of change of the geostrophic pressure can be calculated. In the tropics the balance condition degenerates to the weak temperature gradient approximation. Within the boundary layer the semigeostriptic approximation has to be used because friction and rotation are equally important. Assuming the calculated pressure tendency and ageotriptic circulation match the observed model behaviour, the influence of the large-scale state and the nature of the forcing on the model response can be deduced in a straightforward way. This process is illustrated by comparing predictions of the ageotriptic circulation from the theory and the model. It is then used to show that the effects of latent heat release can be included by modifying the static stability, and to show the effect of an idealised tropical heat source on the subtropical jet. Finally the response of the ageotriptic flow to boundary layer heating in the tropics is demonstrated. These illustrations show that the model behaviour on large scales conforms with theoretical expectations, so that the results of the diagnostic can be used to aid the development of further improvements to the model.

Understanding brain function is one of the most important open problems in science today. At present, there is no concrete theory for how the brain works. Here, a theory is presented that provides a detailed mechanistic biological account of the brain’s capacities. Brain function is managed by a response (R) process that is structurally similar to the immune response, and shows anatomical and molecular specificity. Different R process stages utilize different cortical layers, hippocampus fields, basal ganglia paths, GABAergic interneurons, cerebellum paths, and molecular agents such as dopamine, serotonin and opioids. We show how the R process supports hierarchical action sequences, language and thought. The theory is supported by a large body of experimental evidence in many modalities, and accounts for virtually all of the major facts known about the brain at the system level.

Current paper is aiming to show the obtained results in manufacturing and characterization of graphite-based and metal composite materials suitable to be used in high voltage applications of automotive and/or aviation field. Thus, 4 types of materials have been processed and tested in order to assess their suitability to be used as current collector material for electric engines. The novelty of this work is the presentation of the new innovative technological process for obtaining the carbon/metal composite material used in the manufacture of sliding contacts for current collector heads, as well as the characteristics obtained for this material. All 4 obtained materials have been characterized from structural, mechanical and functional point of view and the most suitable of them has been chosen to be further developed by the end beneficiary.

Malaria and Typhoid fever are prevalent diseases in tropical regions, exacerbated by unclear protocols, drug resistance, and environmental factors. Prompt and accurate diagnosis is crucial to improve accessibility and reduce mortality rates. Traditional diagnosis methods cannot effectively capture the complexities of these diseases due to the presence of confusable symptoms. Although machine learning (ML) models offer accurate predictions, they operate as "black boxes" with non-interpretable decision-making processes, making it challenging for healthcare providers to comprehend how the conclusions are reached. This study employs explainable AI (XAI) models such as Local Interpretable Model-agnostic Explanations (LIME), and Large Language Models (LLMs) like ChatGPT, Gemini, and Perplexity, to clarify diagnostic results for healthcare workers, building trust and transparency in medical diagnostics. The models were implemented on Google Colab and Visual Studio Code because of their rich libraries and extensions. Results showed that the Random Forest model outperformed Extreme Gradient Boost and Support Vector Machines and important features for predicting Malaria and Typhoid were identified with the LIME plots. Among LLMs, ChatGPT 3.5 demonstrates a comparative advantage over Gemini and Perplexity, hence highlighting how crucial it is to use a hybrid strategy that combines ML, XAI, and LLMs to improve diagnostic performance and reliability in healthcare applications. The study recommends implementing the integrated ML, LIME, and LLM processes because it streamlines the development and maintenance process's workflow overall, makes use of resources more effectively, and improves explainability by allowing the LLM to consider the complete context that LIME provides.

Abstract: This article aims to better understand the mechanisms that connect climate change perceptions and general willingness to engage in pro-environmental behavior using Spanish cross-sectional data (N = 403). To do this, we first developed and validated the General Willingness for Environmental Behavior Scale (GWEBS), which includes the classical approach of voluntarily doing new actions but also actions implying not doing things (degrowth) and actions forced by social constraints. The exploratory and confirmatory factor analysis showed a good fit for the one-factor structure, which had adequate validity based on their relationship with other variables. Additionally, the GWEBS distinguished between women and men, left- and right-oriented people, and people who belonged to pro-environmental groups and people who did not. In the second place, we tested the parallel mediator role of eco-anxiety and trust in science in the relationship between climate change perceptions and the GWEBS. The results showed that eco-anxiety, fully, and trust in science partially mediated such a relationship, making them crucial to mobilize the intention to act following the perception. This study contributes to our understanding of the psychological mechanisms that eventually drive pro-environmental behaviors and provides a clear direction for future research.

Nutraceuticals have taken the spotlight during the past two decades as evidenced by the exponential publications on them. Long a part of routine in Traditional Medicine Systems, the rise of their mainstream use globally raises both safety concerns and need for better understanding of efficacious dosing. We attempt to answer these questions in this preliminary scoping review by an analysis of current literature on nutraceutical use as a personalized or prescription medicine. Using Covidence, Rayyan, and manual searches of PubMed, 598 unique publications were selected. 32 are systematic reviews, of which we overview the scope. We also overview 30 papers that address adverse drug reactions. To obtain an unbiased landscape of the 598 papers, we analyzed keywords using multiple methods. Expectedly, the most frequent keywords were probiotics and vitamins. Unexpectedly and remarkably, among the highest keyword yield was ‘COVID’. Further exploring this aspect, we review 15 pertinent papers, that not only provide robust evidence for nutraceutical benefits as part of SARS-CoV-2 treatment, but also amplify the notion that nutraceuticals are protective. Overall, the strident note is that further robust targeted research is needed in order to reap the full benefits of nutraceuticals in a safe and efficacious manner.

Lipids have not traditionally been considered likely candidates for catalyzing reactions in biological systems. However, there is significant evidence that aggregates of amphiphilic compounds are capable of catalyzing reactions in synthetic organic chemistry. Here we demonstrate the potential for the hydrophobic region of a lipid bilayer to provide an environment suitable for catalysis by means of a lipozyme, or lipid aggregate capable of speeding up a chemical reaction. By bringing organic molecules into the non-polar or hydrophobic region of a lipid bilayer, reactions can be catalyzed by collections of small, nonpolar or amphiphilic molecules. We demonstrate this concept by the ester hydrolysis of calcein-AM to produce a fluorescent product, which is a widely used assay for esterase activity in cells. The reaction was first carried out in a two phase octanol-water system with the organic phase containing the cationic amphiphiles cetyltrimethylammonium bromide (CTAB) or octadecylamine. The octanol was then replaced with DOPC added to the water in the form of vesicles, where the reaction was also found to be carried out. The reaction was monitored using quantitative fluorescence which revealed catalytic turnover numbers on a scale of 10−4s−1 for each system, which is comparable to some slow enzymes. The reaction product was characterized by 1H-NMR measurements which were consistent with ester hydrolysis. The implications of thinking about lipid aggregates as catalytic entities are discussed in the context of biochemistry, pharmacology and synthetic biology.

The domestic ducks (Anas platyrhynchos domesticus) are resistant to most of the highly pathogenic avian influenza virus (HPAIV) infections. In this study, we characterized the lung proteome and phosphoproteome of ducks infected with HPAI H5N1 virus (A/duck/India/02CA10/2011/Agartala) at 12 hr, 48 hr and 5 days post infection condition. Total 2082 proteins were differentially expressed and 320 phosphorylation sites mapping to 199 phosphopeptides corresponding to 129 proteins were identified. Functional annotation of proteome data analysis reveals activation of RIG-I-like receptor and Jak-STAT signaling pathways, that lead to induction of interferon stimulated genes (ISGs) expression. The pathway analysis of phosphoproteome datasets also confirms the activation of RIG I, Jak-STAT signaling, NF-kappa B signaling and MAPK signaling pathways in the lung tissues. The induction of ISGs proteins (STAT1, STAT3, STAT5B, STAT6, IFIT5 and PKR) establishes protective antiviral immune response in duck lung tissue. Further, the protein-protein interaction network analysis identified proteins like AKT1, STAT3, JAK2, RAC1, STAT1, PTPN11, RPS27A, NFKB1 and MAPK1 as main hub proteins that might play important roles in disease progression in ducks. Together, functional annotation of proteome and phosphoproteome datasets reveals the molecular basis of the disease progression and disease resistance mechanism in ducks infected with HPAI H5N1 virus.

This study investigated the characteristics and outcomes of drug-resistant tuberculosis patients in selected rural healthcare facilities in the Eastern Cape, South Africa. A retrospective review of clinical records from 456 patients, covering the period from January 2018 to December 2020, revealed that a statistically significant relationship between DR-TB types and age groups (Chi-square statistic: 30.74, p-value: 0.015). Younger adults (19-35 years) and middle-aged adults (36-50 years) are more frequently affected by RR-TB and MDR-TB, which are the most prevalent forms of DR-TB. Less common types, including Pre-XDR, XDR, and INH TB, were observed in smaller numbers. The study suggests that DR-TB imposes a heavy burden on the working-age population. Gender analysis shows that while the frequency of DR-TB differs between males and females, the percentage distribution of DR-TB types is relatively equal. Both genders are predominantly affected by RR-TB and MDR-TB, which together account for nearly 90% of cases. Pre-XDR, XDR, and INH-resistant TB are much less common, comprising only a small percentage of cases in both males and females. High-risk behaviors such as smoking and drinking are linked to a wider diversity of DR-TB types, while occupations like mining and prison work show higher rates of RR-TB and MDR-TB. In HIV-positive individuals, DR-TB is more common, but the distribution of DR-TB types between HIV-positive and negative groups shows no statistically significant difference. However, HIV-positive individuals have a 20% lower survival rate (65%) compared to HIV-negative patients (85%). Financial stability and comorbidities also significantly influence outcomes, with patients having stable income and fewer high-risk comorbidities experiencing better survival and treatment outcomes. The findings underscore the importance of addressing socioeconomic disparities and strengthening healthcare infrastructure to improve DR-TB treatment outcomes in rural Eastern Cape.

Background/Objectives: The global push to eliminate tuberculosis (TB) as a public health threat is in-creasingly urgent, particularly in high-burden areas like O.R. Tambo District Municipality, South Africa. Drug-resistant TB (DR-TB) poses a significant challenge to TB control efforts and is a leading cause of TB-related deaths. This study aimed to assess DR-TB transmission patterns and predict future cases using geospatial and predictive modeling techniques. Methods: A retrospective cohort study was conducted across five decentralized DR-TB facilities in O.R. Tambo District Municipality from January 2018 to De-cember 2020. Data were obtained from Statistics South Africa, and patient GPS coordinates were used to identify clusters of DR-TB cases via DBSCAN clustering. Hot Spot Analysis (Getis-Ord Gi) was performed, and two predictive models (Linear Regression and Random Forest) were developed to estimate future DR-TB cases. Analyses were conducted using Python 3.8 and R 4.1.1, with significance set at p < 0.05. Results: A total of 456 DR-TB patients were enrolled, with 56.1% males and 43.9% females. The mean age was 37.5 (±14.9) years. The incidence of DR-TB was 11.89 cases per 100,000 population, with males dispropor-tionately affected. Key risk factors included poverty, lack of education, and occupational exposure. DR-TB types included RR-TB (60%), MDR-TB (30%), Pre-XDR-TB (5%), XDR-TB (3%), and INHR-TB (2%). Spatial analysis revealed significant clustering in socioeconomically disadvantaged areas. A major cluster was identified, along with a distinct outlier. Predictive models indicated differing trends, with the Random Forest model predicting stabilization at 30 cases per year by 2022, while the Linear Regression model projected a decline to zero by 2026. Conclusions: The study highlights the need for targeted interventions in vulnerable populations to curb DR-TB transmission and improve treatment outcomes.

Making sustainable business models, especially in manufacturing, is a key approach for organizations to adopt in order to create more sustainable processes. New technologies are needed to implement new policies that can minimize the economic, environmental, and social consequences of industrial activity. Sustainable maintenance is decisive to guarantee the availability, reliability, and safety of assets, surpassing traditional maintenance policies and embracing the principles of the circular economy. The main objective of Maintenance 4.0 is to increase the lifespan and availability of the equipment by preventing unplanned downtime, reducing planned shutdowns, and improving safety. This research focuses on the integration of advanced Industry 4.0 tools with conventional monitoring and control techniques in process industries. The article suggests that while complex neural networks might provide good predictions, simpler solutions can generalize well and have lower prediction errors because they can capture the underlying relationships between variables in a specific asset better. The combination of maintenance function, Industry 4.0 tools, and advanced statistical solutions to obtain sustainable processes can facilitate organizational change and aid decision-making in equipment management. These solutions seek to reduce interruptions and expenses through the unnecessary utilization of labor and spare parts. All these considerations improve communication and data processing, resulting in improved maintenance of a hydrogen compressor in the petrochemical industry.

The commercial aviation industry is constantly evolving, driven by technological innovation and the growing demand for more efficient and environmental friendly transportation solutions. In this context, the design and development of next-generation aircraft is a crucial task, with the goal of reducing environmental impact and improving flight efficiency while continuing to ensure passenger comfort and safety. This paper aims at evaluating the technical feasibility as well as economic and environmental viability of a narrow body aircraft powered by hydrogen for direct combustion. Notably, the paper offers innovative insights regarding the update of literature models for conceptual design, in order to consider the impact of hydrogen fuel on the sizing loop at aircraft level. Moreover, it also focuses on the evaluation of operating costs for the aforementioned aircraft class, highlighting the required model updates and considerations to properly match the effect on costs of the introduction of the innovative fuel. Ultimately, a preliminary analysis of upstream sustainability of the concept, considering fuel life cycle, is provided, in order to understand the overall competitiveness, not only in terms of monetary resources but also of environmental impact, beyond the pure operating life of the airplane.

Chronic inflammatory diseases are considered the most significant cause of death worldwide. Current treatments for inflammatory diseases are limited due to the lack of understanding of the biological factors involved in early-stage disease progression. Nerve growth factor (NGF) is a neurotrophic factor directly associated with inflammatory and autoimmune diseases like osteoarthritis, multiple sclerosis, and rheumatoid arthritis. It has been shown that NGF levels are significantly upregulated at the site of inflammation and play a crucial role in developing a robust inflammatory response. However, little is known about NGF's temporal expression profile during the initial progressive phase of inflammation. This study aimed to determine the temporal expression pattern of NGF in rat skin during Adjuvant-Induced Arthritis (AIA). Sprague Dawley rats were randomly divided into control and Complete Freund's Adjuvant (CFA) treated groups. Levels of NGF were evaluated following unilateral AIA at different time points and it was found that peripheral inflammation due to AIA significantly upregulated the expression of NGF mRNA and Protein in a biphasic pattern. These results suggest that NGF signaling is crucial for initiating and maintaining peripheral neurogenic inflammation in rats during AIA.

System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV) , serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.

In this paper, we proposed the fusion of two projection based face recognition algorithms: local binary Patterns in DCT domain and singular value decomposition (SVD) characterized by its simplicity and efficiently. Standard databases ORL are used to test the experimental results which prove that proposed system achieves more accurate face recognition as compared to individual method.

The purpose of the present study was to examine the relationships between bat swing speed (BSS) and muscle thickness and lateral asymmetry of the trunk and limbs in collegiate baseball players. Twenty-four collegiate baseball players participated in this study. The maximum BSS in hitting a teed ball was measured using a motion capture system. The muscle thicknesses of the trunk (upper abdominal rectus, central abdominal rectus, lower abdominal rectus, abdominal wall, and multifidus lumborum), upper limb, and lower limb were measured using a B-mode ultrasonography. Lateral asymmetry between each pair of muscles was determined as the ratio of the thickness of the dominant side to that of the non-dominant side. Significant positive correlations were observed between BSS and muscle thicknesses of the abdominal wall and multifidus lumborum on the dominant side (r = 0.426 and 0.431, respectively; p < 0.05), while nearly significant positive correlations were observed between BSS and muscle thicknesses on the non-dominant side. No significant correlations were found between BSS and lateral asymmetry of all muscles. These findings indicate the importance of the trunk muscles for bat swing, and the lack of association between BSS and lateral asymmetry of muscle size.

Leptin, acting centrally or peripherally, has complex effects on cardiac remodeling and heart function. We previously reported that central leptin exerts an anti-hypertrophic effect in the heart via cardiac PPARβ/δ activation. Here, we assessed the impact of central leptin administra-tion and PPARβ/δ inhibition on cardiac function. Various cardiac properties, including QRS du-ration, R wave amplitude, heart rate (HR), ejection fraction (EF), end-diastolic left ventricular mass (EDLVM), end-diastolic volume (EDV), and cardiac output (CO) were analyzed. Central leptin infusion increased cardiac PPARβ/δ protein content and decreased HR, QRS duration and R wave amplitude. These changes induced by central leptin suggested a decrease in the ventricu-lar wall growth which was confirmed by MRI. In fact, the EDLVM was reduced by central leptin, while increased in rats co-treated with leptin and GSK0660, a selective antagonist of PPARβ/δ ac-tivity. In summary, central leptin plays a dual role in cardiac health, potentially leading to ven-tricular atrophy and improving heart function when PPARβ/δ signaling is intact. The protective effects of leptin are lost by PPARβ/δ inhibition, underscoring the importance of this pathway. These findings highlight the therapeutic potential of targeting leptin and PPARβ/δ pathways to combat cardiac alterations and heart failure, particularly, in the context of obesity

Capsinoids (CAP) are potential antioxidant agents capable of reducing oxidative damage and the resulting complications triggered by obesity. Thus, the aim of this study was to investigate the effects of CAP on adiposity and biomarkers of cardiac oxidative stress in rats with obesity induced by a high-fat diet. Male Wistar rats were exposed to a high-fat diet for 27 consecutive weeks. After characterization of obesity (week 19), some of the obese (Ob) animals began to receive capsinoids (10 mg/kg/day) by orogastric gavage. Adiposity and comorbidities were assessed. In the heart, remodeling, injury and biomarkers of oxidative stress were determinated. The treatment did not reduce the adiposity induced d by obesity, but they were efficient in reducing cholesterol levels. CAP treatment did not cause a difference in heart and LV mass, despite having reduced troponin I concentrations. Furthermore, capisinoids did not reduce the increase in advanced oxidation of proteins products (AOPP) and carbonylated proteins (CBO) caused by obesity in cardiac tissue. Besides that, it was observed that ObCap rats presented high levels of malondialdehyde and greater activity of antioxidant enzymes compared to Ob. In conclusion, treatment with capsinoids did not lead to significant positive effects in reducing obesity-induced oxidative stress.

(1) Background: Although diagnostic criteria for massive hemorrhage with organ dysfunction such as disseminated intravascular coagulation associated with delivery have been empirically established based on clinical findings, strict logic has yet to be used to establish numerical criteria. (2) Methods: A dataset of 107 deliveries with ≥2,000 g of blood loss, among 13,368 deliveries from nine national perinatal centers in Japan between 2020 and 2023 was obtained. Twenty-three patients had fibrinogen levels &lt;170 mg/dl, which is the initiation of coagulation system failure per our previous reports. Three of these patients had hematuria. We used six machine learning methods to identify the borderline criteria dividing the fibrinogen/fibrin/fibrinogen degradation product (FDP) planes, using 15 coagulation fibrinolytic factors. (3) Results: The boundaries of hematuria development on a two-dimensional plane of fibrinogen and FDP were obtained. If the FDP–fibrinogen/3–60 (mg/dl) value is positive, this indicates hematuria; otherwise, the case is non-hematuria, as demonstrated by the support vector machine method that seemed the most appropriate. (4) Conclusions: The borderline criterion dividing the fibrinogen/FDP plane for patients with hematuria that could be considered organ dysfunction in massive hemorrhage during delivery was obtained using artificial intelligence, and this method seemed to be useful.

Background: This study investigates microvascular changes in the dermis and muscle following a balneotherapy regimen prescribed by therapeutic protocols. Methods: Thirty-five inpatients participated in a two-week balneal course involving the application of mud and hypersaline water. Mud was applied outdoors in the summer as cold mud ointment and indoors year-round as mud packs or mud baths alternated with hypersaline water baths. Biopsies of the skin and muscle were obtained twenty-four hours post-discharge and prepared histologically. Results: Analysis focused on the epidermis, dermis, and muscle. Microvascular changes observed included increased caliber and number of open capillaries, as well as the presence of angiogenesis vessels. Statistical analysis showed significant differences in angiogenesis vessels between cold mud ointment and mud pack treatments compared to control and mud bath groups. A higher number of angiogenesis vessels were observed in the dermis in the mud pack group and in the muscle with mud cold ointment. Repeated thermal stimulation with mud and hypersaline water over two weeks produced histologically evident tissue modifications, persisting in the dermis and muscle at least twenty-four hours post-treatment completion.

In individuals with spinal cord injury (SCI), rapid skeletal muscle atrophy and metabolic dysfunction pose profound rehabilitation challenges, often resulting in substantial loss of muscle mass and function. This study evaluates the effect of combined neuromuscular electrical stimulation (Comb-NMES) on skeletal muscle cross-sectional area (CSA) and inflammatory signaling within the acute phase of SCI. We applied a novel Comb-NMES regimen, integrating both high-frequency resistance and low-frequency aerobic protocols on the vastus lateralis muscle, to participants early post-SCI. Muscle biopsies were analyzed for CSA and inflammatory markers pre-and post-intervention. The results revealed a preservation of muscle CSA in the Comb-NMES group compared to a control group receiving transcutaneous electrical nerve stimulation and passive movements. Inflammatory signaling proteins such as TLR4 and Atrogin-1 were downregulated, whereas markers associated with muscle repair and growth were modulated beneficially in the Comb-NMES group. The study's findings suggest that early application of Comb-NMES post-SCI can attenuate inflammatory pathways linked to muscle atrophy and potentially promote muscle repair, advocating for its inclusion in early rehabilitation efforts. However, the small sample size and variability in injury characteristics emphasize the need for further research to corroborate these results across a more diverse and extensive SCI population.

Quaking aspen (Populus tremuloides Michx.) is an important deciduous species in western U.S. forests. Existing maps of aspen distribution are based on Landsat imagery and often miss small stands (&lt;0.09 ha or 30 m²), which rapidly regrow when managed or following disturbance. In this study, we present methods for deriving a new regional map of aspen forests using one year of Sentinel-1 (S1) and Sentinel-2 (S2) imagery in Google Earth Engine. We assessed the annual phenology of aspen in the Southern Rockies and leveraged the frequent temporal resolution of S1 and S2 to create ecologically relevant seasonal composites. Additionally, we derived spectral indices and radar textural features targeting the canopy structure, moisture, and chlorophyll content. Using spatial block cross-validation and Random Forests, we assessed the accuracy of different scenarios and selected the best performing set of features for classification. Comparisons were then made with existing landcover products across the study region. The resulting map improves on existing landcover products in both accuracy (average F1-score = 93.1%) and detection of smaller forest patches. These methods enable accurate mapping at spatial and temporal scales relevant to forest management for one of the most widely distributed tree species in North America.

Named Entity Recognition (NER) serves as a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries.To address these challenges, a corpus, labeled datasets and a lexicon specific to pig diseases for Chinese named entity recognition were constructed. Subsequently, a Pig Disease Chinese Named Entity Recognition (PDCNER) model was proposed. The model integrates external lexicon knowledge of pig disease by employing Lexicon-enhanced BERT and enhance feature representation by incorporating contrastive learning. Experimental results show that the model achieved the best recognition results, with a precision of 86.92%, a recall of 85.08%, and an F1-score of 85.99% respectively. Furthermore, the model exhibits robustness and generalizability across few-shot and publicly available datasets. Experimental results illustrate the proposed model could effectively identify Chinese named entities of pig diseases, outperforming several existing baseline methods. Moreover, the proposed model can be extended to other animal disease domains, such as chicken and cattle, thereby facilitating seamless adaptation for named entity identification across diverse contexts.

Ischemic stroke is a leading cause of mortality worldwide, and intravenous thrombo-lysis, while improving functional outcomes, still leaves a significant mortality rate. This study aimed to investigate the clinical and pathological data of thrombolysed stroke patients who subsequently died and underwent autopsy, focusing on hemorrhagic transformation (HT). Over a 10-year period, 1426 acute ischemic stroke patients re-ceived thrombolysis at our center, with an in-hospital mortality rate of 11.7%. Autop-sies were performed on 98 of the 167 deceased patients. HT was found in 47% of these cases, only less than half occurring within a day of thrombolysis. Significant inde-pendent predictors of HT included higher lactate dehydrogenase (LD) levels and higher INR values at admission. HT directly caused death in 30% of cases, often through herniation, while other complications (pulmonary embolism, pneumonia) were also common. These findings underscore the importance of postmortem investi-gations to accurately determine the incidence of HT and contributing factors. Our data indicate that in the vast majority of HT cases, the role of contributing factors other than rt-PA may be important. Of the routinely assessed clinical and laboratory parameters at admission only LD and INR were found to be independent predictors of HT in the autopsied studied cohort.

Cerebral palsy (CP) presents challenges in movement and posture due to central nervous system damage, necessitating lifelong management. The COVID-19 pandemic has limited access to traditional treatments, increasing demand for home-based therapies and digital resources. This study analyzed the qualitative and quantitative aspects of YouTube videos focusing on CP therapy for children. In total, 95 videos were evaluated for content quality, therapeutic program efficacy, and popularity. Most videos were uploaded by health professionals. Neuromotor therapy types varied, with Bobath therapy being prominent. The quality assessments revealed moderate to high reliability in video content; nonetheless, shortcomings in addressing uncertainties were observed. Therapeutic programs often lack crucial details such as intensity and outcome assessment methods. No significant differences were found between professional and non-professional uploads. Classifiable videos based on exercise type, in contrast to the types of neuromotor therapy, exhibited better therapeutic quality than unclassified ones. Notably, video popularity did not correlate with quality. This study underscores the need for improved educational content on YouTube, particularly in providing comprehensive therapeutic information for CP management. Further refinement of evaluation tools is essential for assessing the rationale behind neuromotor therapeutic modalities for children with CP.

The design and integration of intelligent energy management systems in hybrid Electric Vehicle (EV) charging stations, leveraging Industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. This paper introduces a pioneering hybrid EV charging station that employs hybrid power sources, combining photovoltaic panels, a battery, and a fuel cell, each operating at 240V and 14A. Utilizing wireless power transfer technology, the system comprises five integral blocks: a power source, a boost converter, a High-Frequency (HF) inverter, transfer coils, and EV batteries. The optimization process involves two key stages: i) maximum power point tracking optimization of the boost converter, ensuring maximum power generation by varying the duty cycle between 10% and 90%, and ii) the HF phase employing a class-ϕ2 inverter at 30 MHz, synchronized with the resonant frequency of wireless power transfer coils. Control is executed through a digital signal processor card utilizing zero-voltage switching for efficient operations. This design enhances sustainability in EV charging solutions by optimizing power transfer through the use of hybrid sources.

The focus of our study was the changes in the composition of semi-natural dry grasslands in Hungary. Since these valuable habitats were created with the help of humans, their preservation also requires regular treatment. Our mowing experiment aimed to investigate the suppression of the native Calamagrostis epigejos L. Roth. In Hungary, in the Cserhát Mountains, eight permanent plots were mowed twice a year. The vegetation was sampled annually between 2001 and 2011. The effects of mowing were investigated with repeated measures analysis of variance (ANOVA). After 10 years, the C. epigejos cover of the mowed plots decreased significantly, from the initial average of 62.38 to 7.50%. Surprisingly, we also experienced a similar decrease in the control plots. Regular treatment also had a positive effect on the number of species and diversity. Species richness increased continuously in both treatment types, which indicates the combined effect of vegetation succession and treatment. The biomass growth of other Poaceae and Fabaceae species, which are important from a grassland management perspective, was also facilitated by mowing. Our results suggest that long-term in situ experiments and continuous botanical studies are needed to establish a reliable utilization of grasslands.

This review delves into the growing field of flexible and stretchable strain sensors utilized in human gait analysis, focusing on determining knee bending angle and muscle activities when a human subject walks. Recent advancements have enabled these sensors to accurately capture biomechanical parameters while accommodating the body's natural movements. We examine the principles underlying these sensors' design and fabrication techniques, emphasizing their flexibility, stretchability, and biocompatibility for seamless integration into wearable systems. Through critical evaluation of existing methodologies, we assess the reliability and validity of these sensors in quantifying gait parameters. Furthermore, we discuss this technology's clinical and research implications, highlighting its potential in personalized rehabilitation and sports performance optimization. We provide an overview of flexible strain sensors and their application for analyzing human walking gait, especially for observing walking gait in rehabilitation programs. First, we present the basic working principles of various flexible strain sensors. Second, by the application of flexible strain sensors as walking gait monitoring. Finally, current challenges and future opportunities in this research area are discussed. By synthesizing current knowledge and outlining future directions, this review aims to provide insights for researchers and practitioners seeking to leverage flexible and stretchable strain sensors for comprehensive human gait analysis.

Maize mosaic virus (MMV) is a plant virus that is transmitted by the corn planthopper, Peregrinus maidis Ashmead (Hemiptera: Delphacidae) in a persistent and propagative manner. MMV is the causal agent for mosaic disease that can cause significant loss in corn yield. Management is focused on controlling insect vector population. The molecular mechanisms of virus transmission by the insect vector are not fully understood. Previously, we identified a receptor for MMV, the P. maidis syntaxin 18 (PmStx18). PmStx18 interacts with the viral glycoprotein and potentially facilitates MMV recognition and entry into the vector, enabling virus proliferation. Stx18 is a key component of the vesicular trafficking machinery and also plays a critical role in neurotransmitter release in insects. In this work, we investigated the expression of PmStx18 in different developmental stages and major parts of the insect vector. Primers were designed to generate templates for synthesis of double-stranded RNA fragments. A 374-bp amplicon was selected and gene-specific sequences were flanked by T7 promoter sequences for in vitro transcription. Results showed a significant difference in the expression of Stx18 in the different developmental stages of P. maidis but not between male and female adults and major body parts. In vitro transcription products indicated optimal reaction conditions and protocol suitability. The findings establish the initial steps for developing RNAi-based approach in controlling MMV transmission by targeting Syntaxin-18 in P. maidis. Further investigations are needed to evaluate the efficacy of this approach.

Lidocaine hydrochloride (HCl) 2% with 1:100,000 epinephrine (LW/E) is widely used to prevent pain during dental procedures and has been associated with injection sting, jittering effects, slow onset, and a bitter aftertaste. Since LW/E’s introduction in 1948, no significant modifications have been proposed. This study aims to design and characterize an improved dental lidocaine HCl injectable formulation without epinephrine (LW/O/E) via buffers, sweeteners, and amino acids. LW/O/E injections were prepared with pH and osmolality values of 6.5 – 7.0 and 590-610 mOsm/kg. Using the electronic tongue (ETongue), the LW/O/E injectable formulations were characterized for viscosity, injectability, and taste analysis. The results were compared with the LW/E control. In vivo efficacy and anesthetic duration of the samples were measured through radiant heat tail-flick latency (RHTFL) and hot plate (HP) tests and local toxicity was assessed after a single intra-oral injection in Sprague Dawley rats (SDR). The viscosity and injectability values of the LW/O/E samples were found to be comparable to the LW/E injection. ETongue taste analysis showed an improvement in bitterness reduction of the LW/O/E samples compared to the LW/E formulation. Toxicity studies of samples in SDR showed minor and transient signs of erythema/eschar and edema. Anesthetic duration via RHTFL and HP paw withdrawal latency time in SDR were found to be comparable for the LW/O/E Sample 3A and the LW/E injection (p<0.05). In conclusion, the buffered, higher osmolality and reduced bitterness developed LW/O/E formulation (Sample 3A) could be considered a promising alternative to the LW/E formulation for dental use.

Vision impairment may delay responses to hazards when driving. In a proof-of-concept driving simulator study, we evaluated a hazard warning device designed for vision impaired drivers. Three groups participated: 11 persons with central vision loss (CVL; median age 60 years), 12 with homonymous field loss (HFL; 52 years) and 11 with normal vision (NV; 60 years). CVL is characterised by impaired visual acuity and contrast sensitivity while HFL is a loss of vision in the same half of the visual field (blind side) in both eyes. Participants completed drives with and without the device which gave a directional vibro-tactile warning for an approaching pedestrian hazard when collision risk exceeded a pre-defined threshold. Warnings significantly (p< 0.001) reduced brake response times for CVL (∆= 0.54s) and HFL (∆= 0.30s), but not NV participants. For HFL participants, the reduction was greater (p = 0.02) for hazards on the blind (∆= 0.57s) than seeing side (∆= 0.10s). The warnings significantly reduced collision rates for CVL participants from 11 to 0% (all hazards) and for HFL participants from 30 to 1.7% (blind side hazards). Thus, hazard warnings may be beneficial in improving safety of vision impaired drivers; further research is warranted.

The accurate detection and assessment of stress play a pivotal role in enhancing individual well-being and healthcare outcomes. Traditional methods of stress detection often grapple with limitations in accuracy and scalability. With the advent of machine learning (ML), the potential to revolutionize stress detection has emerged. This paper presents a comprehensive study on the application of ML algorithms for stress detection, with a focus on physiological and behavioral data analysis. Central to our approach is the integration of SHapley Additive exPlanations (SHAP), an Explainable Artificial Intelligence (XAI) technique, to interpret ML models. SHAP provides a novel lens to understand the impact of individual features in the complex decision-making processes of ML models, thereby enhancing the transparency and reliability of stress predictions. We demonstrate how SHAP not only aids in elucidating model decisions but also contributes to refining the models for greater accuracy. Our results highlight the effectiveness of ML in detecting stress and the pivotal role of XAI in making these models more interpretable and trustworthy. This study underscores the synergy between advanced ML techniques and XAI, paving the way for more nuanced and reliable stress detection methodologies that are essential in diverse settings, from healthcare to workplace environments.

Exposure to cadmium sulfate (CdSO4) can lead to neurotoxicity. Nevertheless, the precise molec-ular mechanisms underlying this phenomenon remain unclear, and effective treatment strategies are scarce. This study explored the protective effects of ellagic acid (EA), a natural polyphenolic compound, against CdSO4 exposure-induced neurotoxicity in HT22 cells. Additionally, the inves-tigation sought to elucidate the molecular mechanisms underlying this phenomenon. Our findings demonstrated that exposure of HT22 cells to CdSO4 resulted in apoptosis, which was effectively reversed by EA supplementation. EA supplementation also decreased reactive oxygen species (ROS) and mitochondrial ROS productions, reduced malondialdehyde (MDA) levels, and restored the activities of superoxide dismutase (SOD) and catalase (CAT). Additionally, EA supplementation at 5–20 μM significantly counteracted Cd-induced mitochondrial membrane potential and ATP losses and reduced the ratio of Bax/Bcl-2 and cleaved-caspase-3 protein expression. Furthermore, EA supplementation upregulated Nrf2 and HO-1 protein expression while downregulating the phosphorylation of JNK and p38 proteins. Pharmacological inhibition of JNK partially inhibited CdSO4-induced Nrf2/HO-1 activation and exacerbated its cytotoxicity. In conclusion, our results indicate that EA supplementation provides protection against CdSO4-induced cell apoptosis in HT22 cells by inhibiting oxidative stress and activating the Nrf2 pathway. Activation of the JNK pathway plays a protective role in CdSO4-induced cell apoptosis in HT22 cells.

Background/Objectives: Caregivers play an important role in supporting care recipients to navigate their health needs including adherence to dietary recommendations which are complex and multifaceted. The study aimed to i) describe the nutrition knowledge of caregivers of people with chronic kidney disease (CKD), and ii) explore caregivers’ perception of their role in providing healthy meals and nutrition support for care recipients. Methods: Caregivers (n=78) of people with stage 1-5 CKD or post-transplant were recruited from a single centre. A sequential mixed-methods approach was used. Nutrition knowledge was assessed with the revised General Nutrition Knowledge questionnaire. Theory informed semi-structured interviews in a sub-sample (n=12) explored caregiver perception. Results: Most caregivers were female (75.6%) caring for a male care recipient (87%; aged 74 (66;80) yrs.). Caregivers (75.6%) provided a meal ≥6 times/week to their care recipient and had moderate nutrition knowledge (66.1 (60.5; 73.9) %). Four themes emerged describing caregivers’ perception on meal provision and nutrition support including i) food literacy skills are valued, ii) social support is important, iii) caregivers’ sense of social responsibility, and iv) meal provision is complex and dynamic. Conclusions: Caregivers have moderate nutrition knowledge; they want to provide healthy meals and support to care recipients to adhere to dietary recommendations. Targeted, co-designed nutrition education programs for caregivers may enhance nutrition care delivery to people with CKD.

A Partial discharge (PD) occurrence in high-voltage (HV) and medium-voltage (MV) power assets is a warning sign of insulation system degradation or defective manufacturing/assembly, that may cause catastrophic damage and eventually lead to the breakdown of the power equipment. Ultra-high frequency (UHF) PD measurement technology has extensively been used to monitor the insulation health of the HV or MV equipment due to its high immunity against ambient noise and conducted disturbances. This paper presents experimental validation of a new design of a UHF planar rectangular microstrip antenna that can be used for PD monitoring of HV and/or MV power equipment, such as power transformers. The designed planar rectangular microstrip antenna has a compact size of 80mmx70mmx1.6mm. Laboratory experiments were performed to validate the antenna sensitivity to PD activity caused by insulation defects created inside an oil-filled transformer tank model. PD measurement results confirm that the proposed rectangular microstrip antenna has high sensitivity and high signal-to-noise ratio (SNR), thanks to its capability to suppress external noise disturbances, especially during online PD monitoring. Thus, the proposed UHF antenna seems to be a promising and alternative PD sensor that can be employed in UHF PD monitoring systems of HV or MV power equipment.

This study presents a detailed computational investigation aimed at identifying and evaluating potential small organic molecular scaffolds as inhibitors targeting the Pks13 protein, a critical enzyme in tuberculosis (TB) drug discovery. Our approach integrates multiple computational techniques, including quantitative structure-activity relationship (QSAR) modelling, compound library generation, molecular docking, biological activity prediction, re-evaluation of screened compounds, molecular dynamics (MD) analysis, and binding free energy estimation using the MM-GBSA approach. We developed a robust QSAR model using multiple linear regression (MLR) techniques, specifically tailored to predict the minimum inhibitory concentration (MIC) values of potential anti-TB agents. This model demonstrated excellent predictive performance, validated through rigorous internal and external validation procedures. Through molecular docking simulations, we identified five promising inhibitors Ethyl (R,Z)-2-(2-((2-chlorobenzyl) oxy)benzylidene)-5-(4-chlorophenyl)-7-methyl-3-oxo-2,3-dihydro-5H-thiazolo [3,2-a] pyrimidine-6-carboxylate (PKD1), (7R,9aR)-7-(2-hydroxyphenyl)-4-(2-methoxyphenyl)-3-methyl-7,8,9,9a-tetrahydroisoxazolo[5,4-b]quinolin-5(6H)-one (PKD2), (7-(4-methoxy phenyl)-2-methyl-3-phenylpyrazolo[1,5-a]pyrimidin-5-yl)(4-(3-(trifluoromethyl)phenyl) piperazin-1-yl)methanone (PKD3), (8S,10S)-10-(((2R,4S,5S,6S)-4-amino-5-hydroxy-6-methyl tetrahydro-2H-pyran-2-yl)oxy)-6,8,11-trihydroxy-8-(2-hydroxyacetyl)-1-methoxy-7,8,9,10-tetra-hydrotetracene-5,12-dione (PKD4), 3-(3,4-dimethylphenyl)-5-((2-(4-fluorophenyl)-2-oxoethyl) thio)-6-phenyl-2-thioxo-2,3,5,7a-tetrahydrothiazolo[4,5-d]pyrimidin-7(6H)-one (PKD5)with high binding affinities, prioritizing them based on their binding energies. These selected compounds were further assessed for their biological activity predictions, guiding the selection of candidates for experimental validation. Subsequent re-evaluation using absolute binding free energy estimation and MD simulations provided insights into the stability and dynamics of the protein-ligand complexes over time. Our findings highlighted the potential of identified compounds as stable binders within the Pks13-TE domain binding pocket, underscoring their viability as drug candidates. Additionally, binding free energy analysis using MM-GBSA reaffirmed the strong affinity of the proposed compounds towards Pks13. Our integrated computational approach offers a systematic and detailed framework for the identification and characterization of potential Pks13 inhibitors with specific relevance to TB drug discovery.

Abstract: The Willows, belonging to the genus Salix, encompass a vast array of species and hybrids, totaling between 330 to 500 species along with approximately 200 hybrids. These plants are found across continents such as Africa, North America, Europe, and Asia, exhibiting diverse forms ranging from trees to shrubs and prostrate plants. Traditionally, Willows have been utilized in folk medicine due to their rich reservoir of biologically active compounds, with salicin being a notable example, serving as a prodrug for salicylic acid.The chemical composition of Willows is highly diverse, with 322 secondary metabolites identified thus far. These include various classes such as flavonoids (94 types encompassing flavonols, flavones, flavanones, isoflavones, flavan-3-ols like catechins and procyanidins, chalcones, dihydrochalcone, anthocyanins, and dihydroflavonols), phenolic glycosides (76), organic acids (28), non-phenolic glycosides (17), sterols, and terpenes (17), simple phenolics (13), lignans (7), as well as volatiles and fatty acids (69). This rich chemical di-versity underpins the various pharmacological activities exhibited by Willows. The pharmacological properties attributed to Willows are extensive and include analgesic, an-ti-inflammatory, antioxidant, anticancer, cytotoxic, antidiabetic, antimicrobial, antiobesity, neuroprotective, and hepatoprotective effects. These properties make Willows a subject of con-siderable interest in the fields of medicine and pharmacology. In summary, Willows represent a valuable natural resource with a diverse chemical composition and a wide range of pharmacological activities. This study aimed to enhance our comprehension of the medicinal characteristics of willow bark extract, focusing on its antimicrobial properties. This investigation holds significance in validating the traditional usage of willow bark and investi-gating its potential utility in contemporary medicine.

This review summarizes the most recent developments of research on protozoan parasite infections ac-quired through food and drinking water and is aimed at gathering updated knowledge on the risk factors, illnesses caused, and measures for prevention. Reports included dated from 2019 to the present and regarded recent outbreaks and cases of severe infec-tions, prevalence in different population segments and food handlers, and occurrence in food and drinking water. Cryptosporidium spp., Entamoeba histolytica, Giardia intestinalis, Trypanosoma cruzi and Toxoplasma gondii were the microorganisms most frequently involved in large outbreaks and/or severe infections, while Cyclospora cayetanensis was most frequently involved in single case reports. Cystoisospora belli was implied in severe infections in immunocompromised patients, while Blastocystis homi-nis, Sarcocystis spp., Dientamoeba fragilis, Endolimax nana and Pentatrichomonas hominis appeared to be less relevant for number of symptomatic infections and pathogenic potential. A progressive improvement in surveillance of protozoan infections and infection sources was highlighted, especially in developed countries. The apparent increase in cases was concomitant with the implementa-tion of reporting systems and the application of molecular diagnostic methods that represent valuable in-struments for establishing epidemiological links and preventing or limiting outbreaks. The pathogenic role and sources of infection should be better elucidated for some species.

We monitored the water quality and hydrological conditions of a horizontal subsurface constructed wetland (HSSF-CW) in Beijing, China, for 2 years. We simulated the area rate constant and the temperature coefficient with the first-order kinetic model. We examined the relationships between the nitrogen (N) removal rate, N load, seasonal variations in the N removal rate, and environmental factors, such as the area rate constant, temperature, and dissolved oxygen (DO). The effluent ammonia (NH₄⁺-N) and nitrate (NO₃⁻-N) concentrations were significantly lower than the influent concentrations (p<0.01, n=38). The NO₃⁻-N load was significantly correlated with the removal rate (R²=0.9566, p<0.01), but the NH^₄+-N load was not correlated with the removal rate (R²=0.0187, p>0.01). The area rate constants of NO₃⁻-N and NH₄⁺-N at 20 °C were 27.01±26.49 and 16.63±10.58 m∙yr⁻¹, respectively. The temperature coefficients for NO₃⁻-N and NH₄⁺-N were estimated at 1.0042 and 0.9604, respectively. The area rate constants for NO₃⁻-N and NH₄⁺-N were not correlated with temperature (p>0.01). The NO₃⁻-N area rate constant was correlated with the corresponding load (R²=0.9625, p<0.01). The NH₄⁺-N area rate was correlated with DO (R²=0.6922, p<0.01), suggesting that the factors that influenced the N removal rate in this wetland met Liebig's law of the minimum.

(1) Background: To further investigate the link between inadvertent tooth movements (ITMs) and mechanical properties of different retainers, the response upon vertical loading was compared in three different retainers (two stainless steel and one nickel-titanium). The influence of different reference teeth was also considered. (2) Methods: Three retainers (R1, R2, R3) were tested in a newly developed biomechanical analysis system (FRANS). They were bonded to 3D-printed models of the lower anterior jaw and vertically displaced up to 0.3mm. Developing forces and moments were recorded at the center of force. (3) Results: The vertical displacement caused vertical forces (Fz) and labiolingual moments (My) to arise. These were highest in the lateral incisors (up to 2.35±0.59N and 9.27±5.86Nmm for R1; 1.69±1.06N and 7.42±2.65Nmm for R2; 3.28±1.73N and 15.91±9.71Nmm for R3) for all analyzed retainers, and with the R3 retainer for all analyzed reference teeth, while the lowest Fz and My values were recorded with the R1 retainer. (4) Conclusions: Displacements of 0.2mm and larger provided forces and moments which could be sufficient to cause unwanted torque movements, such as ITMs in all analyzed retainers. Clinicians must be mindful of these risks and perform posttreatment checkups on patients with retainers of all materials.

Background/Objectives: This study aimed to identify various risk factors associated with breast cancer and explore different treatment strategies based on socio-economic, hormonal, and clinical perspectives.; Methods: We analyzed socio-economic, hormonal, genetic, and clinical variables using data from 356 questionnaires obtained from breast cancer patients at Hassan II Regional Hospital in Agadir, Morocco ; Results:The study reveals that the average age of breast cancer patients is 50 ± 10.72 years, with a mode of 47 years and an age range from 29 to 94 years. Notably, women aged 38 are most frequently diagnosed with triple-negative breast cancer. Significant socio-economic trends were observed among the participants, with the majority being housewives (91%) and nearly half residing in rural areas (48.60%). High rates of illiteracy (74.72%) and financial difficulties (54.2%) pose challenges in accessing healthcare services. Psychological distress is prevalent, with 40.7% experiencing anxiety and 30.1% experiencing sadness. The use of oral contraceptives varies, with implications for breast cancer risk. Additionally, statistics on menarche and menopause suggest hormonal influences on breast cancer development. Histological analysis indicates a predominance of grade II invasive ductal carcinoma and left breast localization. Metastases are present in nearly 38.20% of cases. Notably, surgery as the primary treatment effectively treats medium-sized tumors while limiting metastasis. Furthermore, a comparison of treatment strategies reveals a preference for letrozole among postmenopausal patients and tamoxifen among premenopausal patients; Conclusions:These findings highlight the complexity of breast cancer risk factors and therapeutic approaches, emphasizing the importance of personalized care and targeted interventions.

Background - Compared to the da Vinci platform, there is limited experience with the Hugo RAS platform for colorectal surgery in Europe. This difference is especially notable when considering a single complex procedure, such as total colectomy. Aim - To demonstrate the feasibility and safety of using the Hugo RAS for assisted total colectomy. Clinical case- Female patient: eighteen-year-old with Familial Adenomatous Polyposis (FAP). Her IBM was 19. She underwent a total colectomy with ileorectal anastomosis using the Hugo RAS platform. The procedure took 230 minutes and had no complications. The postoperative period was uneventful. She was discharged from the hospital on the third postoperative day. Conclusion - The Hugo RAS platform is one of the emergent minimally invasive robotic platforms that can be used even in total colectomy if the proper patient selection is made. The placement choice of arms and trocars was crucial to obtain a similar operative time of the single laparoscopic approach. The certification of Hugo’s new instruments, such as energy devices and staplers, will make this platform even more competitive.

Though Bangladesh is often labelled as "climate change ground zero," the Chittagong Hill Tracts (CHT) were never considered a significant climate change hotspot until heavy rainfall and consequent landslides in 2017. This shift in problem narrative seemingly overlooks myriad environmentally unsustainable 'development' initiatives along with (il)legal deforestation. This paper examines climate change trends and impacts on the lives and livelihoods of diverse local groups in CHT. By analysing changing patterns in meteorological data and qualitative information on changes and challenges in everyday life and the livelihoods of local people, we show a shortfall of captured climate data on CHT to make a strong case for climate change. Yet, this study finds that this region is exposed to a substantial threat of land degradation, flash floods, water scarcity, landslides, and other slow and quick onsets due to a combined effect of deforestation, neoliberalised development, and slight changes in temperature and precipitation patterns. This paper calls for proactive measures to address climate change impacts by endorsing sustainable development strategies prioritizing environmental stewardship, social justice, and economic prosperity.

Traditional seismic wave-based tunnel advanced geological forecasting techniques are primarily designed for drill and blast method construction tunnels. However, given the fast excavation speed and limited prediction space in Tunnel Boring Machine (TBM) construction tunnels, traditional methods have significant technical limitations. This paper analyzes the characteristics of different types of TBM construction tunnels and, considering the practical construction conditions, identifies an effective observation system and data acquisition method. Utilizing numerical simulation, a model for advanced detection of typical unfavorable geological formations is established by obtaining the wave field response characteristics of seismic waves in three-dimensional space. The seismic wave reflection method can effectively receive information from unfavorable geological formations in front of the tunnel face. To address the challenges in advanced forecasting for TBM construction tunnels, a method of ellipsoid positioning velocity analysis, which takes into account the constraints of three-component data directions, is proposed. Based on the characteristics of the advanced forecasting observation system, the method compares the maximum values on the spatial isochronous ellipsoidal surface to determine the average velocity of the geological layer rays, thereby enabling accurate inversion of the spatial distribution ahead. Field verification of advanced geological forecasting based on ellipsoid positioning velocity analysis for TBM tunnels is conducted. The results indicate that this method can be used to obtain the three-dimensional velocity of the predicted unfavorable geological formations in front of the tunnel and is effective for the application of geological forecasting in TBM construction tunnels, thus providing crucial references and assurances for construction.

Industry 4.0 (I4.0) epitomizes the nexus of technological evolution and manufacturing process enhancements, underpinned by four critical pillars: Cyber-Physical Systems (CPS), Digital Twins (DT), the Industrial Internet of Things (IIoT), and Cloud Computing (CC). Through the lens of the German Reference Architectural Model Industrie (RAMI) 4.0, these components are integral to fostering Smart Manufacturing (SM) practices. In conjunction, the Asset Administration Shell (AAS) facilitates seamless inter-company communication and enables adaptability to fluctuating market demands. This paper delves into the structural intricacies of AAS and SM within the RAMI 4.0 framework, unveiling three distinct implementation strategies. Furthermore, it meticulously scrutinizes challenges that hinder the full realization of I4.0 potential, such as interoperability, security concerns, and standardized communication protocols. By examining these models and identifying current barriers, the study illuminates pathways for future research, particularly in enhancing the integration of AAS and SM systems, ensuring robust security measures, and advocating for the development of universal standards. This exploration aims to contribute to the ongoing discourse on I4.0, proposing avenues for advancing manufacturing technologies and operational frameworks in alignment with the principles of RAMI 4.0.

Pulmonary hypertension (PH) is a serious condition linked to immune-system dysfunction. Myositis-specific/associated antibodies (MSA/MAA) play a role in idiopathic inflammatory myopathy (IIM) and interstitial lung disease (ILD), but their significance in PH remains unclear. We believe the presence of these antibodies, may be underestimated. This study analyzed adult PH patients without pre-existing IIM for MSA/MAA prevalence using a line-blot assay. We compared PH patients with and without ILD signs to a cohort clinically suspected of IIM/ILD (n=558). Our PH cohort (n=121) showed a significantly higher prevalence of overall weak positive MSA/MAAs and positive overlap syndrome-associated MAAs than the suspected IIM/ILD group (p

Laser-Powder Directed Energy Deposition (DED-LB/Powder) is an additive manufacturing process that is gaining popularity in the manufacturing industry due to its numerous advantages, particularly in repairing operations. However, its application is often limited to case studies due to some critical issues that need to be addressed, such as the degree of internal porosity. This paper investigates the effect of the most relevant process parameters of the DED-LB/Powder process on the level and distribution of porosity. Results indicate that, among the process parameters examined, porosity is less affected by travel speed and more influenced by powder mass flow rate and laser power. Additionally, a three-dimensional finite element transient model was introduced, which was able to predict the development and location of lack-of-fusion pores along the building direction.

Human papillomavirus (HPV) can potentially compromise both unassisted conception and success rates with assisted reproductive technology (ART). We aimed to evaluate the rates of HPV vaccination among women presenting for fertility care. This was a prospective study analyzing a self-reported intake form completed by all new patients presenting to a fertility clinic. The primary outcome was HPV vaccination status. Among the 523 new patients included in this study 172 (32.9%) were fully vaccinated, 237 (45.3%) were unvaccinated, and 95 (18.2%) patients didn’t respond. There was a significant age difference between fully vaccinated patients (33.1 years) compared to unvaccinated patients (37.4 years) p&lt;0.001 and a significant difference in gravidity between fully vaccinated patients (average 0.65) and unvaccinated patients (average 1.11) p=0.0005. No significant association was found between HPV vaccination status and history of abnormal Pap smears or relationship status (single vs. married). Less than half of patients presenting to our fertility center were vaccinated against HPV, despite being eligible to receive the vaccine. Younger patients with lower gravidity were more likely to be vaccinated against HPV than older patients with higher gravidity. Reproductive Endocrinologists have the opportunity to recommend HPV vaccination for eligible patients presenting for fertility care.

The rapid growth of the global wearable technologies market is projected to reach $61.30 billion by 2030 which reflects a shift towards personalised health monitoring embedded in everyday activities. This paper explores the potential of embroidered interdigitated transducer (IDT)-based sensors for non-invasive, continuous monitoring of human biomarkers, particularly glucose levels, in human sweat. The study employs innovative embroidery techniques to create flexible fabric-based sensors with gold-coated IDTs. Experimental results demonstrate the sensor’s capability to detect water droplets and glucose concentrations, showcasing promising applications for health monitoring. The embroidered IDT-based sensors offer a unique approach for seamless integration into clothing, paving the way for personalised, continuous health data capture.

The increasing demand for higher power in rocket launchers is driving the development of higher power nozzles, achieved essentially by increasing the expansion ratio. However, this can lead to flow separation and resulting unsteady, asymmetrical forces, called side loads, which can limit the life of both the nozzle itself and other engine components. If this problem can be overcome, engine performance can be significantly increased. Therefore, various separation control methods have been proposed, but none have been fully implemented to date due to the uncertainties associated with modeling and predicting the flow phenomena. A numerical study of high area ratio rocket engine is presented to analyze the aeroelastic performance of the structure under the condition of flow separation. The finite volume method, the 2nd order upwind scheme and the extended wall function are used to analyze the flow inside the rocket nozzle and the transition of the Free Shock Separation (FSS) to Restricted Shock Separation (RSS) separation pattern ) to be discussed in detail. Since the location of the flow separation point depends heavily on the flow.

Induction heating is a fast, reproducible, and efficient heating method used in various manufacturing processes. However, there is no established additive manufacturing (AM) process based on induction heating using wire as feedstock. This study investigates a novel approach to AM based on inductive heating, where a steel wire is melted and droplets are detached periodically using a two-winding induction coil. The process parameters and energy input into the droplets are characterized. The induction generator exhibits sluggish response to the excitation voltage, resulting in a lag in the coil current. The process is captured using a high-speed camera, revealing regular droplet formation and uniform size and shape when operated within an appropriate process window. Larger drops and increased spatter formation occur outside this window. The proposed method allows for the production of droplets with almost spherical shapes. Further analysis and characterization of droplet formation and energy input provide insights into process optimization and overall efficiency.

Soil and Water Assessment Tool (SWAT) was used to simulate five glacierized river basins that are global in coverage and vary in climate. The river basins included the Narayani (Nepal), Vakhsh (Central Asia), Rhone (Switzerland), Mendoza (Central Andes, Argentina), and Central Dry Andes (Chile) with a total area of 85,000 km2. A modified SWAT snow algorithm was applied in order to consider spatial variation of associated snow melt /accumulation by elevation band across each subbasin. In the previous studies, melt rates varied as a function of elevation resulting from an air temperature gradient while the snow parameters were constant throughout the entire basin. A major improvement of the new snow algorithm is separating the glaciers from seasonal snow based on their characteristics. Two SWAT snow algorithms were evaluated in simulation of monthly runoff from glaciered watershed: 1) the snow parameters are lumped (i.e. constant throughout the entire basin) and 2) the snow parameters are spatially variable based on elevation band-subbasin (i.e. modified snow algorithm). Applying the distributed SWAT snow algorithm improved the model performance in simulation of monthly runoff with snow-glacial regime, so that mean RSR decreased to 0.49 from 0.55 and NSE increased to 0.75 from 0.69. Improvement of model performance was negligible in simulation of monthly runoff from the basins with monsoon runoff regime.

In this study, a basic study was performed to analyze the seasonal temperature status of a research room in the Global Environment Research Building where ceiling-embedded indoor units are installed to study the room temperature status of the building as well as to improve its thermal environment. In addition, a direction for improvement of the indoor thermal environment in the winter was proposed through a CFD (computational fluid dynamics) simulation and was proven by an additional experiment. Through the results of this study, it appeared that if the ceiling-embedded indoor unit was installed in the small indoor space without considering the thermal vulnerability of its perimeter boundary, the air temperature of the upper part was greatly different from that of the bottom part in the winter. Hence, in this study, as a means to improve it, convectors were installed to minimize the effect of the external thermal environment and angle-controllable air flowing fans were installed to mitigate the stratification distribution. With such result, it was intended to present the essential data for improvement of the thermal environment as well as conservation of heating energy in the winter for buildings by reviewing the use of the ceiling-embedded indoor unit in the future.

Synaptopodin 2-like protein (SYNPO2L) is localized in the sarcomere of cardiomyocytes and is involved in heart morphogenesis. However, the molecular function of SYNPO2L in the heart is not fully understood. We investigated the interaction of SYNPO2L with sarcomeric alpha-actinin and actin filaments in cultured mouse cardiomyocytes. Immunofluorescence studies showed that SYNPO2L colocalized with alpha-actinin and actin filaments at the Z-discs of the sarcomere. Recombinant SYNPO2La or SYNPO2Lb caused bundling of actin filaments in the absence of alpha-actinin and enhanced alpha-actinin-dependent formation of actin bundles. In addition, high-speed atomic force microscopy revealed that SYNPO2La directly bound to alpha-actinin via its globular ends. The interaction between alpha-actinin and SYNPO2La stabilized the two proteins on actin filaments. These results strongly suggest that SYNPO2L cooperates with alpha-actinin during actin bundle formation to facilitate sarcomere formation and maintenance.

Background: Over the past two decades, the Copenhagen Psychosocial Questionnaire (COPSOQ) has been established as a valid instrument to measure psychosocial stress at work. Currently, the COPSOQ international network is responsible for monitoring and improving COPSOQ. In 2019, a new questionnaire was published, and the Greek version is now being validated. Methods: The measurement qualities of the Greek COPSOQ III have been explored in accordance with the usual requirements of a validation study as defined by DIN EN ISO 10075-3. A sample of observations from 2,189 participants surveyed with the COPSOQ in Greece was used to validate the current version with appropriate statistical analyses. Exploratory factor analysis was used to assess the statistical relationships for many scales. Results: With its 108 items and 40 scales, the Greek COPSOQ III includes all psychosocial work factors that have been internationally validated and remain compatible with approximately 72% of the COPSOQ III German version content. In addition to the primary results, congruence with widely used theoretical approaches such as the demand-control (−support) model (DCM) or the job demands-resources model (JDR) is generally satisfactory. In summary, our validation study for the Greek COPSOQ III version showed adequate reliability and validity, which is in line with the findings of the COPSOQ III questionnaire from other European countries, and it is also compatible with the validation of the German COPSOQ III (Lincke, 2021). Our regression analysis revealed that 34 psychosocial work factors (34 “context” scales) could adequately predict the scores of the satisfactory and health scales (6 “outcome” scales). The analysis also revealed the top five (5) predictors (context variables) for each of the six (6) “effect” scales (outcome variables). Conclusions: With the launch of COPSOQ III in Greece, current and new workplace psychosocial aspects could be explored. COPSOQ III (GR) is a beneficial instrument for enterprise research and risk assessment.

Background: Aging impacts semantic abilities, therefore semantic exercises might be a valuable tool for cognitive stimulation in healthy older adults. However, programs in this area are limited. Methods: In the current paper, we adapted the BOX linguistic semantic program for online use via the CerUp platform. Our goal was to prepare the optimization future experience of CerUp users, including potential changes in task structure, level of difficulty and instructions, and participants’ adherence, engagement, performance accuracy, perceived difficulty, and adequacy ratings assigned to the instructions we provided that might guide future changes. Additionally, we created a map of hypothesized task-specific features that may guide tailor-made uses of the program. Results: To that end, we present preliminary results on the usability of CerUp. By analyzing task-specific usability data alongside the hypothesized map of task-specific features, we also tested the validity of the latter. Conclusions: These preliminary results on the usability of the CerUp platform highlight the potential of programs for semantic stimulation. Such findings can inform future optimizations of the program, tailored to users’ needs and effectively supporting overall language skills and general cognition.

This paper provides a first-time ever unification of information theory, semi-group theory with the theory of uncertain reasoning, through functional perspective. Fundamentally, the threshold theorems for the inference functional were devised. Furthermore, numerical experiments are illustrated. The complex proofs in our paper are original results which emphasizes the credibility of the class of Rényi Generalized Entropies as measures of information, and that the field is open to extend an enhanced methodology regarding queuing networks with heavy tails.

The paper analyzes the probability distribution of the occupancy numbers and the entropy of a system at the equilibrium composed by an arbitrary number of non-interacting bosons. The probability distribution is derived both by tracing out the environment from a bosonic eigenstate of the union of environment and system of interest (the empirical approach) and by tracing out the environment from the mixed state of the union of environment and system of interest (the Bayesian approach). In the thermodynamic limit, the two coincide and are equal to the multinomial distribution. Furthermore, the paper proposes to identify the physical entropy of the bosonic system with the Shannon entropy of the occupancy numbers, fixing certain contradictions that arise in the classical analysis of thermodynamic entropy. Finally, by leveraging an information-theoretic inequality between the entropy of the multinomial distribution and the entropy of the multivariate hypergeometric distribution, Bayesianism of information theory and empiricism of statistical mechanics are integrated into a common ''infomechanical'' framework.

The objectives of this article are to examine whether the application of digitalization in enterprise budgeting affects the fulfilment of budgeting functions and their qualitative characteristics, and to investigate whether the realization of budgeting functions brings benefits. The research was conducted from the perspective of contingency theory, both internal and external conditions. The study used an online survey targeting company budgeting staff. Structural Equation Modelling was utilized to validate the structural model (PLS-SEM technique). The conclusions confirm that the application of digitalization, which particularly pertains to ERP, Business Intelligence systems and accounting systems with budgeting solutions, positively impacts the fulfilment of budgeting functions and most of their qualitative characteristics, while Blockchain makes a minor contribution. Our research suggests that contingency variables, particularly internal ones such as company size, the quality of the organizational structure, and support for competitive strategy, continue to affect budgeting implementation even with the participation of digitalization in budgeting. Budgeting functions and their qualitative aspects are notably correlated with the advantages derived from budgeting, including financial performance, sustainable development and budget task execution.

We discuss the issues with tentative generalisations of the process matrix formalism from finite-dimensional mechanical systems all the way to quantum field theory. We present a detailed overview of possible open problems that arise when one attempts to move from particle ontology into the realm of field ontology, i.e., when one transitions from mechanics to field theory framework. These issues need to be addressed, and problems solved, if one aims to expand the scope of applicability of the process matrix formalism, and therefore its usefulness. This is far from a trivial and straightforward endeavour, but rather a task for a whole future research programme.

In this paper, we have constructed a WKB approximation for graphene having a Y-shaped Kekul\'e lattice distortion and a special folding of the $\mbox{\boldmath$K$}$ and $\mbox{\boldmath$K$}^\prime$ valleys, which leads to very specific linear energy dispersions with two non-equivalent pairs of subbands. These obtained semi-classical results, which include the action, electron momentum and wave functions, are utilized to analyze the dynamics of electron tunneling through non-square potential barriers. In particular, we explore resonant scattering of an electron by a potential barrier built on Kekul\'e-distorted graphene. Mathematically, a group of consecutive equations for a semi-classical action have been solved by following a perturbation approach under the condition of small strain-induced coupling parameter $\Delta_0 \ll 1$ (a good fit to its actual value $\Delta_0 \backsim 0.1$). Specifically, we consider a generalized model for Kek-Y graphene with two arbitrary Fermi velocities. The dependence of the electron transmission amplitude on the potential profile $V(x)$ and band parameters of Kekul\'e-patterned graphene has been explored and analyzed in details.

The purpose of this research is to determine if the tradeoffs that Kissick proposed among cost containment, quality, and access remain as rigidly interconnected as originally conceived in the contemporary health care context. Although many have relied on the Kissick model to advocate for health policy decisions, to our knowledge, the model has never been empirically tested. Some have called for policy makers to come to terms with the premise of the Kissick model tradeoffs, others have questioned the model given the proliferation of quality enhancing initiatives, automation, and information technology in the health care industry. One wonders whether these evolutionary changes alter or disrupt the originality of the Kissick paradigms themselves. Methods: Structural Equation Modeling (SEM) was used to evaluate the Kissick hypothetical relationships among the unobserved constructs of cost, quality, and access in hospitals for the year 2018. Hospital data were obtained from Definitive Healthcare a subscription site which contains Medicare data as well as non-Medicare data for networks, hospitals, and clinics (final n= 2,766). Results: Reporting significant net effects as defined by our chosen study variables, we find that as quality increases costs increase, as access increases quality increases, and as access increases, costs increase. Policy and Practice Implications: Our findings lend continued relevance to a balanced approach to health care policy reform efforts. Simultaneously bending the health care cost curve, increasing access to care, and advancing quality of care is as challenging now as it was when the Kissick model was originally conceived.

In this paper, we considered two phenomena in acoustically excited aqueous solutions of a strong electrolyte. These are the well-known Debye ionic vibrational potential (IVP), and radiofrequency anisotropy we discovered earlier , apparently, for the first time. Since both occur due to the accelerated motion of the solution, we have tried to combine them in one simple model. We have established that for a polarized UHF radio wave passed through a NaCl aqueous solution excited by an acoustic pulse the rotation angle of its vector E is proportional to the integral of the square of the observing IVP over time. An equivalent electrical circuit simulating the observed phenomena has been proposed and tested for physical feasibility. Several arguments are given in favour of the fluid-gyroscopic mechanism of RF anisotropy-related effects. We also found out that the IVP is practically independent of the vibrational velocity for frequencies below 10 kHz and it tends to zero at zero frequency. The latter is consistent with the law of conservation of energy but contradicts the incomplete existing theory.

Abstract: Discussed is an expansion of SRT to fourth-order of space-time including any velocities and possible negative kinetic energies by real rest mass in analogy of the model amplitude of enforced damped oscillation. This paper serves an introduction to developed SRT including superluminar translating velocities without tachyons or imaginary restmass. Focused is on those parts, which develop the elaborated lorentz-transformations. Discussed are the problems of this theory.

Deep learning machines are computational models composed of multiple processing layers of adaptive weights to learn representations of data with multiple levels of abstraction. Their structure is mainly reflecting the intuitive plausibility of decomposing a problem into multiple levels of computation and representation since it is believed that higher layers of representation allow a system to learn complex functions. Surprisingly, after decades of research, from learning and design perspectives these models are still deployed in a heuristic manner. In this paper, deep learning feed-forward machines are modeled from a statistical mechanics point of view as disordered physical systems where its macroscopic behavior is determined in terms of the interactions defined between the basic constituent of these models, namely, the artificial neuron. They are viewed as the equilibrium states of a theoretical body that is subject to the law of increase of the entropy. The study of the changes in energy of the body when passing from one equilibrium state to another is used to understand the structure and role of the phase space of the system, the stability of the equilibrium states, and the resulting degree of disorder. It is shown that the topology of these models is strongly linked to their stability and resulting level of disorder. Furthermore, the proposed theoretical characterization permit to assess the thermodynamic efficiency with which information can be processed by these models, and to provide a practical methodology to quantitatively estimate and compare their expected learning and generalization capabilities. These theoretical results provides new insights to the theory of deep learning and their implications are shown to be consistent through a set of benchmarks designed to experimentally assess their validity.

In this work, we study the dynamical behaviors of the electromagnetic fields and material responses in the hyperbolic metamaterial consisting of periodically arranged metallic and dielectric layers. The thickness of each unit cell is assumed to be much smaller than the wavelength of the electromagnetic waves, so the effective medium concept can be applied. When electromagnetic (EM) fields are present, the responses of the medium in the directions parallel to and perpendicular to the layers are like that of Drude and Lorentz media, respectively. We derive the energy density of the EM fields and the power loss in the effective medium based on Poynting theorem and the dynamical equations of the polarization field. We also show that the Lagrangian density of the system can be constructed. The Euler-Lagrangian equations yield the correct dynamical equations of the electromagnetic fields and the polarization field in the medium. The canonical momentum conjugates to every dynamical field can be derived from the Lagrangian density via differentiation or variation with respect to that field. We apply Legendre transformation to this system, and find that the resultant Hamitonian density is identical to the energy density, up to an irrelevant divergence term.

We provide an axiomatic base for the set of natural numbers, that has been proposed as a canonical construction, and use this definition of $\mathbb N$ to find several results on finite group theory. Every finite group $G$, is well represented with a natural number $N_G$; if $N_G=N_H$ then $H,G$ are in the same isomorphism class. We have a linear order, on the quotient space of isomorphism classes of finite groups, that is well behaved with respect to cardinality. If $H,G$ are two finite groups such that $|H|=m<n=|G|$, then $H<\mathbb Z_n\leq G\leq\mathbb Z_{p_1}^{n_1}\oplus\mathbb Z_{p_2}^{n_2}\oplus\cdots\oplus\mathbb Z_{p_k}^{n_k}$ where $n=p_1^{n_1}p_2^{n_2}\cdots p_{k}^{n_k}$ is the prime factorization of $n$. We find a canonical order for the objects of $G$ and define equivalent objects of $G$, thus finding the automorphisms of $G$. The Cayley table of $G$ takes canonical block form, and we are provided with a minimal set of independent equations that define the group. We show how to find all groups of order $n$, and order them. We give examples using all groups with order smaller than $10$, and we find the canonical block form of the symmetry group $\Delta_4$. In the next section, we extend our results to the infinite case, which defines a real number as an infinite set of natural numbers. A real function is a set of real numbers, and a sequence of real functions $f_1,f_2,\ldots$ is well represented by a set of real numbers, as well. In general, we represent mathematical objects using the smallest possible data-type. In the last section, mathematical objects are well assigned to tree structures. We conclude with brief comments on type theory and future work on computational aspects of these representations.

Due to the periodic movement of the piston in the reciprocating pump, the fluid will cause pressure pulsation, and the vibration of the pipeline will lead to instrument distortion, pipe failure and equipment damage. Therefore, it is necessary to study the vibration phenomena of the reciprocating pump pipeline based on the pressure pulsation theory. This paper starts from the reciprocating pump pipe pressure pulsation caused by fluid, pressure pulsation in the pipeline and the excited force is calculated under the action of the reciprocating pump. Then, the numerical simulation model is established based on the pipe beam model, and the rationality of the numerical simulation method is verified by the indoor experiment. Finally, a case study is taken as an example to analyze the vibration law of the pipeline system, and proposed the stress reduction and vibration reduction measures. The main conclusions are drawn from the analysis: (1) Excited force is produced in the bend or tee joint, and it can also influence the straight pipe in different levels; (2) In this pipeline system, the pump discharge pipe has a larger vibration amplitude and lower natural frequency; (3) The vibration amplitude increases with the pipe thermal stress, and when the oil temperature is higher than 85°C, it had a greater influence on the vertical vibration amplitude of the pipe.