：An effective technology for predicting the end-point phosphorous content of basic oxygen furnace (BOF) can provide theoretical instruction to improve the quality of steel via controlling the hardness and toughness. Given the slightly inadequate prediction accuracy in the existing prediction model, a novel hybrid method was suggested to more accurately predict the end-point phosphorus content by integrating an enhanced sparrow search algorithm (ESSA) and a multi-strategy with a deep extreme learning machine (DELM) as ESSA-DELM in this study. To begin with, the input weights and hidden biases of DELM were randomly selected, resulting in that DELM inevitably had a set of non-optimal or unnecessary weights and biases. Therefore, the ESSA was used to optimize the DELM in this work. For the ESSA, the Trigonometric substitution mechanism and Cauchy mutation were introduced to avoid trapping in local optima and improve the global exploration capacity in SSA. Finally, to evaluate the prediction efficiency of ESSSA-DELM, the proposed model was tested on process data of the converter from the Baogang steel plant. The efficacy of ESSA-DELM was more superior to that of other DELM-based hybrid prediction models and conventional models. The result demonstrated that the hit rate of end-point phosphorus content within ±0.003%, ±0.002%, and ±0.001% was 91.67%, 83.33%, and 63.55%, respectively. The proposed ESSA-DELM model could possess better prediction accuracy compared with other models, which could guide field operations.

Despite increased awareness of and attention to the need for sustainable agriculture, fertilizers and compost application in excess of crop requirements remain common agricultural practices in South Korea, causing eutrophication of freshwater and coastal ecosystems. In this study, a phosphorus (P) budget was developed to quantify P inputs, outputs, and retention in a forested- agricultural watershed. The P budget showed that chemical fertilizers and organic compost were the largest source of P (97.6% of the total) followed by atmospheric deposition (2.1% of the total P), whereas forest export (0.2% of the total) and sewage treatment plants (STPs) (0.1% of the total) were negligible. The dominant P outputs were crop harvesting and hydrologic export to surface water. The P balance showed a significant accumulation of P in the watershed; approximately 87% of the total P input was retained in the soils within the watershed. However, P concentrations in drainage water were still high enough to cause eutrophication of downstream reservoirs. The results provide useful information on the proportion of P export and retention in soils and will help support efforts to improve water quality and design better management of agricultural non-point source pollution.

Filamentous fungi such as Aspergillus spp. are opportunistic pathogens, which cause highly invasive infections, especially in immunocompromised individuals. Control of such fungal pathogens is increasingly problematic due to the small number of effective drugs available for treatment. Moreover, the increased incidence of fungal resistance to antifungal agents makes this problem a global human health issue. The cell wall integrity system of fungi is the target of antimycotic drugs echinocandins, such as caspofungin (CAS). However, echinocandins cannot completely inhibit the growth of filamentous fungal pathogens, which results in survival/escape of fungi during treatment. Chemosensitization was developed as an alternative intervention strategy, where co-application of CAS with the intervention catalyst octyl gallate (OG; chemosensitizer) greatly enhanced CAS efficacy, thus achieved ≥ 99.9% elimination of filamentous fungi in vitro. Based on hypersensitive responses of Aspergillus antioxidant mutants to OG, it is hypothesized that, besides destabilizing cell wall integrity, the redox-active characteristic of OG may further debilitate fungal antioxidant system.

This work assesses sea-level rise using three different models created on Free and Open-Source Software for Geographic Information System (FOSS4GIS). Based on regional projections of Special Report on Climate Change and Oceans and Cryosphere (SROCC) of the Intergovernmental Panel on Climate Change (IPCC), the models were applied to a case of study on Rio Grande do Sul coast – Brazil under different sea-level rise scenarios by the end of this century. The End Point Rate for QGIS (EPR4Q), calculates a shoreline projection using End Point Rate method. The Uncertainty Bathtub Model (uBTM), analyses the sea-level rise impact by the uncertainty of sea-level projec-tions and vertical error of the Digital Elevation/Terrain Model (DEM/DTM). The Bruun Rule for Google Earth Engine Model (BRGM) predicts the shoreline position with sea-level rise, using topographic and bathymetric data from Unmanned Aerial Vehicles (UAV) and Coastal Modelling System (SMC – Brazil), respectively. The results indicated a maximum shoreline retreat for 2100 of -502 m and -1727 m using EPR4Q and BRGM, correspondingly. The uBTM using the land-use of Mapbiomas showed a maximum of 44.57 km2 of urban area impacted by the sea-level flood. This research highlights the possibility of performing coastal management analysis in GIS environ-ment using non-commercial software.

As an emerging building solution, cross-laminated timber (CLT) floors have been increasingly used in mass timber construction. The current vibration design of CLT floors is conservative due to the assumption of simple support conditions in the floor-to-wall connections. It is noted that end fixity occurs as a result of clamping action at the ends, arising from the gravity load applied by the structure above the floor and by the mechanical fasteners. In this paper, the semi-rigid floor-to-wall connections are treated as elastically restrained edges against rotations to account for the effect of partial constraint. A rotational end-fixity factor was first defined to reflect the relative bending stiffness between CLT floors and elastic restraints at the edges. Then, for the design of vibration serviceability of CLT floors as per the Canadian Standard for Engineering Design in Wood (CSA O86), restraint coefficients were defined and their analytical expressions were derived for natural frequencies and the mid-span deflection under a concentrated load, respectively. In particular, a simplified formula of the restraint coefficient for the fundamental frequency was developed to assist engineers in practical design. At last, by comparing with reported experimental data, the proposed design formula showed excellent agreement with test results. In the end, the proposed end fixity factor with their corresponding restraint coefficients is recommended as an effective mechanics-based approach to account for the effect of end support conditions of CLT floors.

Aiming at the problem of model error and tracking dependence in the process of intelligent vehicle motion planning, an intelligent vehicle model transfer trajectory planning method based on deep reinforcement learning is proposed, which obtain an effective control action sequence directly. Firstly, an abstract model of the real environment is extracted. On this basis, Deep Deterministic Policy Gradient (DDPG) and vehicle dynamic model are adopted to jointly train a reinforcement learning model, and to decide the optimal intelligent driving maneuver. Secondly, the actual scene is transferred to equivalent virtual abstract scene by transfer model, furthermore, the control action and trajectory sequences are calculated according to trained deep reinforcement learning model. Thirdly, the optimal trajectory sequence is selected according to evaluation function in the real environment. Finally, the results demonstrate that the proposed method can deal with the problem of intelligent vehicle trajectory planning for continuous input and continuous output. The model transfer method improves the model generalization performance. Compared with the traditional trajectory planning, the proposed method output continuous rotation angle control sequence, meanwhile, the lateral control error is also reduced.

The phenomenon of seat occupancy in university libraries is a prevalent issue. However, existing solutions, such as software-based seat reservations and sensors-based occupancy detection, have proven to be inadequate in effectively addressing this problem. In this study, we propose a novel approach: a serial dual-channel object detection model based on Faster RCNN. Furthermore, we develop a user-friendly web interface and mobile APP to create a computer vision-based platform for library seat occupancy detection. To construct our dataset, we combine real-world data collection with UE5 virtual reality. The results of our tests also demonstrate that the utilization of personalized virtual dataset significantly enhances the performance of the convolutional neural network (CNN) in dedicated scenarios. The serial dual-channel detection model comprises three essential steps. Firstly, we employ Faster RCNN algorithm to determine whether a seat is occupied by an individual. Subsequently, we utilize an object classification algorithm based on transfer learning, to classify and identify images of unoccupied seats. This eliminates the need for manual judgment regarding whether a person is suspected of occupying a seat. Lastly, the web interface and APP provide seat information to librarians and students respectively, enabling comprehensive services. By leveraging deep learning methodologies, this research effectively addresses the issue of seat occupancy in library systems. It significantly enhances the accuracy of seat occupancy recognition, reduces the computational resources required for training CNNs, and greatly improves the efficiency of library seat management.

Tanzania like any other developing countries is depending on uncertain rainfall for their subsistence and commercial agriculture. In this paper the start and end of rains for Mbeya Meteorological Station in Tanzania Mainland was analyzed and critically examined. Data were kindly given by Tanzania Meteorological Agency (TMA) during face to face Statistics in Applied Climatology (fSIAC) workshop which was held at Sokoine University of Agriculture (SUA) the year 2013 prior to online Course on Statistics in Applied climatology (eSIAC). In this study data were analyzed using Instat (for windows version 3.3.7) package developed by the Statistical Services Centre of the University of Reading. The analysis showed that much of rains start early December all the way to May. There is also 50 percent chance of having below and above the mean for both total annual rainfall and number of rains, in other word one in two years the total rains are below means likewise the number of rain days.

With the advancement of photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and was used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain a final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration.

Autonomous driving navigation relies on diverse approaches, each with advantages and limitations depending on various factors. For HD maps, modular systems excel, while end-to-end methods dominate mapless scenarios. However, few leverage the strengths of both. This paper innovates by proposing a hybrid architecture that seamlessly integrates modular perception and control modules with data-driven path planning. This innovative design leverages the strengths of both approaches, enabling a clear understanding and debugging of individual components while simultaneously harnessing the learning power of end-to-end approaches. Our proposed architecture achieved 1st and 2nd place in the 2023 CARLA Autonomous Driving Challenge’s SENSORS and MAP tracks, respectively. These results demonstrate the architecture’s effectiveness in both map-based and mapless navigation.

Speech recognition, or automatic speech recognition (ASR), is a technology designed to convert spoken language into text using software. However, conventional ASR methods involve several distinct components, including language, acoustic, and pronunciation models with dictionaries. This modular approach can be time-consuming and may influence performance. In this study, we propose a method that streamlines the speech recognition process by incorporating a unified recurrent neural network (RNN) architecture. Our architecture integrates a convolutional neural network (CNN) with an RNN and employs a connectionist temporal classification (CTC) loss function. Key experiments were carried out using a dataset comprising 576,656 valid sentences, using erosion techniques. Evaluation of the model performance, measured by the word error rate (WER) metric, demonstrated remarkable results, achieving a WER of 2%. This approach has significant implications for the realm of speech recognition, as it alleviates the need for labor-intensive dictionary creation, enhancing the efficiency and accuracy of ASR systems, and making them more applicable to real-world scenarios. For future enhancements, we recommend the inclusion of dialectal and spontaneous data in the dataset to broaden the model's adaptability. Additionally, fine-tuning the model for specific tasks can optimize its performance for targeted objectives or domains, further enhancing its effectiveness in those areas.

Biotechnological potential of Thermoanaerobacter strain AK15 to produce ethanol and valuable alcohols from carbohydrates and amino acids were evaluated in present study. The strain is highly ethanologenic, producing a maximum of 1.57 mol ethanol per mol of glucose degraded at high liquid-gas phase ratios. The strain degrades most of the sugars tested as well as starch and pretreated hydrolysates of biopolymers (Whatman paper, newspaper, Timothy grass, Rhubarb leaves, and several macroalgae species) to ethanol mainly. The strains degraded serine and threonine when used as a single substrate, producing mainly acetate and ethanol as end products, and the branched-chain amino acids (leucine, isoleucine, valine) when cultivated in the presence of thiosulfate. The main end products from branched-chain amino acids were a mixture of their corresponding branched-chain fatty acids and alcohols. Finally, the strain was also shown to use butyrate as an electron sink during glucose degradation resulting in the reduced product butanol in addition to end products produced from glucose. Thus, strain AK15 is a promising candidate for ethanol and fine chemical production from a range of lignocellulosic and algal biomass.

Abstract: Like other plants, every PV power plant will one day reach the end of its service life. Calculations show that 20,400 tons of PV waste will be generated worldwide by 2030 and 60.2 million tons by 2050, not including the mass of the support structure. Such large amounts of waste pose a potential threat to the environment and people if not properly managed. The paper analysed the options for dealing with PV waste, namely reuse, recycling and landfilling. For recycling as the best option in terms of environmental protection and circular economy, an overview of recycling technologies and the percentage of achievable recycling for the materials contained in each PV system component is given. In addition, the current situation of legislation and recycling of PV modules in Europe was examined with special reference to the Balkan countries. There are a small number of factories for recycling PV modules in Europe, but none in the Balkan countries. The main reason for this is the small amount of PV waste in these countries and consequently the economic unprofitability. For this reason, PV modules (after dismantling the aluminum frame and cables) are mostly disposed of as non-hazardous waste in landfills in these countries. Finally, the main barriers to faster implementation of PV module reuse and recycling are listed, along with guidelines for their removal. The cost of recycling Si modules is about ten times higher than the cost of disposal. To change this ratio in favor of recycling, cheaper recycling methods need to be developed and taxes on landfill disposal need to be increased.

Doppler findings of persistent reverse end-diastolic flow (PREDF) in a fetal middle cerebral artery (MCA) are a very rare sonographic finding and are a marker of poor fetal condition. This finding often leads to intrauterine fetal death or early neonatal death. The reverse end-diastolic flow in the middle cerebral artery is the terminal hemodynamic event. Fetal cerebral circulation normally has a high impedance; in the event of fetal hypoxemia, impedance decreases, resulting in the central redistribution of blood flow to vital organs, which maintains the oxygen delivery to the brain. Reverse flow in the middle cerebral arteries describes the loss of this autoregulatory process. PREDF is a sequence happening due to increased extracranial or intracranial pressure. Previous case reports mentioned intracranial hemorrhage, intrauterine growth restriction (IUGR), fetal anemia, and fetal hepatic abnormalities as leading problems to PREDF. This condition presumably arises due to cerebral edema associated with severe hypoxemia. We reported Doppler findings of PREDF MCA in a 33-year-old female at 30 weeks gestation who was referred to the hospital with severe preeclampsia accompanied by IUGR and oligohydramnios. A cesarean section was performed due to severe preeclampsia and a low bishop score. Hypotheses on various etiologies and their association with intrauterine/neonatal death as well as the best management still require further investigation.

Utility perceived by individuals is believed to be different from the utility experienced by that individual. System dynamicists implicitly categorize this phenomenon as a form of bounded rationality and traditionally employ a simple smoothing function to capture it. We challenge this generalization by testing it against an alternative formulation of utility perception that is suggested by modern theories of behavioral economics. In particular, the traditional smoothing formulation is compared with the peak-end rule in a simple theoretical model as well as in a medium-size model of electronic health record implementation. Experimentation with the models reveals that the way utility perception is formulated is important and might affect behavior and policy implications of system dynamics models.

IgA nephropathy (IgAN) is the most common primary glomerulonephritis worldwide. IgAN causes end-stage kidney disease (ESKD) in 20–40% of all cases. The activation of the complement system by pathological circulating IgAs, which is often associated with low serum C3 levels (LowC3), seems to play a crucial role. Previous studies have shown an association between histological evidence of TMA, which is the result of alternative complement activation, and poor outcome. However, it is not known to what extent the decrease in serum C3 levels reflects ongoing TMA injury. Our study aimed at assessing the association of LowC3 and ESKD and whether this association reflects ongoing TMA. We enrolled all patients with biopsy-proven IgAN and followed-up patients until last visit, ESKD or death. Of the 56 patients included in the study, 12 (21%) presented low serum C3 (LowC3) at the time of renal biopsy. TMA was significantly more frequent in the LowC3 group [7/12 (58%) vs 9/44 (20%), p=0.02]. After adjusting for potential confounders, LowC3 was strongly associated with an increased hazard of ESKD (hazard ratio [HR]: 5.84 [95%CI: 1.69,20.15; P=0.005). The association was not affected by adjusting for TMA. The estimated overall proportion of the relation between C3 and ESKD mediated by TMA was low and not statistically significant. Our study provides evidence that C3 hypocomplementemia is associated with an increased risk of ESKD with mechanisms that are largely independent from TMA.

To study the coupling mechanism and dynamic response of the end-face movable gear trans-mission system under complex excitation, a specific configuration of end-meshing movable gear reduction mechanism was used to achieve predetermined rigid thrust transmission and mis-matched gear meshing functions, which solved the inherent defects of traditional harmonic gear mechanism thin-wall flexible wheel easy to be damaged by fatigue. Considering the phenomenon of elastic deformation of live teeth accompanied by significant changes in meshing characteristics in the transmission process of end-meshing harmonic reducer, the influence of dynamic meshing parameters, live tooth deformation, time-varying stiffness of tooth meshing, and time-varying backlash on nonlinear dynamic performance was explored, as well as the mechanism of mul-ti-parameter coupling on transmission performance. The nonlinear dynamics model of the end-meshing harmonic reducer is established to solve the chattering prediction problem. Finally, a comprehensive test bed for the transmission system of harmonic reducer with meshing type with adjustable characteristic end was built to verify the correctness of the theoretical model and provide the theoretical and technical basis for exploring the optimal parameter selection of pas-sive vibration suppression problem.

The recycling of wastes to obtain value-added products is a great challenge in the sustainable economy. In our studies, a mixed precursor containing aluminium scrap from end – of – life vehicles and TiO2 Degussa P25 was used to prepare TiO2 modified Al photocatalyst through solid state reaction. Al scrap and TiO2 Degussa P25 in the ration of n(Ti) / n(Al) = x (x = 0.0 - 2.5%), and acetylacetonate and Tween 80 was mixed under magnetic stirring and sintered using a con-ventional furnance at 550°C. DTA – TG – DTG, X – ray diffraction, Fourier – transform infrared, UV – Vis and photoluminescence spectroscopies were assed to investigate the thermal, structural, optical properties and photocatalytic activity. The effect of aluminium concentration on the characteristics of TiO2 Degussa P25 depending on the above instruments are studied. XRD revealed that the materials have crystal structure of anatase TiO2, rutile TiO2 and aluminium. The opto-electronic properties show that Al addition shifted the absorption spectra toward the visible range. The photocatalytic performance of Al – TiO2 composites was quantified by the degradation of Allura Red solution under a visible condition. The highest photocatalytic activity (79.99%) was achieved for the sample containing 0.5% Al and a higher concentration of Al particles shields the active centres of the semiconductor, thus reducing its photocatalytic activity. Aluminium scrap from end-of-life vehicles can be a source of raw material to be considered in semiconductor production. The spin-trapping technique evidenced that •O2- was the main species generated responsible for the Allura Red degradation.

Background: The UNHLM pledged to enroll 30 million in TB preventive treatment (TPT) by 2022, necessitating TPT expansion to all at TB risk. We assessed uptake and completion of a 12-dose, weekly isoniazid-rifapentine (3HP) TPT regimen. Methods: Between February 2018 - March 2019 in Dhaka, community-based TPT using 3HP targeted household contacts of 883 confirmed drug-sensitive pulmonary TB patients. Adhering to WHO guidelines, contacts underwent active TB screening before TPT initiation. Results: Of 3,193 contacts who advised health facility visits for screening, 67% (n=2,149) complied. Among these, 1,804 (84%) received chest X-rays. Active TB was diagnosed in 39 (2%) contacts; they commenced TB treatment. Over 97% of 1,216 contacts began 3HP TPT, with completion rates higher among females, those with more education and income, non-slum residents, and those without 3HP-related adverse events. Adverse events, mainly mild, occurred in 5% of participants. Conclusions: The 3HP weekly regimen, with its short duration, self-administered option, and minimal side effects, achieved satisfactory completion rates. A community-focused 3HP TPT approach is feasible, scalable nationally, and aligns with UNHLM targets.

Nowadays, there are implemented devices whose purpose is to perform massive computations by saving resources at the time they reduce the latency of arithmetic operations. These devices are usually GPUs, FPGAs and other specialised devices such as "Coral". Neural networks, digital filters and numerical simulators take advantage of the massively parallel operations of such devices. One way to reduce the amount of resources used is to limit the size of the registers that store data. This has led to the proliferation of numeric formats with a length of less than 32 bits, known as short floating point or SFP. We have developed several SFP’s for use in our neural network accelerator design, allowing for different levels of accuracy. We use a 16-bit format for data transfer and different formats can be used simultaneously for internal operations. The internal operations can be performed in 16-bit, 20-bit and 24-bit. The use of registers larger than 16-bit allows the preservation of fractional information while increasing precision. By leveraging some of the FPGA’s arithmetic resources, our design outperforms designs implemented from scratch and is competitive with specialized arithmetic circuits already implemented in the FPGA.

This study considers the change-point test problem for time series based on the self-normalization ratio statistic test, which is constructed using residuals obtained from a support vector regression (SVR)-autoregressive moving average (ARMA) model. Under the null hypothesis, the series is a stationary process, and our test statistic converges to a non-degenerate distribution. Under the alternative hypothesis, there are change-points in the time series, and the self-normalization test statistic diverges to infinity. The simulations show that our proposed new test has better finite sample performance than other SVR-based tests in the literature. Finally, we illustrate its usefulness by analyzing two actual data sets.

Speech recognition, also known as automatic speech recognition (ASR), is a technology that enables software to transcribe spoken language into text. However, existing Amharic ASR methods require multiple separate blocks, such as language, acoustic, and pronunciation models with dictionaries, which can be time-consuming and influence performance. This study proposes an approach that replaces much of the speech pipeline with a single recurrent neural network (RNN) architecture. Our proposed architecture is based on a hybrid approach that combines a convolutional neural network (CNN) with a recurrent neural network (RNN) and a connectionist temporal classification (CTC) loss function. We conducted several experiments with noisy audio data that contain 20,000 valid sentences. The model was evaluated using the word error rate (WER) metric, achieving impressive results of 7% WER on noisy data. This approach has significant implications for the field of speech recognition, as it reduces the human effort required to create dictionaries and improves the efficiency and accuracy of ASR systems, making them more practical for real-world applications.

Recent literature has seen a rapid expansion in the reference to front-end forensics as an indication of the untapped value of forensic science. While some of these contributions have centered on development of forensic intelligence from a single area of investigation, others call for a more fundamental change in the relationship between crime laboratories and policing, particularly relating early laboratory analysis with big datasets to provide leads to investigators. We highlight several recently implemented tactical strategies of crime laboratories that contribute to the body of forensic intelligence. Beyond the scientific gains from these tactical applications, the corresponding details on associated efficiencies, costs, time savings, and quality improvements offer insights towards patterns of success for the community of crime laboratories. Further details expand an interpretation of what constitutes success with an eye on the contributions of the crime laboratory towards public health, safety, and protection of the innocent in addition to societal gains from conviction of the guilty. The economic interpretation of the value provided by the forensic laboratory assists in the cost-benefit review of strategic and tactical decisions and supports the justification for laboratory public funding with measures of the return on investment from public support of the forensic laboratory

Introduction: Advanced glycation end-products (AGEs) may play a relevant role as inducers in the chronic inflammatory pathway present in immune-mediated diseases, such as systemic lupus erythematosus (SLE). AGEs concentrations have been associated, with discrepant results to date, with some parameters such as disease activity or accrual damage, suggesting their potential usefulness as biomarkers of the disease. Objectives: To confirm differences in AGEs levels measured by cutaneous autofluorescence between SLE patients and healthy controls (HC) and study their correlation with various disease parameters. Methods: Cross-sectional study, where AGEs levels were measured by skin autofluorescence, and SLE patients’ data were compared with those of sex- and age-matched HC in a 1:3 proportion through a multiple linear regression model. Correlations of AGEs levels with demographic and clinical data were analyzed through ANOVA tests. Both analyses were adjusted for confounders. Results: AGEs levels in SLE patients were significantly higher than in HC (p<0.001). We found statistically significant positive correlation with SLE disease activity index (SLEDAI) and damage index (SDI), physician and patient global assessment, C-reactive protein, leukocyturia, complement C4, IL6 and oral ulcers. We also found a negative statistically significant correlation with current positivity of anti-nuclear and anti-Ro60 antibodies. Conclusions: AGEs seem to have a role in LES pathophysiology, becoming a new management and prognosis biomarker in this disease. The association with specific antibodies and disease manifestations may indicate a specific clinical phenotype related to higher AGEs levels.

The LED light source is an important light source for indoor visible light communication. It has the characteristics of a large divergence angle and a high transmission rate. Therefore, using LED as the light source for visible light full-duplex communication can not only satisfy the lighting requirements but also transmit information at high speed. To analyze the factors that affect the optical power of the indoor single-light source visible light communication uplink receiving end, an indoor visible light communication model was established, and mathematical calculation formulas were derived based on the model establishment, and the maximum movable range formula of the retroreflective end under specific conditions was derived; The reliability of the formula was verified with the help of Zemax simulation software. Theoretical analysis and experimental results show that increasing the movement range of the retroreflective end can be achieved by increasing the lens diameter, reducing the focal length, and increasing the link distance.

There is a pressing need for more precise biomarkers of chronic kidney disease (CKD). Plasma samples from 820 subjects [231 with CKD, 325 with end-stage kidney disease (ESKD) and 264 controls] were analyzed by LC-MS/MS to determine a metabolic profile of 28 aminoacids (AA) and biogenic amines to test their value as markers of CKD risk and progression. The Kynurenine/Tryptophan ratio showed the strongest correlation with estimated glomerular filtration rate values (coefficient=-0.731, P&lt;0.0001). Models created with Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) containing the metabolic signature showed high goodness of fit and predictability for controls/CKD (R2X:0.73;R2Y:0.92;Q2:0.92) and lower for CDK/ESKD (R2X:0.56;R2Y:0.59;Q2:0.55). Based on generated VIP scores, the most relevant markers for segregating samples into control/CKD or CKD/ESKD groups were citrulline (1.67) and tryptophan (1.59), respectively. ROC analysis showed that the addition of the metabolic profile to a model including CKD classic risk factors improved AUC from 86.7% (83.6-89.9) to 100% (100-100) for CKD risk (P&lt;0.0001), and from 63.0% (58.2-67.8) to 96.5% (95.3-97.8) for the risk of progression from CKD to ESKD (P&lt;0.0001). Plasma concentrations of AA and related amines may be useful as diagnostic biomarkers of kidney disease, both for CKD risk and for progression of CKD patients to ESKD.

The right to live with dignity during the final stages of existence, enshrined in national and supranational Charters of Rights, represents a significant step towards humanizing medicine and is integral to the right to health. Palliative Care, rooted in health, dignity, and therapeutic self-determination, has emerged as a fundamental human right and a moral imperative within health systems. It seeks to alleviate suffering, emphasizing the holistic well-being of patients with life-limiting illnesses. This paper provides a comprehensive global perspective on Palliative Care, with special attention to its implementation in Italy. Palliative Care is a crucial dimension of care, designed to enhance the quality of life for patients suffering from progressive and incurable diseases. Despite its growing need due to an aging population and increased prevalence of chronic illnesses, still remains a significant disparity in access to these essential services worldwide. Insufficient resources, legislative constraints, and limited training for healthcare providers pose challenges to the widespread adoption of Palliative Care. In Italy, although laws have been enacted to ensure the provision of Palliative Care, its availability remains inconsistent across different regions. Financial constraints and insufficient support hinder the comprehensive dissemination of these services. Recognizing the significance of Palliative Care, the Catholic Church also endorses its implementation as a response to human suffering and an approach to end-of-life care. Efforts to strengthen Palliative Care are critical to meeting the rising demand and ensuring access to compassionate and dignified care for all individuals in need. Through legislative advancements and adequate resources, Italy can make significant strides in advancing the provision of Palliative Care.

Introduction: The treatment of peritoneal dialysis in end-stage renal disease is increasing in clinical practice. The main purpose of this study was to evaluate the effect of far-infrared radiation therapy on inflammation and the cellular immunity of patients undergoing peritoneal dialysis. Materials and Methods: We recruited 56 patients undergoing peritoneal dialysis, and we included 32 patients from the experimental group and 24 patients from the control group in the final analysis. The experimental evaluation in our study was as follows: (1) We used abdominal computed tomography to measure the diameter and degree of hardening of the abdominal veins and large blood vessels to explore the changes in abdominal blood vessels. (2) The study compared the effects of peritoneal dialysis using albumin, blood urea nitrogen, creatinine, white blood cell, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, erythrocyte sedimentation rate, Hs-CRP clearance rate, and HBAlC. (3) We compared the cytokine concentration of blood between the two groups while controlling for GM-CSF, IL-2, IFN-γ, IL-6, IL-18, IL-4, IL-5, IL-13, IL-12p70, TNF-α, and IL-1β. Results and Discussion: (1) There was no significant difference in the abdominal blood vessels of the experimental group relative to the control group according to abdominal CT measured over 6 months. (2) Our study demonstrates the statistically significant effect of FIR therapy on the following parameters: albumin (p = 0.048*), creatinine (p = 0.039*), and Hs-CRP (p < 0.001**) decreased significantly, and glomerular filtration rate (eGFR, p = 0.043*) and glucose (p < 0.001**) increased significantly. Our study found that, in the experimental group, albumin and creatinine decreased significantly due to FIR therapy for 6 months. However, our study also found that, in the experimental group, glucose (p < 0.001**) increased significantly due to FIR therapy for 6 months. Peritoneal dialysis combined with FIR can reduce the side effects of glucose in dialysis buffer, which interferes with peritoneal inflammation and peritoneal mesothelial cell fibrosis. In addition, we also found that, in the experimental group, Hs-CRP (p < 0.001**) decreased significantly due to FIR therapy for 6 months. (3) No statistical significance in the inflammatory cytokines related to FIR therapy differences was observed in our study. IFN-γ (p = 0.124), IL-12p70 (p = 0.093), IL-18 (p = 0.213), and TNF-α (p = 0.254) did not exhibit significant improvements in peritoneal dialysis with FIR treatment over 6 months. IFN-γ and IL-18 in the plasma of patients in the experimental group and the control group were higher in the third month than in the first month. Conclusion: We found that the effect of peritoneal dialysis improved significantly with FIR therapy, and significant improvements in the peritoneal permeability and inflammatory response were observed. end-stage renal disease; peritoneal dialysis; far-infrared therapy (FIR)

Packaging of the eukaryotic DNA genome with histone and other proteins forms a chromatin structure that regulates the outcome of all DNA mediated processes. The cellular pathways that ensure genomic stability detect and repair DNA damage through mechanisms which are critically dependent upon chromatin structures established by histones and, particularly, transient histone post-translational modifications . Though subject to a range of modifications, histone methylation is especially crucial for DNA damage repair as the methylated histones often form platforms for subsequent repair protein binding at damaged sites. In this review, we highlight and discuss how histone methylation impacts the maintenance of genome integrity through effects related to DNA repair and repair pathway choice.

Short stature has been associated with increased various disease and all-cause death, but no reliable data exist the association between height and end-stage renal disease (ESRD) in diabetic patients. We investigated the relationship between short stature, development of ESRD, and mortality in type 2 diabetes. This study analyzed clinical data using the National Health Insurance Database in Korea. Height was stratified by five groups according to age and sex. Risk of ESRD and all-cause mortality was analyzed with Cox proportional hazards models. During a 6.9-year follow-up period, 220,457 subjects (8.4%) died and 28,704 subjects (1.1%) started dialysis. Short stature significantly increased the incidence of ESRD and all-cause mortality in the overall cohort analysis. In multivariable analysis, hazard ratios (HR) for development of ESRD comparing the highest versus lowest quartiles of adult height were 0.86 (95% confidence interval (CI), 0.83–0.89). All-cause mortality also decreased with highest height compared to patients with lowest height after fully adjusting for confounding variables (HR 0.79, 95% CI, 0.78–0.81). Adult height had an inverse relationship with newly diagnosed ESRD and all-cause in both males and females. Short stature is strongly associated with an increased risk of ESRD and all-cause mortality in type 2 diabetes.

Health information and communication are key elements that allow patients and family members to make decisions about end-of-life process and guarantee a death with dignity. Objective: To understand carers’ experiences regarding health information and communication during the illness and death of family members. Methods: This qualitative study was conducted in Andalusia based on the paradigm of hermeneutic phenomenology. Participants were carers who had accompanied a family member at the end of life for over 2 months and less than 2 years. Five nominal groups and 5 discussion groups were established, and 41 in-depth interviews with 123 participants were conducted. Atlas.ti 7.0 software was used to analyse the discourses. Results: Four dimensions of the dying process emerged: differences in carers’ perceptions of information and communication, a conspiracy of silence, consequences of the absence or presence of information, and the need for a culture change. Conclusions: Poor management of health information and communication at the end of life suffering and discomfort patients and their families. The culture of denying and avoiding death is still present today. A change in education about death would better enable health professionals to care for patients at the end of life.

Parallel manipulators with multiple end-effectors bring us interesting advantages over conventional parallel manipulators such as improved manipulability, workspace and avoidance of singularities. In this work the kinematics of a five-bar planar parallel manipulator equipped with two end-effectors is approached by means of the theory of screws. As an intermediate step the displacement analysis of the robot is also investigated. The input-output equations of velocity and acceleration are systematically obtained by resorting to reciprocal-screw theory. In that regard the Klein form of the Lie algebra se(3) of the Euclidean group SE(3) plays a central role. In order to exemplify the method of kinematic analysis, a case study is included. Furthermore, the numerical results obtained by means of the theory of screws are confirmed with the aid of special software like ADAMS.^TM

Longitudinal image registration of pulmonary computed tomography (PCT) images may serve as an essential tool for investigating the relationship between radiation dose distribution and the occurrence and phenotype of radiation-induced lung disease (RILD). Although numerous longitudinal registration algorithms have been developed for PCT, most similarity-based approaches are not suitable for PCT involving RILD due to the complex tissue variation between two PCT images. Moreover, conventional feature-based approaches might fail to find a sufficient number of matched pairs of feature points due to the disparate lung deformation caused by breathing and RILD. To overcome the challenges resulting from RILD, component structure coherence point drift (CSCPD) was proposed to establish a deformation model by decomposing the chest into several components and matching them with individual parameters based on coherence point drift (CPD). Moreover, a regional vascular point matching (RVPM) algorithm was proposed to generate a vascular subtree and to substantially increase the number of corresponding pairs between two images. Eventually, the components were recomposed and aligned by a thin plate spline algorithm. A performance assessment on 15 pairs of PCT images of patients with RILD yielded recall and precision values of 0.85 and 0.89 for RVPM, respectively. Moreover, the target registration error of CSCPD with RVPM (2.3 ± 1.79) was significantly better than that of conventional CPD with RVPM (2.95 ± 1.89) and conventional CPD (5.04 ± 2.87). Therefore, the proposed registration system is robust enough to address the disparate deformation of lungs with RILD, and it improves registration accuracy within the parenchyma.

With the ever-increasing popularity of unmanned aerial vehicles and other platforms providing dense point clouds, filters for identification of ground points in such dense clouds are needed. Many filters have been proposed and are widely used, usually based on the determination of an original surface approximation and subsequent identification of points within a predefined dis-tance from such surface. We present a new filter, Multi-view and shift rasterization algorithm (MVSR) is based on a different principle, i.e., on the identification of just the lowest points in in-dividual grid cells, shifting the grid along both planar axis and subsequent tilting of the entire grid. The principle is presented in detail and compared both visually and numerically to other commonly used ground filters (PMF, SMRF, CSF, ATIN) on three sites with different ruggedness and vegetation density. Visually, the MVSR filter showed the smoothest and thinnest ground profiles, with ATIN the only filter performing comparably. The same was confirmed when comparing ground filtered by other filters with the MVSR-based surface. The goodness of fit with the original cloud is demonstrated by the root mean square deviations (RMSD) of the points from the original cloud found below the MVSR-generated surface (ranging, depending on site, between 0.6-2.5 cm). The MVSR filter performed outstandingly at all sites, identifying the ground points with great accuracy while filtering out the maximum of vegetation/above-ground points. The filter dilutes the cloud somewhat; in such dense point clouds, however, this can be perceived rather as a benefit than as a disadvantage.

With the ever-increasing popularity of unmanned aerial vehicles and other platforms providing dense point clouds, universal filters for accurate identification of ground points in such dense clouds are needed. Many filters have been proposed and are widely used, usually based on the determination of an original surface approximation and subsequent identification of points within a predefined distance from such surface. In this paper, we present a new filter. This Multi-view and shift rasterization algorithm (MVSR) is based on an entirely different principle, i.e., on the identification of just the lowest points in individual grid cells, shifting the grid along both planar axis and subsequent tilting of the entire grid – after each of these steps, one lowest point per cell is detected. The principle is presented in detail and compared both visually and numerically to other commonly used ground filters (PMF, SMRF, CSF, ATIN) on three sites with different ruggedness and vegetation density. Visually, the MVSR filter showed the smoothest and thinnest ground profiles, with ATIN the only filter performing comparably (although the profiles were somewhat thicker and not as complete as MVSR-acquired ground). The same was confirmed when comparing ground filtered by other filters with the MVSR-based surface. The goodness of fit with the original cloud is demonstrated by the root mean square deviations (RMSD) of the points from the original cloud found below the MVSR-generated surface (ranging, depending on site, between 0.6-2.5 cm). ATIN again performed closest to MVSR, with RMSDs of ground filtered points found above MVSR-based surface at individual sites ranging between 4.5-7.4 cm. The remaining filters performed comparable in the simplest flat area but poorly in rugged and much-vegetated sites, with RMSDs above the MVSR surface ranging at such sites from 21 to 95 cm. In conclusion, the novel filter presented in this paper performed outstandingly at all sites, identifying the ground points with great accuracy while filtering out the maximum of vegetation/above-ground points. The filter dilutes the cloud somewhat; in such dense point clouds, however, this can be perceived rather as a benefit than as a disadvantage.

In this paper, we present a relapse based demonstrating way to deal with investigate various arrangement MTC information. A commonplace use of this displaying approach incorporates three stages: first, define a model that approximates the connection between quality articulation and trial factors, with boundaries consolidated to address the exploration premium; second, utilize least-squares and assessing condition methods to gauge boundaries and their relating standard blunders; third, register test insights, P-qualities and NFD as proportions of factual criticalness. The benefits of this methodology are as per the following. To begin with, it tends to the exploration interest in a particular, precise way, and maximally uses all the information and other important data. Second, it represents both orderly and irregular varieties related with the information, and the consequences of such examination give not just quality explicit data applicable to the exploration objective, yet additionally its dependability, in this way helping agents to settle on better choices for subsequent investigations. Third, this methodology is truly adaptable, and can undoubtedly be stretched out to different sorts of MTC considers or other microarray explores by detailing various models dependent on the test plan of the investigations.

Breeding for low hydrogen cyanide (HCN) varieties is a major objective of programmes targeting boiled cassava food product. To enhance breeding for low HCN, knowledge on genetic variation and trait heritability is essential. In this study, 64 cassava clones established across four locations and evaluated for HCN using three HCN assessment methods; 1 to 9 scale, 0 ppm to 800 ppm scale and a quantitative assay based on spectrophotometer readings (HCN_Spec). Data were also collected on the weather variables precipitation, relative humidity, and temperature. Significant differences were observed among clones (P &lt; 0.001) and locations (p &lt; 0.001). There were also significant (p &lt; 0. 001) clone by environment interactions. Locations Arua and Serere posted higher HCN scores among clones and were associated with significantly higher (p &lt; 0.001) mean daily temperature (K) and lower relative humidity (%) across 12-hour and 18-hour intervals. Within locations HCN broad sense heritability estimates ranged 0.22 – 0.64 while combined location heritability estimates ranged 0.14 to 0.32. Relationships between the methods were positive and strong (R2 = 0.75 – 0.92). It is expected that the information herein will accelerate efforts towards breeding for low HCN cassava varieties.

Chronic hemodialysis (HD) patients have very high cardiovascular risk. Acute vascular changes during dialysis mediated by factors of the endothelium may have a crucial role in this. Aim: To study acute vascular changes during HD. In 29 consecutive chronic HD patients (age: 65.6±10.4 years) pre-, mid-, and post-HD plasma syndecan-1 (SDC-1) and endothelin-1 (ET-1) levels were measured. Applanation tonometry was performed before HD. SDC-1 levels increased during HD (p=0.004). Males had higher ET-1 levels. Patients were divided into 2 groups based on pulse wave velocity (PWV): PWV≥12 m/s and PWV<12 m/s. Pre-HD and mid-HD SDC-1 levels were higher in the group with PWV≥12 m/s (10.174±2.568 vs. 7.928±1.794 ng/ml, p=0.013, and 10.319±3.482 vs. 8.248±1.793 ng/ml, p=0.044, respectively). Post-HD ET-1 levels were higher in the patient group having PWV≥12 m/s (10.88±3.00 vs. 8.05±3.48 pg/l, p=0.027). Patients having PWV≥12 m/s had higher pre-HD peripheral and aortic systolic blood pressure (p<0.05). Total cholesterol correlated with the SDC-1 decrease during HD (r=0.539; p=0.008). Pre-, mid-, and post-HD SDC-1 correlated with the ultrafiltration (r=0.432, p=0.019; r=0.377, p=0.044; and r=0.401, p=0.012, respectively). SDC-1 and ET-1 contribute to vascular changes observed during HD, and they have correlations with some cardiovascular risk factors.

Hundreds of thousands of kilometers of communication and power (umbilical) cables and flowlines lie undersea worldwide. Most of these offshore cables and flowlines have reached or will soon be nearing the end of their service life, necessitating the need for a viable recycling approach to recover some valuable material, e.g., copper. However, separation into constituent materials has proven very challenging due to the highly robust design of the composite cables (and flowlines) to withstand service conditions and the tough external plastic sheaths that protect against seawater corrosion. Here, we summarize the findings of the cryogenic comminution of subsea cables and flowlines for an effective separation and recovery of component materials. Heat transfer analyses of complex multilayer flowlines and umbilicals were conducted to evaluate the time required for these structures to reach their respective critical brittle-transition temperatures. Subsequently, the time was used as a guide to crush the flowline and umbilical cables under cryogenic conditions. The results show that the flowlines and umbilical cables will reach the brittle-transition temperature after approximately 1000s (i.e., 17 min) of submergence in liquid nitrogen (LN). Comminution of the materials at temperatures near the brittle-transition temperature was proven relatively efficient compared to room-temperature processing. The present evaluation of heat transfer and lab-scale crushing will afford accurate process modelling and design of a pilot cryogenic comminution of decommissioned flowlines in an LN-doped atmosphere.

Diabetic nephropathy (DN), one of the primary consequences of diabetes mellitus, affecting many people worldwide. Diabetes affects yearly 463 million adults globally between the ages of 20 and 79, and is the main cause of death under the age of 60. ROS production rises during hyperglycemia and is crucial to the development of diabetic complications. Advanced glycation end products (AGEs) are produced excessively in a diabetic state and accumulate in the kidney, where they change renal architecture and impair renal function. Another important targeted pathway for the formation of DN includes NF-B, Nrf2, NLRP3, Akt/mTOR, and autophagy. About 40% of individuals with diabetes eventually acquire diabetic kidney disease and end stage renal disease that needs hemodialysis, peritoneal dialysis, or kidney transplantation, to survive. The current state of acceptable therapy for this kidney ailment is limited. The studies revealed that some naturally occurring bioactive substances might shield the kidney by controlling oxidative stress, renal fibrosis, inflammation, and autophagy. In order to provide new potential therapeutic lead bioactive compounds for contemporary drug discovery and clinical management of DN, this review was designed to examine the various mechanistic pathways by which current conventional plants derive phytocompounds that are effective for the control and treatment of DN.

Regular exercise can reduce depression. However, the uptake of exercise is limited in patients with end-stage renal disease undergoing hemodialysis. To address the gap, we designed a gamified non-weight-bearing exercise program (Exergame), which can be executed during hemodialysis treatment. The Exergame is virtually supervised based on its interactive feedback via wearable sensors attached on lower extremities. We examined the effectiveness of this program to reduce depression symptom compared to supervised exercise in 73 hemodialysis patients (age=64.5±8.7years, BMI=31.6±7.6kg/m2). Participants were randomized into an Exergame group (EG) or a Supervised-exercise group (SG). Both groups received similar exercise tasks for 4-week, 3-session per week, 30-min per session, during hemodialysis treatment. Depression symptom was assessed at baseline and 4-week using Center for Epidemiologic Studies Depression (CES-D). Both groups showed significant reduction in depression score (37%, p<0.001, Cohen’s effect size d=0.69 in EG vs. 41%, p<0.001, d=0.65 in SG) with no between-group difference for the observed effect (p>0.050). The EG expressed a positive exercise experience including fun, safety, and helpfulness of sensor-feedback. Together, results suggested that the virtually-supervised low-intensity Exergame is feasible during routine hemodialysis treatment. It is as effective as supervised-exercise to reduce depression symptom, while reducing burden of administrating exercise in dialysis clinics.

Pit lakes can represent significant liabilities at mine closure. However, pit lakes also present opportunities to provide significant regional benefit and address residual closure risks of both their own and overall project closure, and even offset the environmental costs of mining by creating new end uses. Unfortunately, many pit lakes have continued to be abandoned without repurposing for an end use. We reviewed published pit lake repurposing case studies of abandoned mine pit lakes. We found beneficial end use type and outcome varied dependent upon climate and commodity; but equally important social and political dynamics that manifest as mining company commitments or regulatory requirements. Many end uses have been realized: passive and active recreation, nature conservation, fishery and aquaculture, drinking and industrial water storage, greenhouse carbon fixation, flood protection and waterway remediation, disposal of mine and other waste, mine water treatment and containment, and education and research. Common attributes and reasons that led to successful repurposing of abandoned pit lakes as beneficial end uses are discussed. Recommendations are given for all stages of mine closure planning to prevent pit lake abandonment and to achieve successful pit lake closure with beneficial end uses.

Objective: To examine variations in end-of-life care for breast cancer, heart failure, and dementia patients. Data and methods: Data from four Norwegian health registries were linked using a personal identification number. Longitudinal trends over 365 days and the type of care on the final day of life were analyzed using descriptive techniques and logistic regression analysis. Results: Patients with dementia were most commonly placed in nursing homes, while patients with heart failure and breast cancer were more frequently hospitalized. Breast cancer and heart failure patients had a higher likelihood of dying at home. The presence of general practitioners increased the probability of home-based end-of-life care for cancer patients, while non-physician healthcare workers increased the likelihood of home-based care for the other patient groups. Conclusion: Diagnoses, individual characteristics, and service availability are all associated with the place of death in end-of-life care.

Heat using behavior has a large impact on in heating energy in heat metering system, and therefore a better understanding can assist in behavior guidance to reduce energy. To understand the current situation of indoor thermal environment and heat using behavior for heat metering households in northern China, including adjusting heating end valves and operating windows, 30 households were measured and surveyed. The factors influencing heat using behavior, including outdoor and indoor environmental parameters and time of the day, were analyzed. The results are :1) Thermal neutral temperature for heat metering households is relatively high, up to 24.5℃; 2) The heat using behavior of households is lack of rationality: low proportion of active households; high indoor temperature setting; more frequency of window opening. Improving indoor comfort is the main reason for households to adjust the heating end valve, and only 7.15% of households have considered the economic benefits brought by adjusting the valve. " Thermostat control valve does not work" is the main reason for households without adjustment; 3) Time of the day and indoor temperature affect active households’ willingness to adjusting heating end valve. Time of the day, indoor temperature and outdoor temperature have impacts on opening window during heating period.

Traditional end-effector robots for arm rehabilitation are usually attached a­t the hand, primarily focusing on coordinated multi-joint training. Therapy at an individual joint level of the arm for severely impaired stroke survivors is not always possible with existing end-effector robots. The Arm Rehabilitation Robot (AREBO)—an end-effector robot—was designed to provide both single and multi-joint assisted training while retaining the advantages of traditional end-effector robots, such as ease of use and cost-effectiveness (compared to exoskeletons). This work presents the design, optimization, and characterization of AREBO for training single-joint movements of the arm. The AREBO has three actuated and three unactuated degrees of freedom, allowing it to apply forces in any arbitrary direction at its endpoint and self-align to arbitrary orientations within its workspace. The AREBO’s link lengths were optimized to maximize its workspace and manipulability. The AREBO provides single-joint training in both unassisted and adaptive weight support modes using a human arm model to estimate the human arm's kinematics and dynamics without using additional sensors. The characterization of the robot’s controller and the algorithm for estimating the human arm parameters were performed using a two degrees of freedom mechatronic model of the human shoulder joint. The results demonstrate that: (a) the movements of the human arm can be estimated using a model of the human arm and robot’s kinematics, (b) the AREBO has similar transparency to that of existing arm therapy robots in the literature, and (c) the adaptive weight support mode control can adapt to different levels of impairment in the arm. This work demonstrates how an appropriately designed end-effector robot can be used for single-joint training, which can be easily extended to multi-joint training. Future work will focus on the evaluation of the system on patients with any neurological condition requiring arm training.

Background. There is limited evidence on the relationship between chronic kidney disease (CKD) and frailty in older people in Vietnam. This study aimed to examine the prevalence of frailty and its impact on mortality in older patients with end-stage renal disease. Method. This is a prospective, observational study at two large Dialysis Centres in Vietnam from November 2020 to June 2021. Consecutive older patients diagnosed with end-stage renal disease and on dialysis were recruited. Participants’ frailty status was defined by the Clinical Frailty Scale (CFS). The study outcome was all-cause mortality at 6th month. Results. A total of 175 participants were recruited (mean age 72.4 years, 58.9% female). Using the cut-point of CFS ≥4, 87.4% of the participants were frail. Mortality at 6th month was 14.9%, 31.9% in participants with CFS ≥7, 12.8% in participants with CFS=6, 7.5% in participants with CFS from 4 to 5, and 4.5% in participants with CFS ≤3 (p=0.001). Cox regression analysis showed that compared to the non-frail participants, the probability of death over 6 months was nearly 2-fold higher in the mildly frail, 3-fold higher in the moderately frail, and 9-fold higher in the severely frail participants. Conclusion. This study demonstrated a very high prevalence of frailty in older patients with end-stage renal disease and dialysis and the significant impact of frailty severity on mortality. Healthcare providers should consider incorporating frailty screening into routine care for older patients with end-stage renal disease and dialysis.

Digital health research is an emerging discipline that requires easy-to-understand theoretical frameworks and implementation models for digital health providers in health and social care settings. The COVID-19 pandemic has heightened demand for digital health discipline-specific instruction on ‘how to’ manage evidence-based digital health transformation. Access to the use of these models guarantees that digital health providers can investigate phenomena using safe and suitable approaches and methods to conduct research and identify answers to challenges and problems that arise in health and social care settings. The COMPASS theory is designed to aid transformation of health and social care environments. A navigational rose of primary quadrants is divided by four main compass points, with person-centred care being central to the philosophy. Two axes produce Cartesian planes that intersect to form a box plot, which can be used to discover human and physical resource weightings to augment digital health research design and implementation. A third continuum highlights stakeholders' capabilities, critical for any multidisciplinary study. The COMPASS mnemonic guides end-users through the process of design, development, implementation, evaluation, and communication of digital health transformations. The theory's foundations are presented and explained in context of the 'new normal' of health and social care delivery.

Background: Membranous glomerulonephritis (MGN) is one of the most common causes of nephrotic syndrome in non-diabetic adults, accounting for up to one-third of biopsy diagnostics in some regions. A watchful waiting strategy has been adopted to accurately assess the success of MGN. In this study, we predict prognostic risk factors for primary MGN patients for effective intervention and prognosis. Materials and Methods: This is a retrospective study design involving 125 biopsy-proven MGN subjects on follow-up from January 2012 to October 2019 in Hospital Serdang and Hospital Kuala Lumpur, Malaysia. Result and Conclusion: After the median follow-up of 36 (15.0-57.0) months, 26 (20.8%) consisting of 17 (13.6%) primary MGN and 9 (7.2%) secondary MGN subjects achieved complete remission while 17 (13.6%) and 10 (8.0%) of primary and secondary MGN subjects respectively achieved spontaneous remission. Subjects with nephrotic range proteinuria have 2.917 odds more at risk of primary MGN while subjects with hypertension have 2.417 odds risk of primary MGN compared to those with no hypertension at presentation. Multiple logistic regression models retained only factors directly associated with MGN with only nephrotic range proteinuria associated with primary MGN. Those with nephrotic range proteinuria were 3.04 times the odds of primary MGN when compared with those without nephrotic syndrome (95% C. I = 1.089, 8.472). The nephrotic syndrome could be used to determine the prognosis in patients with primary MGN especially those at risk of end-stage kidney disease.

Initially, retinol-binding protein (RBP), was thought to be a biomarker for proximal convoluted tubule dysfunction could be important in chronic kidney diseases (CKD). Membranous glomerulonephritis (MGN) is an important cause of CKD and end-stage renal disease (ESRD). Therefore, monitoring MGN patients using urinary RBP is important in effective treatment decision making and prognostication of MGN patients. Enzyme-linked immunosorbent assay (ELISA) technique was used to detect the RBP in the urine samples of 69 MGN patients comprising 47 primary and 22 secondary MGN, at the end of the follow-up period. The test for the urinary biomarker gave the following results: urinary RBP was detected in 27 (39.1%) and 6 (8.7%) of the primary and secondary MGN patients, respectively. The correlation analysis demonstrated a significant relationship between urinary RBP and renal function test parameters, in addition to a logistic regression analysis that proved urinary RBP as a prognostic non-invasive biomarker for primary MGN. Therefore, urinary RBP could be employed to monitor and provide effective prognosis and early treatment decisions in primary MGN.

Critically the paper argues that a truly people-centered technology supported chronic care system can only be designed by understanding and responding to the needs, attributes and capabilities of the most vulnerable in society. The paper suggests innovative ways of supporting interactions with these ‘end-users’ and highlights how reflection on these approaches can contribute to emancipating both the health system to move towards more socially inclusive e-health solutions.

Abstract The aim of this research is to conduct a systematic review analysing the role of the physiotherapy interventions in palliative care. PRISMA as a critical appraisal tool was utilised for the selection of the research articles. The inclusion criteria were based on the year of publication, ease of availability, language, geographical location, and study type. To ensure the credibility, databases such as Elsevier, Proquest, and EBSCO Host were used to filter the grey content. Data published in the past ten years (2009-onwards) was only included to ensure the selection of the most recent interventions used by the physiotherapists. A total of 11 articles were selected which determined that physiotherapy interventions involving breathing exercises, aerobic exercises, manual therapies, and educational awareness were critical to promoting the functional capability and empower the patients.

The urbanisation phenomenon and related cities expansion and transport networks entail preventing the increase of population exposed to environmental pollution. Regarding noise exposure, the Environmental Noise Directive demands on main metropolis to produce noise maps. While based on standard methods, these latter are usually generated by proprietary software and require numerous input data concerning, for example, the buildings, land use, transportation network and traffic. The present work describes an open source implementation of a noise mapping tool fully implemented in a Geographic Information System compliant with the Open Geospatial Consortium standards. This integration makes easier at once the formatting and harvesting of noise model input data, cartographic rendering and output data linkage with population data. An application is given for a French city, which consists in estimating the impact of road traffic-related scenarios in terms of population exposure to noise levels both in relation to a threshold value and level classes.

Advanced Glycation End Products (AGEs) represent a set of substances that contribute directly to the triggering and/or aggravation of pathologies associated with ageing. AGEs are produced by the reaction between reducing sugars (or α-dicarbonyl compounds) proteins and amino acid residues. Current methodologies require an incubation period of 1-3 weeks to generate AGEs. In this study the reaction time for the formation of AGEs (48 and 3 hours) is significantly reduced by coupling and adapting procedures already existing in the literature to the free radical generation system called the hypoxanthine/xanthine oxidase assay. The capacity of different classes and chemical compounds (aminoguanidine, chlorogenic acid, rutin, extracts of Hancornia speciosa Gomes) were evaluated to inhibit the protein glycation process, acting as capturing agents of α-dicarbonyl species. Aminoguanidine, rutin and the leaf extracts of Hancornia speciosa Gomes show a high capacity to act as α-dicarbonyl compound scavengers (RCS-trapping) and resulting in the inhibition of AGEs formation.

Previous camera self-calibration methods have exhibited certain notable shortcomings. On one hand, they either exclusively emphasized scene cues or solely focused on vehicle-related cues, resulting in a lack of adaptability to diverse scenarios and a limited number of effective features. Furthermore, these methods either solely utilized geometric features within traffic scenes or exclusively extracted semantic information, failing to comprehensively consider both aspects. This limited the comprehensive feature extraction from scenes, ultimately leading to a decrease in calibration accuracy. Additionally, conventional vanishing point-based self-calibration methods often required the design of additional edge-background models and manual parameter tuning, thereby increasing operational complexity and the potential for errors. Given these observed limitations, and in order to address these challenges, we propose an innovative roadside camera self-calibration model based on the Transformer architecture. This model possesses a unique capability to simultaneously learn scene features and vehicle features within traffic scenarios while considering both geometric and semantic information. Through this approach, our model can overcome the constraints of prior methods, enhancing calibration accuracy and robustness while reducing operational complexity and the potential for errors. Our method outperforms existing approaches on both real-world dataset scenarios and publicly available datasets, demonstrating the effectiveness of our approach.

Most real-time terrain point cloud rendering techniques do not address the empty space between the points but rather try to minimize it by changing the way how the points are rendered by either rendering them bigger or with more appropriate shapes such as paraboloids. In this work, we propose an alternative approach to point cloud rendering, which addresses the empty space between the points and tries to fill it with appropriate values to achieve the best possible output. The proposed approach runs in real time and outperforms several existing point cloud rendering techniques in terms of speed and render quality.

In this article, we introduce a relatively new concept of multi-valued (θ;R)-contractions and utilize the same to prove some xed point results for a new class of multi-valued mappings in metric spaces endowed with an amorphous binary relation. Illustrative examples are also provided to exhibit the utility of our results proved herein. Finally, we utilize some of our results to investigate the existence and uniqueness of a positive solution for the integral equation of Volterra type.

Background: Reelin is a large extracellular glycoprotein secreted by Cajal–Retzius cells and has a main role during brain development, especially in neuronal migration. Reelin is comprised of N-terminal F-spondin like domain, eight tandem repeats, and a highly conserved basic C-terminal region (CTR). The CTR main role in the secretion of Reelin has been investigated by advertently inducing deletion in whole or a part of this region; however, the role of CTR point mutations on the secretion of Reelin is shrouded in mystery. Materials and Methods: In this study, we performed experimental analyses on a subregion of Human Reelin containing 5th and 6th repeats (R5-R6), a part of 8 th repeat and the CTR which were amplified from cDNA of K562 and HEPG2 cells and cloned into a mammalian expressional plasmid (pVP22/myc-His). Bioinformatics investigation was performed on the CTR at both level of nucleotide and amino acid as well as mutant type. Random mutagenesis by error-prone PCR method was utilized to induce mutation in the CTR. The secretion efficiency of recombinant wild-type and mutant Reelin constructs compared in cell lysate and supernatant isolated from the transiently transfected HEK 293T cells using 6XHistag ELISA method. Results: In-vitro study demonstrated that the CTR alteration(S3440P) leads to impairment of Reelin secretion even after overexpression. Conclusions: Our results indicate that S3440P substitution in highly conserved structure of the CTR has an important effect on Reelin secretion.

This paper aims at tackling with the task of fusion feature from images and its corresponding point clouds for 3D object detection in autonomous driving scenarios basing on AVOD, an Aggregate View Object Detection network. The proposed fusion algorithms fuse features targeted from Bird’s Eye View (BEV) LIDAR point clouds and its corresponding RGB images. Differs in existing fusion methods, which are simply the adoptions of concatenation module, element-wise sum module or element-wise mean module, our proposed fusion algorithms enhance the interaction between BEV feature maps and its corresponding images feature maps by designing a novel structure, where single level feature maps and another utilizes multilevel feature maps. Experiments show that our proposed fusion algorithm produces better results on 3D mAP and AHS with less speed loss comparing to existing fusion method used on the KITTI 3D object detection benchmark.

Assuming the Multiple Point Principle (MPP) as a new law of Nature, we considered the existence of the two degenerate vacua of the Universe: a) the first Electroweak (EW) vacuum at $v_1\approx 246$ GeV—“true vacuum”, and b) the second Planck scale “false vacuum” at $v_2 \sim 10^{18}$ GeV. In these vacua, we investigated different topological defects. The main aim of the paper is an investigation of the black-hole-hedgehogs configurations as defects of the false vacuum. In the framework of the $f(R)$ gravity, described by the Gravi-Weak unification model, we considered a black-hole solution, which corresponds to a “hedgehog”—global monopole, that has been “swallowed” by the black-hole with mass core $M_{BH}\sim 10^{18}$ GeV and radius $\delta\sim 10^{-21}$ GeV$^{-1}$. Considering the results of the hedgehog lattice theory in the framework of the $SU(2)$ Yang-Mills gauge-invariant theory with hedgehogs in the Wilson loops, we have used the critical value of temperature for the hedgehogs’ confinement phase ($T_c\sim 10^{18}$ GeV). This result gave us the possibility to conclude that the SM shows a new physics (with contributions of the $SU(2)$-triplet Higgs bosons) at the scale $\sim 10$ TeV. This theory predicts the stability of the EW-vacuum and the accuracy of the MPP.

The purpose of this article is to characterize the Caristi type mapping by the absolute derivative. The equivalences of the Caristi mapping with contraction mapping is discussed too. In addition, it was shown that the contraction mapping can be characterized by its absolute derivative.

In this paper, we present the idea of Self Supervised learning on the Shape Completion and Classification of point clouds. Most 3D shape completion pipelines utilize autoencoders to extract features from point clouds used in downstream tasks such as Classification, Segmentation, Detection, and other related applications. Our idea is to add Contrastive Learning into Auto-Encoders to learn both global and local feature representations of point clouds. We use a combination of Triplet Loss and Chamfer distance to learn global and local feature representations. To evaluate the performance of embeddings for Classification, we utilize the PointNet classifier. We also extend the number of classes to evaluate our model from 4 to 10 to show the generalization ability of learned features. Based on our results, embedding generated from the Contrastive autoencoder enhances Shape Completion and Classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

In this paper, the notion of θ∗-weak contraction is introduced, which is utilized to prove some fixed point results. These results are helpful to give a positive response to certain open question raised by Kannan [Amer. Math. Monthly 76:1969] and Rhoades [Contemp. Math. 72:1988] on the existence of contractive definition which does not force the mapping to be continuous at the fixed point. Some illustrative examples are also given to support our results. As applications of our result, we investigate the existence and uniqueness of a solution of non-linear matrix equations and integral equations of Volterra type as well.

Using a theorem of Ulam and Hyers, we will prove the Hyers-Ulam stability of two-dimensional Lagrange's mean value points $(\eta, \xi)$ which satisfy the equation, $f(u, v) - f(p, q) = (u-p) f_x(\eta, \xi) + (v-q) f_y(\eta, \xi)$, where $(p, q)$ and $(u, v)$ are distinct points in the plane. Moreover, we introduce an efficient algorithm for applying our main result in practical use.

This study innovatively assesses the Mun River flow components in Thailand, integrating Artificial Neural Networks (ANN) and Isotopic (18O) End-Member Mixing Analysis (IEMMA). It quantifies the contributions of the Upper Mun River (UMR) and Chi River (CR) to the overall flow, revealing a discrepancy in their estimated contributions. ANN predicts the UMR and CR contribute approximately 70.1% and 29.5% respectively, while IEMMA indicates a more pronounced disparity with 84% from UMR and 16% from CR. This divergence highlights the distinct perspectives of ANN, focusing on hydrological data patterns, and IEMMA, emphasizing isotopic signatures. Despite discrepancies, both methods validate UMR as a significant contributor to the overall flow, highlighting their utility in hydrological research. The findings underscore the complexity of river systems and advocate for an integrated approach of river flow analysis for a comprehensive understanding, crucial for effective water resource management and planning.

Dielectric elastomer actuators (DEAs) offer robust, high energy density solutions for soft robotics. The proposed end effector consists of three spring roll configuration DEAs, each acting as a robotic finger, using a 3M VHB-F9473PC adhesive membrane. The lateral force, bending angle and response time of the actuator are measured experimentally and compared with the predictions of an analytical model. The cylindrical actuator measures 70 mm in length and 15 mm in diameter and achieved a lateral force of 30 mN, a bending angle of 6.8 ° and a response time of ≈ 1 s. Spring roll configuration DEAs are shown to reduce the effects of viscoelasticity seen in the membrane, making the actuator more controllable at higher voltages. The dielectric constant of the membrane is shown to be a limiting factor of actuation, with a decrease in dielectric constant resulting in larger actuation. The end effector successfully gripped numerous light objects for extended periods, showing the applicability of spring roll DEAs for soft end effectors.

This paper discusses a MEMS switch that can be fabricated using low temperature (<100^oC) deposition and patterning techniques suitable for the back-end-of-the-line integration with CMOS. The resulting cross-bar switches can be used for electrostatic discharge protection, FPGA implementation, chip security assessment and lock-down, and circuit block power management. We discuss platinum and iron switch with turn-on voltages of ~ 1.8 V. In the case of the iron switches, we also show that they can be magnetized to have “memory” and stay on when turned on. Platinum switch cycling of up to 1000 times did not show any changes in their turn-on voltage and their contact resistance was unchanged. The 10-100 nm switch airgaps were formed using low temperature sputtered sacrificial polysilicon and XeF₂ etching. XeF₂ does not attack any of the metals used in CMOS enabling fabrication of cross-bar switches with any of these metals. Once activated, it takes ∿ 6 to mechanically turn on the switch that can be decreased to ~1 ns by optimizing the device structure. Interestingly, the nm-scale gaps can be used as spark gap as a fast plasma switch to discharge first followed by the activation of the MEMS switch.

For older adults, approaching end-of-life (EOL) brings unique transitions related to family relationships. Unfortunately, most families greatly underestimate the need to discuss these difficult issues. For example, parents approaching EOL issues often struggle with receiving assistance from others, avoiding family conflict, and maintaining their sense of personhood. In addition, discussions of EOL issues force family members to face their parents’ mortality, which can be particularly difficult for the adult children to process emotionally. This study explored aging issues identified by aging parents and their families as they traverse these impending EOL changes. Ten focus groups of seniors (n = 65) were conducted. Focus groups were organized according to race (African American/Caucasian), gender, and whether the older adult was living independently or in an assisted care facility. When asked open-ended questions about discussing aging and EOL issues with family members, participants revealed tensions that led us to consider Relational Dialectics Theory as a framework for analysis. The predominant tension highlighted in this report was certainty versus uncertainty, with the two sub-themes of sustained life versus sustained personhood and confronting versus avoiding EOL issues. For these data, there were more similarities than differences as a result of gender, race, or living situation than one might expect, although culture and financial status were found to be influential in the avoidance of EOL discussions. The results of this study help to provide additional insight into relational dialectics related to aging, EOL, and the importance of communication in facilitating family coping.

This paper proposes a solution for the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpolation algorithm. The paper first introduces some open-source laser SLAM algorithms and then elaborates in detail on the general framework of the SLAM system used in this paper. Secondly, the concept of voxel is introduced into the occupation probability map to enhance the ability of local voxel maps to represent dynamic objects. Then, in this paper, we propose a PointNet++ point cloud semantic segmentation network combined with deep learning algorithms to extract deep features of dynamic point clouds in large scenes and output semantic information of points on static objects. A descriptor of the global environment is generated based on its semantic information. Closed-loop completion of global map optimization is performed to reduce cumulative error. Finally, T-trajectory interpolation is utilized to ensure the motion performance of the robot and improve the smooth stability of the robot trajectory. The experimental results indicate that the combination of the semantic laser SLAM system with deep learning and the trajectory interpolation algorithm proposed in this paper yields better graph building and loop closure effects in large scenes at SIASUN large scene campus. The use of T-trajectory interpolation ensures vibration-free and stable transitions between target points.

Traditional underground pipeline inspection primarily relies on closed-circuit television (CCTV) systems, capturing visual data of deformations within sewer systems for manual assessment of their types and severity. However, these methods heavily rely on human expertise, which leads to subjective detection with limited accuracy. Moreover, they lack the capability for quantitative analysis of deformation extent, hindering accurate assessments and limiting overall inspection effectiveness. To address these challenges, this paper proposes a method for quantitatively detecting geometric deformations in underground pipe corridors using laser point cloud data. The approach, employing laser scanning with a 3D scanner, enables objective detection of internal pipeline deformations and quantitative assessment of blockage levels. In comparison to traditional CCTV-based methods, this approach offers advantages in objectivity and quantification, thereby improving detection reliability, accuracy, and overall efficiency.

Point-contact spectroscopy has been performed on bulk samples of electron-doped high temperature superconductor Nd2-xCexCuO4-. The samples were characterized by X-ray diffraction and scanning electron microscopy equipped with a wavelength dispersive spectrometer and an electron backscatter diffraction detector. The samples with a Ce content x = 0.15, show the absence of spurious phases and randomly oriented grains, most of which have dimensions of approximately 220 µm2. The low-bias spectra in the tunnelling regime, i.e., high transparency interface, exhibit a gap feature at about ±5 meV and no zero-bias conductance, consistent with the most literature data on oriented samples. The high-bias conductance has also been measured in order to obtain information on the properties of the barrier. A V-shape has been in some cases observed, instead of the parabolic behavior expected for tunnel junctions.

Non-rigid registration presents a significant challenge in the domain of point cloud processing. The general objective is to model complex non-rigid deformations between two or more overlapping point clouds. Applications are diverse and span multiple research fields, including registration of topographic data, scene flow estimation, and dynamic shape reconstruction. To provide context, we begin with a general introduction to the topic of point cloud registration, including a categorization of methods. Next, we introduce a general mathematical formulation for point cloud registration and extend it to address non-rigid registration. A detailed discussion and categorization of existing approaches to non-rigid registration follows. We then introduce our own method where the usage of piece-wise tricubic polynomials for modeling non-rigid deformations is proposed. Our method offers several advantages over existing methods. These advantages include easy control of flexibility through a small number of intuitive tuning parameters, a closed-form optimization solution, and an efficient transformation of huge point clouds. We demonstrate our method through multiple examples that cover a broad range of applications, with a focus on remote sensing applications - namely, the registration of Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser Scanning (TLS) point clouds. The implementation of our algorithms is open source and can be found on GitHub.

The relationship between living things and their respective environments highly dependent on body temperature regulation. The human capacity to effectively thermoregulate evolved at a time when the environmental temperature was likely around 25°C during the Eocene epoch, some ~ 50-60 million years ago. This effectively meant that homeothermy settled on a core temperature balance point of ~ 37°C. When Homo split from chimpanzee around 5 million years ago the Earth was entering a cooling period where the balance point temperature was always well above that of the environment and body heat balance could be maintained. Following this cooling period, the Earth’s rewarming by 7 °C took over approximately 5,000 years, whereas the current estimates indicate 0.7 °C over the past 100 years; ten times the rate of ice-age-recovery warming, or 20 times faster compared with the last 2 million years. As such, if the predicted continued rise in global temperature continues, and surface temperature reaches values where heat load cannot be dumped as the body temperature balance point is at or near the environmental temperature, areas of the Earth would become inhospitable. This effectively means that we will need to deal with both physiological and behavioral limitations since our ability to adapt will be limited by a thermoregulatory strategy that evolved over millions of years for a different kind of environment, not one that is predicted to change rapidly over the next century. This paper outlines the basis on which Homo settled on a thermoregulatory balance point and what limitations this presents for us in the future.

Urban scene point cloud pose significant challenges for registration due to its large data volume, similar scenarios and dynamic objects. In this paper, we propose PCRMLP, a model for urban scene point cloud registration that achieves comparable registration performance to prior learning-based methods. Compared to previous works which focus on extracting features and estimating correspondence, the model estimates the transformation implicitly from concrete instances. An instance-level urban scene representation method is introduced to extract instance descriptors via semantic segmentation and DBSCAN, which enable the model to obtain robust instance features, filter dynamic objects and estimate transformation in a more logical manner. Then a lightweight network consisting of MLPs is employed to obtain transformation in an encoder-decoder manner. We validate the approach on KITTI dataset. Experimental results demonstrate that PCRMLP can obtain a satisfactory coarse transformation from instance descriptors just in 0.0028s. With a subsequent ICP refinement module, the proposed method achieves higher registration accuracy and computational efficiency than prior learning-based works.

For the symmetrical mixture model and mixture test area, the lattice point set is used to partition, and then the corresponding test statistics can be constructed. In this paper, we first proposes the partition methods under the lattice point sets and obtains several sub-simplexes without common interior points. Furthermore, we present the method for constructing a uniform design on the simplex using the center points of these sub-simplexes. The designs satisfy the uniformity of maximum distance deviation and provide good results for the mean square error deviation. Finally, the uniformity test on the mixture region is considered and illustrated by examples.

Abstract—Low-frequency Fahræus–Lindqvist-driven (not blood pressure-driven) oscillations in the small vessels are crucial because oscillations in small vessels support nutrient supply. Understanding of this is critical in type 2 diabetes mellitus (T2DM) to develop therapeutic measures to prevent Alzheimer's Disease Related Dementias: vascular factors contribute to cerebrovascular disease as well as mild cognitive impairment and dementia, which are predicted to affect 152 million people by 2050 (Alzheimer's Disease International London, UK, 2019). In this clinical study, we performed functional near-infrared spectroscopy (fNIRS) of the forehead to investigate the effect of the Mini-Cog with three-item recall test on the prefrontal cortex (PFC) activation and the relative oscillatory power in the 0.01–0.02-Hz (Fahræus–Lindqvist effect) and 0.021–0.052 Hz (smooth muscle autonomic innervation) frequency bands in elderly (60 years and older) T2DM and age-matched controls. We found a significant (p<0.01) difference in the PFC activation between elderly subjects with T2DM and age-matched elderly controls. Moreover, power spectral density (PSD) analysis revealed a significantly lower relative power in 0.021–0.052 Hz (smooth muscle autonomic innervation) frequency band in elderly subjects with T2DM during the Mini-Cog three-item recall test. Furthermore, a drop in the oscillatory power in the 0.01–0.02-Hz frequency band during Mini-Cog three-item recall test was found more pronounced in the elderly subjects with T2DM. Therefore, our study highlighted portable brain imaging to capture cerebrovascular reactivity to cognitive load that may provide a biomarker of cerebrovascular dysfunction in T2DM.Clinical Relevance— Our study establishes forehead portable brain imaging for monitoring cerebrovascular function in T2DM under cognitive load.

Objectives: This study was performed to estimate diagnostic accuracy of the two commercially available point-of-care tests to identify poor glycemic control defined by HbA1c levels, with HPLC as a reference. Settings: The study was carried at two locations, general medical out-patient department of a teaching medical college in Bhopal (urban), and a primary health care centre in rural area in the state of Madhya Pradesh, India.Participants: All individuals with diabetes mellitus who presented to the health care facility for assessment of glycemic control. We excluded participants who denied a written informed consent. No other exclusions were used. We compared HbA1c estimated from two index tests (Hemocue Hb501, Sweden; SD Biosensor, South Korea) from capillary blood samples with HPLC performed from venous blood, as a reference standard. Primary and secondary outcome measures: Diagnostic properties of index tests such as sensitivity, specificity, positive and negative predictive value and diagnostic accuracy for identifying poor glycemic control were primary outcome measures. Lin’s concordance correlation coefficient (CCC) was secondary outcome measure.Results: Out of 114 patients, all received reference standard, 103-Hemocue A1C test, and 110- SD Biosensor test. Overall both the index-tests had similar diagnostic accuracy estimates. The area under the Receiver Operating Curve for SDA1c device was 0.935 (95%CI 0.886-0.983), and for Hemocue device was 0.938 (95%CI 0.893-0.984). The Hemocue device HbA1c value of above 7.0 (positive) correctly predicted poor glycemic control 92% times (81.58% for SD device). There were 4 vs. 11 device failures and 14 vs. 12 failures with SD and Hemocue respectively. Ambient air temperatures were no different for the device test failures.Conclusions: Commercially available point-of-care tests evaluated in this study are comparable and an acceptable alternative to HPLC based measurements for assessment of glycemic control. Tests and device failure rates of both the index tests are similar.

Bubble (point defect) – a precursor of fuzz or under dense nanostructure formation is crystal lattice defect. Suitable selection of crystal lattice which inhibit Frenkel pair generation and intrinsically promotes self- interstitial solid solution strengthening contributes effectively towards making plasma facing material. For this, interstitial sites, their size, amount / fraction, positions, tendency of occupation and diffusion parameters (e.g. activation energies (Q), activation volumes) are determined. Fcc iron carbon alloys (austenitic stainless steels AISI / SAE 321, fcc structure, Pearson code cF4, space group Fm3̅m) are proposed as suitable candidates. Along with their room temperature fcc structure having 12 interstitial positions (4 octahedral, 6 coordination sites and 8 tetrahedral, 4 coordination sites / unit cell) to allow insertion of self (iron) atoms, they have excellent corrosion resistance, thermal conductivity, and non- magnetic properties. After their melting, casting, and machining to required dimensions and geometry, stabilizing heat treatment is applied to precipitate all carbon as TiC and prevent formation of Cr23C6 (sensitization). This resist heat and surface degradation and yield excellent architecture which not onlyinhibit Frankel pair generation but will also allow bulk assimilation or surface annihilation (loop punching) of this lattice point defect. A superior thermal, fluid, and structural design augment above. A second choice is presented as Co base superalloys owing to same fcc crystal structure and excellent properties (such as strength, dimensional stability, oxidation resistance) at high temperature.

A novel approach has been suggested to use isoelectric points of viral and human proteins to quickly identify proteins that are effective in not allowing virus particles to attach to human receptor cells by virtue of their electrical charge. The method has been applied to SARS CoV-2 to suggest potentially important human proteins that can be suitable for making anti-viral drugs.

Theoretical analysis into the energetics of a novel putative electromagnetic field propulsion device by the author, found that it was able to impart momenergy to the ground state of the electromagnetic field; some rest-energy of the craft was converted to kinetic energy of the craft. Electrical analysis showed that the propulsor was always a net electrical load – if the device accelerated from one frame, then deaccelerated to the original frame, both processes would consume electrical work. The aim of this paper is to look further into this sinking of high-grade electrical energy into the field ground state and to show that an even more pernicious form of 2^nd Law of Thermodynamics exists.

If an algebra of type $A$ contains a subalgebra which is also an algebra of type $B$, then it is a Smarandache $B$-type $A$-algebra provided the subalgebra of type $B$ contains at least two elements. This generalizes the notion of Smarandache group, where the group of order $\geq 2$ is a subsemigroup of a semigroup. In this paper we investigate a number of such pairings and we deduce a number of conclusions as a consequence.

This paper is devoted to change-point detection using only the ordinal structure of a time series. A statistic based on the conditional entropy of ordinal patterns characterizing the local up and down in a time series is introduced and investigated. The statistic requires only minimal a priori information on given data and shows good performance in numerical experiments.

Sequential hypothesis test and change-point detection when the distribution parameters are unknown is a fundamental problem in statistics and machine learning. We show that for such problems, detection procedures based on sequential likelihood ratios with simple one-sample update estimates such as online mirror descent are nearly second-order optimal. This means that the upper bound for the algorithm performance meets the lower bound asymptotically up to a log-log factor in the false-alarm rate when it tends to zero. This is a blessing, since although the generalized likelihood ratio (GLR) statistics are optimal theoretically, but they cannot be computed recursively, and their exact computation usually requires infinite memory of historical data. We prove the nearly second-order optimality by making a connection between sequential analysis and online convex optimization and leveraging the logarithmic regret bound property of online mirror descent algorithm. Numerical and real data examples validate our theory.

We generalize the point information gain (PIG) and derived quantities, i.e., point information gain entropy (PIE) and point information gain entropy density (PIED), for the case of the Rényi entropy and simulate the behavior of PIG for typical distributions. We also use these methods for the analysis of multidimensional datasets. We demonstrate the main properties of PIE/PIED spectra for the real data on the example of several images, and discuss further possible utilizations in other fields of data processing.

We introduce the notion of generalized contraction on dualistic partial metric spaces. A fixed point theorem for mappings satisfying above mentioned contraction is obtained. Some consequences of our result are obtained. We construct an example to demonstrate the effectiveness of our result among the corresponding results in partial metric spaces. Our results provide substantial generalizations and improvements of several well known results existing in the comparable literature. We discuss an application of our fixed point results to show the existence of solution of functional equations.

With the continuous development of 3D city modeling, traditional close-range photogrammetry is limited by complex processing procedures and incomplete 3D depth information, making it unable to meet high-precision modeling requirements. In contrast, the integration of LiDAR and camera in mobile measurement systems provides a new and highly effective solution. The LiDAR can quickly and accurately acquire the 3D spatial coordinates of target objects, while optical imagery contains abundant color information. If the two can be integrated, they can play an important role in multiple fields such as streetscape modeling, archaeology and digital city, etc. Currently, integrated mobile measurement systems commonly require cameras, lasers, POS and IMU, thus the hardware cost is relatively expensive and the system integration is complex. Therefore, in this paper we propose a simple ground mobile measurement system composed of a LiDAR and a GoPro camera without a POS system, providing a more convenient and reliable way to automatically obtain 3D point cloud data with spectral information. The automatic point cloud coloring based on video images mainly includes four aspects: (1) Establishing models for radial distortion and tangential distortion to correct video images. (2) Establishing a registration method based on normalized Zernike moments to obtain the exterior orientation elements. Normalized Zernike moments are region-based shape descriptors that can reflect the features of images in multiple dimensions even for low-quality video images. The results show that registration based on normalized Zernike moments provides a good result, with an error accuracy of 0.5-1 pixel, which is far higher than registration based on a collinearity equation. (3) Establishing adjacent video image relative orientation based on essential matrix decomposition and nonlinear optimization. This involves uniformly using the SURF algorithm with distance restriction and RANSAC to select corresponding points, which can improve the reliability of the corresponding points. The results indicate that the accuracy of the relative orientation method is high. Moreover, this method can converge to good results for stereo image pairs with large rotation angles and displacement amounts. Therefore, relative orientation based on essential matrix decomposition and nonlinear optimization has good applicability. (4) The video imagery suffers from significant motion blur and boundary distortion. Therefore, a point cloud coloring method based on Gaussian distribution with central region restriction is adopted. Only pixels within the central region are considered valid for coloring. Then, the point cloud is colored based on the mean of the Gaussian distribution of the color set. Experimental results show that the coloring accuracy between the video imagery and point cloud data is high, meeting the accuracy requirements of applications such as tunnel detection, street-view modeling and 3D urban modeling.

An explanation of the origin of dark matter is suggested in this work. The argument is based on symmetry considerations about the concept of mass. In the Wigner's view, the rest mass and the spin of a free elementary particle in flat space-time are the two invariants that characterize the associated unitary irreducible representation of the Poincar\'e group. The Poincar\'e group has two and only two deformations with maximal symmetry. They describe respectively the de Sitter (dS) and Anti de Sitter (AdS) kinematic symmetries. Analogously to their shared flat space-time limit, two invariants, spin and energy scale for de Sitter and rest energy for Anti de Sitter, characterize the unitary irreducible representation associated with dS and AdS elementary systems. While the dS energy scale is a simple deformation of the Poincaré rest energy and so has a purely mass nature, AdS rest energy is the sum of a purely mass component and a kind of zero-point energy derived from the curvature. An analysis based on recent estimates on the chemical freeze-out temperature marking in Early Universe the phase transition quark-gluon plasma epoch to the hadron epoch supports the guess that dark matter energy might originate from an effective AdS curvature energy.

This paper presents a study of the 2D roughness profiles on a flat surface generated on a steel work piece, by ball nose end milling, with linear equidistant tool paths (pick-intervals). The milled surface exploration with a surface roughness tester (on pick and feed directions) produces usually 2D roughness profiles having periodical evolutions. These evolutions can be seen as time-dependent signals, describable as a sum of sinusoidal components (the wavelength of each component being regarded as a period). In order to detect a well approximated description of these components, two appropriate signal processing techniques are used in this work: first technique provides a direct mathematical (analytical) description and is based on computer aided curve (signal) fitting (more accurate), the second technique (providing an indirect and incomplete description) is based on the spectrum generated by fast Fourier transform (less accurate). This study can be seen as a way for a better understanding the interaction between the tool and the work piece or to achieve a mathematical description of the machined surface microgeometry in terms of roughness (e. g. its description being a collection of closely spaced 2D roughness profiles).

This study introduces a fruit harvesting mechanism powered by a single motor, designed for integration with unmanned aerial vehicles (UAVs). The mechanism performs reciprocating motion by converting linear motion into rotational motion. Consequently, the end-effector can execute multi-dimensional kinematic trajectories, including biaxial and rotational movements, synchronized with the motor's position. These axial and rotational motions facilitate the gripper's ability to reach, retrieve, and detach fruit from branches during the harvesting process. Notably, a critical consideration in designing this fruit harvesting mechanism is to generate the necessary torque at the end-effector while minimizing reaction forces and torque that could destabilize the UAV during flight. With these considerations in mind, this preliminary study aimed to harvest a Fuji apple and conducted dynamic analysis. We constructed a prototype of the single motor-driven fruit harvesting mechanism using a suitable servo motor. To assess its mechanical performance and evaluate its impact on the hexacopter, we developed both a specific test platform featuring a six-spherical-prismatic-spherical parallel structure and a virtual environmental flight simulator. Overall, the results demonstrate the successful harvesting of a Fuji apple weighing approximately 300 g by the single motor-driven fruit harvesting mechanism, with no adverse effects observed on the hexacopter's operation.

In contrast to some low molar-mass per- and polyfluoroalkyl substances (PFASs), well-established to be toxic, persistent, bioaccumulative and mobile, fluoropolymers (FPs) are water insoluble, safe, bio-inert, durable niche high performance polymers which fulfil the 13 polymer of low concern (PLC) criteria in their recommended conditions of use. In addition, more recent innovations (e.g., the use of non-fluorinated surfactants in aqueous radical (co)polymerization of fluoroalkenes) from industrial manufacturers of FPs are highlighted. This review also aims at showing how these specialty polymers endowed with outstanding properties are essential (even irreplaceable since hydrocarbon polymer alternatives used in similar conditions fail) for our daily life (electronics, Energy, optics, internet of things, transportation, etc) and constitute a special family a part from other “conventional” C1-C10 PFASs found everywhere on the Earth and Oceans. Furthermore, some information reports the recycling (e.g. the unzipping depolymerization of polytetrafluoroethylene, PTFE, into TFE), end of life of FPs, their risk assessment, circular economy and regulations. Various researches are devoted to Environment involving FPs, though they represent a niche volume (with a yearly production of 330,300 tonnes) compared to all plastics (with 460 million tonnes). Complementary to other reviews on PFASs, which lack of such above data, this review presents both fundamental and applied strategies as evidenced by major FP producers.

(1) Background: This article reviews the available scientific literature on drug-related problems and negative outcomes associated with medications identified by medication review with follow-up for end-stage renal disease and discussed with the physicians; (2) Methods: A systematic review was conducted of the scientific literature retrieved from the following databases: MEDLINE (via PubMed), Web of Science, SCOPUS, Cochrane Library: The Cochrane Central Register and Control Trials (CENTRAL) and Literatura Latinoamericana y del Caribe (LILACS), Medicina en Español (MEDES), and the SciELO bibliographic database (collection of scientific journals). The following terms were used as descriptors and searched in free text: "end-stage renal disease", "medication review", and "drug-related problems” and “negative outcomes associated with medication". The following limits were applied: "humans" and "adults (more than 18 years)”; (3) Results: A total of 59 references were recovered and after applying inclusion/exclusion criteria, 16 articles were selected. Of these selected articles, 15 provided information on drug-related problems and only 1 on negative outcomes associated with medications; (4) Conclusions: It can be concluded that drug-related problems and negative outcomes associated with medication affect patients with end-stage renal disease, mainly those receiving renal replacement therapy. More evidence is needed, especially on negative outcomes associated with medication.

Industrial data scarcity is one of the largest factors holding back the widespread use of machine learning in manufacturing. To overcome this problem, the concept of transfer learning was developed and it achieved high attention in recent industrial research. Our paper focuses on the problem of time series segmentation and presents the first in-depth research about transfer learning for deep-learning based time series segmentation on the example of industrial end-of-line pump testing. In particularly, we investigate if the performance of deep learning models can be increased by pretraining the network with data from other domains. Three different scenarios are analyzed: source and target data being closely related, source and target data being distantly related, and source and target data being non-related. The results demonstrate that transfer learning can enhance the performance of time series segmentation models in respect to accuracy and training speed. The benefit is most clearly seen in scenarios where source and training data are closely related and the number of target training data samples is lowest. However, in the scenario of non-related datasets, cases of negative Transfer Learning were observed as well. Thus, the research emphasizes the potential, but also the challenges of industrial Transfer Learning.

Patients living with cancer often experience serious adverse events due to their condition or its treatments. Those events may lead to a critical care unit admission or even result in death. One of the most important but challenging part of care is to build a caring plan according to the patient’s wishes, meeting his goals and values. Advance directives (ADs) allow everyone to give their preferences in advance regarding life sustaining treatments, continuation, and withdrawal or withholding of treatments in case one is not able to speak his mind anymore. While the absence of ADs is associated with a greater probability of receiving unwanted intensive care around the end of his life, their existence correlates with the respect of the patient’s desires and his greater satisfaction. Although progress has been made to promote ADs’ completion, they are still scarcely used among cancer patients in many countries. Several limitations to their acceptation and use can be detected. Efforts should be made to provide tailored solutions for the identified hindrances. This narrative review aims to depict the situation of ADs in the oncology context, and to highlight the future areas of improvement.

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

Background. Pregnancy rates in women on dialysis have increased in the last decades, thus making it a topic of growing interest. The rarity of this event is predominantly due to fertility problems and to the high rates of pregnancy failure including stillbirth, fetal and neonatal deaths [1]. Methods. We conducted a narrative review of existent literature in order to analyse the major issues about pregnancy on dialysis to give the reader a full updated prospective about this topic which, even if not common, is becoming more and more frequent. Results. Even if recently acquired knowledge has improved diagnosis and treatment of dialysis pregnancies focusing on several aspects, pregnancy on dialysis remains a great challenge for obstetricians and should be managed by a multidisciplinary expertise team. Conclusion. Dialysis in pregnancy may be necessary for women previously affected by end stage renal disease (ESRD) becoming pregnant, or in case of acute renal injury presenting for the first time during gestation or, again in case of existent renal pathology worsening during pregnancy and requiring dialysis. Although some evidence suggests that more intensive dialysis regimens are correlated with better obstetric outcomes, the optimal therapeutic protocol still remains to be established.

Recently, significant events took place that added immensely to the sociotechnical pressure for developing sustainable composite recycling solutions, namely (1) a ban on composite landfilling in Germany in 2009, (2) the first major wave of composite wind turbines reaching their End-of-Life (EoL) and being decommissioned in 2019-2020, (3) the acceleration of aircraft decommissioning due to the COVID-19 pandemic, and (4) the increase of composites in mass production cars, thanks to the development of high volume technologies based on thermoplastic composites. Such sociotechnical pressure will only grow in the upcoming decade of 2020s as other countries are to follow Germany by limiting and banning landfill options, and by the ever-growing number of expired composites EoL waste. The recycling of composite materials will therefore play an important role in the future, in particular for the wind energy, but also for aerospace, automotive, construction and marine sectors to reduce environmental impacts and to meet the demand. The scope of this manuscript is a clear and condensed yet full state-of-the-art overview of the available composite recycling technologies of both low and high Technology Readiness Levels (TRL). TRL is a framework that has been used in many variations across industries to provide a measurement of technology maturity from idea generation (basic principles) to commercialization. In other words, this work should be treated as a technology review providing guidelines for the sustainable development of the industry that will benefit the society. The authors propose that one of the key aspects for the development of sustainable recycling technology is to identify the optimal recycling methods for different types of composites. Why is that the case can be answered with a simple price comparison of E-glass fibers (~2 $/kg) versus a typical carbon fiber on the market (~20 $/kg) – which of the two is more valuable to recover? However, the answer is more complicated than that – the glass fiber constitutes about 90% of the modern reinforcement market, and it is clear that different technologies are needed. Therefore, this work aims to provide clear guidelines for economically and environmentally sustainable End-of-Life (EoL) solutions and development of the composite material recycling.

Not a lot is known about either death or the dying process. Politicians and many in the medical profession in the UK tend to shy away from interfering with it by not allowing euthanasia as an end of life option for the patient. This is the first paper in a series of two, comparing the situation in human medicine and veterinary medicine, in which euthanasia is well practiced for relieving suffering at the end of an animal’s life. This first part takes the form of a literature review including best practice around end of life care, its deficiencies and the need for assisted dying. Veterinary surgeons are well trained in the ethics of euthanasia and put it to good use in the best interest of their animal patients. In countries which have legalized physician assisted suicide for the terminally ill reporting indicates that it works well, without increases in involuntary euthanasia and most importantly without intimidation of the vulnerable. However, there is still an ever increasing tendency to overuse sedation and opioids at the end of life, which merits further investigation. With advances in medical science able to significantly prolong the dying process, patient autonomy demands a review of the law in the UK.

The rapid increase in metabolic diseases occurred in the last three decades in both industrialized and developing countries has been related to the rise in sugar-added foods and sweetened beverages consumption. An emerging topic in the pathogenesis of metabolic diseases related to modern nutrition is the role of Advanced Glycation Endproducts (AGEs). AGEs can be ingested with high temperature processed foods, but also endogenously formed as consequence of a high dietary sugars intake. Animal models of high sugars consumption, in particular fructose, have reported AGEs accumulation in different tissues in association with peripheral insulin resistance and lipid metabolism alterations. The in vitro observation that fructose is one of the most rapid and effective glycating agent when compared to other sugars has prompted the investigation of the in vivo fructose-induced glycation. In particular, the widespread employment of fructose as sweetener has been ascribed by many experimental and observational studies for the enhancement of lipogenesis and intracellular lipid deposition. Indeed, diet-derived AGEs have been demonstrated to interfere with many cell functions such as lipid synthesis, inflammation, antioxidant defences, and mitochondrial metabolism. Moreover, emerging evidences also in humans suggest that this impact of dietary AGEs on different signalling pathways can contribute to the onset of organ damage in liver, skeletal and cardiac muscle, and brain, affecting not only metabolic control, but global health. Indeed, the here reviewed most recent reports on the effects of high sugars consumption and diet-derived AGEs on human health suggest the need to limit the dietary sources of AGEs, including added sugars, to prevent the development of metabolic diseases and related comorbidities.

The Republic of Venice was one of the longest-lived states in modern history, and its stability and survival has been studied through many different angles, and one of them is to try and find pivotal moments in its history that explain it, or its eventual demise. In this paper, through the rigorous statistical analysis of the dataset of marriages by nobles in the Republic, we will try to define a methodology for the detection of these events through mono and multivariate change point analysis, and validate that methodology through cross-validation of different procedures, as well as matching the results to historical events. Our analysis shows that these change points occur with statistical significance and that they match political and historical events. These results can be built upon for a better understanding of the historical causes of the success and failure of the Republic of Venice and, by extension, other states.

Myofascial Pain Syndrome (MPS) is a prevalent chronic pain disorder characterized by myofascial trigger points (MTrPs). Current diagnosis relies on manual detection of MTrPs, which has low inter-rater reliability. This study aims to enhance diagnosis through machine learning (ML) and a combination of texture-features extracted from B-mode ultrasound (B-mode-US) images. Four texture-features were investigated: statistical-features, and their combination with Gabor, local binary (LBP), and SEGL, LBP + gray-level-co-occurrence-matrices + Edge for the classification of MTrPs on the B-mode-US images. B-mode-US images of trapezius muscles (n=90) were examined for MTrPs and healthy muscle. Three methods of LBP, SEGL, and Gabor were separately calculated for each B-mode-US image. Then, the statistical-features (e.g., entropy) were calculated over the B-mode-US images and those three methods. Seven Machine learning (ML) classifiers (e.g., neural network) with the calculated statistical-features were applied to discriminate MTrPs from healthy muscle. Additionally, traditional statistical analysis (i.e., ANOVA) was calculated between statistical-features within each method. The results indicated that the combination of statistical-features effectively discriminated between MTrPs and healthy muscles, based on traditional statistical analysis. However, ML classifiers struggled due to high variation and similar mean values among them. This discrepancy between them prompts further exploration of US texture-based automated ML for MTrPs diagnosis.

This work presents the synthesis and self-organization of the calamitic fluorinated mesogen, 1,1,2,2-tetrafluoro-2-(1,1,2,2-tetrafluoro-4-iodobutoxy)ethanesulfonic acid, a potential model for perfluorosulfonic acid membranes (PFSA). The compound is derived in three steps from 1,1,2,2-tetrafluoro-2-(1,1,2,2-tetrafluoro-2-iodoethoxy)ethanesulfonyl fluoride, achieving an 78% overall yield. The resulting compound exhibits intricate thermal behavior. At 150 °C, a crystal-to-crystal transition is observed due to the partial disordering of calamitic molecules, which is followed by isotropization at 218 °C. Upon cooling, sample ordering occurs through the formation of large smectic liquid crystalline phase domains. This thermotropic state transforms into a layered crystal phase at lower temperatures, characterized by alternating hydrophilic and hydrophobic layers. Using X-ray diffraction, crystalline unit cell models at both room temperature and 170 °C were proposed. Computer simulations of the molecule across varying temperatures support the idea that thermal transitions correlate with a loss of molecular orientation. Importantly, the study underscores the pivotal role of precursor self-organization in aligning channels during membrane fabrication, ensuring controlled and oriented positioning.