Hearing dysfunctions can be classified by type, degree, configuration, time of onset, aetiology, and finally, consequences on speech development. They can be divided into conductive, mixed, central types and sensorineural. Conductive hearing loss (CHL) results from interference with the mechanical transmission of sound through the external and middle ear; it can be congenital, as a consequence of anatomic abnormalities, but it can commonly be acquired following middle ear inflammatory pathologies. Sensorineural hearing loss (SNHL) results from failure to transduce vibrations to neural impulses in the cochlean and is a consequence of an irreversible damage to the differentiated cells which make up the organ of hearing and the acoustic paths at various levels. Mixed hearing loss involves a combination of these two types in the same ear. Studies in neuroscience field have shown that the prevention of cell degeneration is only possible if all the factors taken at the different stages of stem cells’ multiplication and differentiation are administered together. We have demonstrated this in a recent study on the ability of SCDSFs to prevent neurodegeneration in hippocampal cells of the CA1 zone in mice. This study confirms previous findings demonstrating that early developmental zebrafish embryo extracts could act as a modulator of senescence in human mesenchymal stem cells (hMSC) isolated from many adult tissues. These findings have open a promising way for the approaches promoting the rejuvenation and regeneration of different tissues, by-passing stem cell transplantation. In the present clinical trial we have used SCDSFs to study the possible reversion of neurosensory hearing loss, until now considered an irreversible condition.

Remote sensing/GIS techniques are a versatile tool for x-raying serial forest structural changes in retrospect. It would be impossible to evaluate past occurrences and changes in forest extents in past decades at Effan Forest Reserve without non-conventional means. Therefore, we adopted remote sensing technology using Landsat images to evaluate land-use change and degradation rates in the area with a view to ascertaining causal factors for possible minimization of forest degradation in Effan Forest Reserve. Land-use/land-cover changes were analyzed using USGS-Landsat TM and ETM images of 1987, 2002, 2014 and 2019. Field-data were collected using handheld GPS receiver and spatial statistical analyses were conducted using the ground control points (GCPs). For inventory data, a systematic sampling technique was adopted using ten 1.05 km-transects at 500 m intervals. A total of 50 sample plots of 50 × 50 m were used. All tree species with Dbh ≥10 cm were enumerated. Nineteen tree species in ten families were encountered with Vitellaria paradoxa as the most-frequently occurring species in the area. IUCN-listed endangered Pterocarpus erinaceus, hitherto abundant in the area, was rarely encountered during the survey, while Vitellaria paradoxa is gradually shrinking, going the relative abundance in the area. The result further showed that primary and secondary forests decreased considerably by 258.03 ha (46.72%) and 9.18 ha (3.63%), respectively, with a total forest loss of 50.3% in 32 years (8.4 hayr-1, 1.6% per annum). While forest plantation size doubled by 369.72 ha within the period. This is worrisome as the remaining fragmented forests appeared to be on the decline, except the riparian vegetation, due to inaccessibility to the riparian by loggers. It thus appeared that forest protection approaches were ineffective. Increased protection efforts could save this forest reserve, and the concerned authority should consider a focused-enrichment planting involving indigenous species for ecosystem-repair.

The quantity of soil loss as a result of soil erosion is dramatically increasing in catchment where land resources management is very weak. In this paper, a RUSLE model-based soil loss quanti-fication technique is presented to estimate the annual soil loss and identify the severity of the erosion in the catchment. This study uses Fincha catchment in Abay river basin as the study area to quantify the annual soil loss by implementing Revised Universal Soil Loss Equation (RUSLE) model developed in ArcGIS version 10.4. Digital Elevation Model (12.5 x 12.5), LANDSAT 8 of Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Annual Rainfall of 10 stations and soil maps of the catchment were used as input parameters to generate the significant factors. Rainfall erosivity factor (R), soil erodibility factor (K), cover and management factor (C), slope length and steepness factor (LS) and support practice factor (P) were used as soil loss quantification significant factors. A model builder for the RUSLE model was developed and raster map calcula-tion algebra was applied in ArcGIS version 10.4 to quantify the total annual soil loss. It was found that the quantified average annual soil loss ranges from 0.0 to 76.5 t ha-1 yr-1 was obtained in the catchment. The area coverage of soil erosion severity with 55%, 35% and 10% as low to moderate, high and very high respectively were identified. The information about the spatial variation of soil loss severity map generated in RUSLE model has a paramount role to alert land resources man-agers and all stakeholders in controlling the effects via implementation of both structural and non-structural mitigations. The results of the RUSLE model can also be further considered along with the catchment for practical soil loss quantification that can help for protection practices.

Quantum noise limits the sensitivity of laser interferometric gravitational-wave detectors. Given the state-of-the-art optics, the optical losses define the lower bound of best possible quantum-limited detector sensitivity. In this work, we come up with the configuration which allows to saturate this lower bound by converting the signal recycling cavity to be a broadband signal amplifier using an active optomechanical filter. We will show the difference and advantage of such a broadband signal recycling scheme compared with the previous white-light-cavity scheme using the optomechanical filter in [Phys.Rev.Lett.115.211104 (2015)]. The drawback is that the new scheme is more susceptible to the thermal noise of the mechanical oscillator. To suppress the radiation pressure noise which rises along with the signal amplification, squeezing with input/output filter cavities and heavier test mass are used in this work.

Foveal vision loss has been shown to reduce efficient visual search guidance due to contextual cueing by incidentally learned contexts. However, previous studies used artificial (T among L-shape) search paradigms that prevent the memorization of a target in a semantically meaningful scene. Here, we investigated contextual cueing in real-life scenes that allow explicit memory of target locations in semantically rich scenes. In contrast to the contextual cueing deficits in artificial scenes, contextual cueing in patients with age-related macular degeneration (AMD) did not differ from age-matched normal-sighted controls. We discuss this in the context of visuospatial working memory demands for which both eye-movement control in the presence of central vision loss and for memory-guided search may compete. Memory-guided search in semantically rich scenes may depend less on visuospatial working memory than search in abstract displays, potentially explaining intact contextual cueing in the former but not the latter. In a practical sense, our findings may indicate that Patients with AMD are less deficient than expected after previous lab experiments. This shows the usefulness of realistic stimuli in experimental clinical research.

Amyloids are fibrillar protein aggregates that are associated with diseases such as Alzheimer’s disease, Parkinson’s disease, type II diabetes and Creutzfeldt–Jakob disease. The process of amyloid aggregation involves three pathological protein transformations; from natively-folded conformation to the cross-β conformation, from biophysically soluble to insoluble, and from biologically functional to non-functional. While amyloids share a similar cross-β conformation, the biophysical transformation can either take place spontaneously via a homogeneous nucleation mechanism (HON) or catalytically on an exogenous surface via a heterogeneous nucleation mechanism (HEN). Here, we postulate that the different nucleation pathways can serve as a mechanistic basis for an etiological classification of amyloidopathies, where hereditary forms generally follow the HON pathway, while sporadic forms follow surface-induced (including microbially-induced) HEN pathways. Furthermore, the conformational and biophysical amyloid transformation results in loss-of-function (LOF) of the original natively-folded and soluble protein. This LOF can, at least initially, be the mechanism of amyloid toxicity even before amyloid accumulation reaches toxic levels. By highlighting the important role of non-protein species in amyloid formation and LOF mechanisms of toxicity, we propose a generalized mechanistic framework that could help better understand the diverse etiology of amyloid diseases and offer new opportunities for therapeutic interventions including replacement therapies.

An appearance model plays a crucial rule in multi-target tracking. In traditional approaches, the two steps of appearance modeling i.e visual representation and statistically similarity measure are modeled separately. Visual representation is achieved either through hand-crafted features or deep features and statically similarity is measure through a cross entropy loss function. A loss function based on cross-entropy (KL-divergence, mutual information) find closely related probability distribution for the targets. However, if the targets have similar visual representation, it ends up mixing the targets. To tackle this problem, we come up with a synergetic appearance model named Single Shot Appearance Model based on Siamese neural network. The network is trained with a contrastive loss function for finding the similarity between different targets in a single shot. The input to the network is two target patches and based on their similarity, a contrastive score is output by the network. The proposed model is evaluated on accumulative dissimilarity metric on three datasets. Quantitatively, promising results are achieved against three baseline methods.

Recently, generative adversarial networks (GANs) can be used to generate the photo-realistic image from a low-dimension random noise. It is very dangerous that the synthesized or generated image is used on inappropriate contents in social media network. In order to successfully detect such fake image, an effective and efficient image forgery detector is desired. However, conventional image forgery detectors are failed to recognize the synthesized or generated images by using GAN-based generator since they are all generated but manipulation from the source. Therefore, we propose a deep learning-based approach to detect the fake image by combining the contrastive loss. First, several state-of-the-art GANs will be collected to generate the fake-real image pairs. Then, the contrastive will be used on the proposed common fake feature network (CFFN)to learn the discriminative feature between the fake image and real image (i.e., paired information). Finally, a smaller network will be concatenated to the CFFN to determine whether the feature of the input image is fake or real. Experimental results demonstrated that the proposed method significantly outperforms other state-of-the-art fake image detectors.

Traumatic brain injury (TBI) is a major source of worldwide morbidity and mortality. Patients suffering from TBI exhibit a higher susceptibility to bone loss and an increased rate of bone fractures; however, the underlying mechanisms remain poorly defined. Herein, we observed significantly lower bone quality and elevated levels of inflammation in bone and bone marrow niche after controlled cortical impact-induced TBI in in-vivo CD-1 mice. Further, we identified dysregulated NFB signaling, an established mediator of osteoclast differentiation and bone loss, within the bone marrow niche of TBI mice. Ex vivo studies revealed increased osteoclast differentiation in bone marrow-derived cells from TBI mice, as compared to sham injured mice. Finally, we found bone marrow derived extracellular vesicles (EVs) from TBI mice enhanced the colony forming ability and osteoclast differentiation efficacy of bone marrow cells and activated NFB signaling genes in bone marrow-derived cells. Taken together, we provide evidence that TBI-induced inflammatory stress on bone and the bone marrow niche may activate NFB leading to accelerated bone loss. Targeted inhibition of these signaling pathways may reverse TBI-induced bone loss and reduce fracture rates.

Magnetic nanoparticles (MNPs) have attracted a great interest in nanomedical research. MNPs exhibit many important properties, particularly, magnetic hyperthermia for selective killing of cancer cells is one of them. In hyperthermia treatment, MNPs act as nano-heaters when they are under the influence of an alternating magnetic field (AMF). In this work, micromagnetic simulations have been used to investigate the magnetization dynamics of a single-domain nanoparticle of magnetite in an external AMF. Special attention is paid to the circumstances dealing with a dynamic phase transition (DPT). Besides, we focus on the influence of the orientation of the magnetic easy-axis of the MNP on the dynamic magnetic properties. For amplitudes of the external AMF above certain critical value, the system is not able to follow the magnetic field and it is found in a dynamically ordered phase; whereas for larger amplitudes, the state corresponds to a dynamically disordered phase and the magnetization follows the external AMF. Our results suggest that the way how the order-disorder DPT takes place, and both the metastable lifetime as well as the specific loss power (SLP), are strongly dependent on the interplay between the orientation of the magnetic easy-axis and the amplitude of the external AMF.

Keywords: Obesity; hypothalamus; appetite; glucose homeostasis; weight-loss drugs; AGRP; POMC; NTS; incretins.

Wet anaerobic storage of algal biomass is a promising preservation approach which can ensure continuous supply of these feedstocks to biorefineries year-round. An effective solution to preservation must ensure minimal dry matter loss and change in biochemical composition during storage. Therefore, the objective of this study was to investigate the preservation of Nannochloropsis gaditana biomass through wet anaerobic storage and its impact on biomass quality. Prior to storage, algae sample was inoculated with two different strains of lactic acid bacteria and thereafter stored for 30 and 180 days. Each inoculant limited dry matter loss to <10% (dry basis) after the storage durations. Final pH values (4.3 - 4.8) indicate that the biomass samples were properly ensiled, achieving the acidic conditions necessary for preservation. Compositional analysis of the biomass after storage showed a reduction in carbohydrate content, a relative increase in lipid content, and no significant change to the protein fraction. Glucose and galactose were the prevalent sugar monomers. The low dry matter loss and minimal compositional change indicates that wet anaerobic storage is an effective means of preserving algal biomass, ensuring a constant supply of algal biomass feedstock to a biorefinery.

Rosavin, a phenylpropanoid in Rhodiola rosea's rhizome, an adaptogen, is known for enhancing the body's response to environmental stress. It significantly affects cellular metabolism in health and many diseases, particularly influencing bone tissue metabolism. In vitro, rosavin inhibits osteoclastogenesis, disrupts F-actin rings formation, and lowers osteoclastogenesis-related gene expression (cathepsin K, CTR, TRAF6, TRAP, MMP-9). It also impedes NFATc1, c-Fos, NF-κB, and MAPK signaling pathways, and blocks phosphorylation processes crucial for bone resorption. Moreover, rosavin promotes osteogenesis, osteoblast differentiation and increases Runx2 and OCN expression. In vivo studies show its effectiveness in enhancing BMD in PMOP mice, re-straining osteoclast maturation, and increasing active osteoblast percentage in bone tissue. It modulates mRNA expressions (increasing EEF2 and decreasing HDAC1, thereby activating os-teoprotective epigenetic mechanisms) and alters various serum markers, including decrease of CTX-1, TRAcp5b, RANKL, M-CSF, TRAP, and increase of ALP and OCN. Additionally, when combined with zinc and probiotics, it reduces pro-osteoporotic MMP-3, IL-6, TNF-α, and en-hances anti-osteoporotic IL-10 and TIMP3 expressions. This paper aims to systematically review rosavin's impact on bone tissue metabolism, exploring its potential in osteoporosis prevention and treatment, and suggesting future research directions.

The eddy current loss should be optimized to be as less as possible for the stability of permanent magnet in high speed permanent magnet synchronous motor (HSPMSM) rotor and ensure the high efficiency and low temperature of the motor. This paper analyzes the eddy current distribution in rotor, with consideration of the conflict of the thickness of sleeve and diameter of the rotor, calculating the eddy current loss (ECL) and the thermal distribution via Separation of variables method for solving Maxwell's equations with analytical hieratical model of ECL constructed. The optimization result of ECL of the HSPMSM whose power and rated speed is 30kw 48000r/min can be got by multi-objective optimization method, combined weighting coefficient method and traversal algorithm based on chaotic local search particle swarm optimization (CLSPSO), utilizing ECL analytical model and other analytical constraints. Related experiment and measurement has been implemented with new approach of loss separation.

The effective management of Fusarium wilt of bananas (FW) depends on the knowledge of the disease dynamics in time and space. The objectives of this work were: To estimate disease intensity and impact, and to investigate the spatial and temporal dynamic of FW. Fields planted with Silk (n = 10), Pome (n = 17) or Cavendish (n = 3) banana subgroups were surveyed in Brazil, totaling 95 ha. In each field, all plants were visually assessed and diseased plants were georeferenced. The incidence of FW and the impact of the disease on yield on a regional scale were estimated. Spatial patterns were analyzed using quadrat- and distance-based methods. FW incidence ranged from 0.09 to 41.42%, being higher in Silk fields (median = 14.26%). Impacts of epidemics on yield ranged from 18.4 to 8,192.5 kg.ha-1.year-1, with a median of 935.2 kg.ha-1.year-1. The higher economic impact of the disease was observed on Silk cultivar with a median loss of US$ 910.5 ha-1.year-1. Overall, estimated losses increased on average by US$ 109.8 ha-1.year-1 at each 1% of incidence. Aggregation of FW was detected by all analytical methods in 13 fields (1 of Cavendish, 11 of Pome and 1 of Silk). In the other 17 fields, at least one analytical method did not reject the null hypothesis of randomness. One field (5 ha), composed of six plots, was selected for spatial and temporal studies during two years with bi-monthly assessments. A sigmoidal curve represented the FW progress and the Gompertz model best fitted disease progress. The level of aggregation varied over time, and evidence of secondary infection to neighboring and distant plants were detected. FW is a widespread problem in Brazil and yield losses can be of high magnitude. Epidemiology-based management strategies can now be better established.

In this paper, under the symmetric entropy and the scale squared error loss functions, we consider the maximum likelihood (ML) estimation and Bayesian estimation of the Shannon entropy and Rényi entropy of the two-parameter inverse Weibull distribution. In the ML estimation, the dichotomy is used to solve likelihood equation. In addition, the approximation confidence interval is given by the Delta method. Because the form of estimation results is more complex in the Bayesian estimation, the Lindley approximation method is used to achieve the numerical calculation. Finally, Monte Carlo simulations and a real data set are used to illustrate the results derived. By comparing the mean square error between the estimated value and the real value, it can be found that the performance of ML estimation of Shannon entropy is better than that of Bayesian estimation, and there is no significant difference between the performance of ML estimation of Rényi entropy and that of Bayesian estimation.

Breakage and damage of fiber optic cable fibers seriously affect the normal operation of fiber optic networks and it is important to quickly and accurately determine the type and location of faults when they occur. The advantages of wavelet transform in singular signal detection and signal filtering are used to analyze Optical Time Domain Reflectometer (OTDR) curve signal and the fault detection method of fiber communication link with no relay and large span in high altitude area is given, which realizes the accurate detection and location of optical fiber communication link fault events under strong noise. The theoretical analysis is basically consistent with the experimental data. The accuracy of optical fiber fault location has been greatly improved in performance. The method has been directly applied to the field detection of ultra-long optical fiber links in high altitude areas, which has certain significance for the future real-time monitoring of optical fiber cables.

In this work, we present an analytical model of dynamic power losses for enhancement-mode AlGaN/GaN high-electron-mobility transistor power devices (eGaN HEMTs). To build this new model, the dynamic on-resistance (Rdson) is first accurately extracted by our ex-traction circuit based on a double-diode isolation (DDI) method for a high operating frequency up to 1 MHz and a large drain voltage up to 600 V, thus the unique problem of an increase in the dynamic Rdson is presented. Then the impact of the current operation mode on the on/off transi-tion time is evaluated by a dual-pulse-current-mode test (DPCT) which including a discontinu-ous conduction mode (DCM) and a continuous conduction mode (CCM), thus the transition time is revised for different current mode. Afterward the discrepancy between the drain current and the real channel current is qualitative investigated by an external shunt capacitance (ESC) meth-od, thus the losses due to device parasitic capacitance are also taken into account. After these improvements, the dynamic model will be more compatible for eGaN HEMTs. Finally, the dynamic power losses calculated by this model are verified to be in good agreement with experimental results. Based on this model, we propose a superior solution with qua-si-resonant mode (QRM) to achieve lossless switching and accelerate switching speed.

In the last few decades, glaucoma became the second biggest leading cause of irreversible vision loss. Because of its asymptotic growth, it is not properly diagnosed until the relatively late stage. To stop the severe damage by glaucoma it is needed to detect glaucoma in its early stages. Surprisingly diabetes also be the greatest cause of glaucoma. In the modern era, artificial intelligence makes great progress in the medical image processing field. Image analysis based on machine learning gives a huge success in diagnosis glaucoma without any misdiagnosis. The aim of this proposed paper is to create an automated process that can detect glaucoma and diabetic retinopathy. Here various Machine Learning models are used and results of these methods are presented.

This paper presents the design of a high frequency zero voltage switching (ZVS) full-bridge converter with a phase-shifted driving signal for photovoltaic applications. The resonant power converter can provide high-power capacity under high-frequency operation. The proposed power converter can also reduce the size of the transformer under the same power rating. The high-frequency transformer was developed by using the resonant and switching frequencies of the power converter to reduce the switching loss and to improve the system efficiency. Phase-shifted modulation was selected to drive the switches of a full-bridge power converter based on the switching loss minimization method. The desired output voltage was controlled using a closed-loop controller under a loop gain stability margin. The simulation results showed that the output voltage can be controlled to the desired constant when the input voltage changes from 30 VDC to 60 VDC. The desired output voltage of power converter is constant at 400 VDC. The power converter can transfer the DC supply to a 220 VAC household via grid-connected inverter. Therefore, the proposed study showed the effectiveness of the phase-shift ZVS full-bridge power converter with high-frequency transformer. This power converter can control the operation of the desired voltage system and has a small sizing of power converter system, low switching loss, and high system efficiency.

Drought stress is a major production constraint in crops globally. Crop wild relatives are important source of resistance and tolerance for both biotic and abiotic stresses, respectively. A breeding program was initiated to introgress drought tolerance in sunflower through hybridization between the wild species Helianthus argophyllus and the cultigen pool of H. annuus. Selection was carried out in the F2 to F5 segregating populations for high cuticular waxes, smaller leaf area, single heading and high oil content. The selected F5 breeding lines were compared with non-adapted elite sunflower germplasm in a replicated randomized complete block research project using different water treatments comparing fully irrigated (T0), with 75 (T1), 50 (T2) and 25% (T3) irrigation treatments. The comparison between the two types of germplasm showed that drought tolerant breeding lines had comparatively lesser decreases in leaf area, shoot weight and root shoot ratio 10 under the various drought treatments. Leaf area of the drought selected F5 lines were smaller than that of the elite germplasm under controlled irrigated treatments, but they maintained their leaf area under the drought treatments. Several drought resistant promising lines D-2, D-5, and D-27 were identified and showed high leaf area, great root length and increase root to shoot ratio under the highest stressed treatments (T3). Some of the lines could be directly used for the development of drought tolerant hybrids. Combining ability test indicated D-27 (F7) as good general combiner for seed yield plant−1 and oil contents when mated with male line RSIN.82. These hybrids could help to minimize seed yield losses due to water stress and to achieve profitable cultivation of sunflower in 18 arid regions of Pakistan.

Monte Carlo Filtering (MCF) is one of the methods of Experimental Statistical Energy Analysis (E-SEA), which allows the correction of a negative LF (Loss Factor). In this article, a modification of the MCF method, called DESA (Diagonal Extension of the Search Area), is proposed. The technique applies a non-uniform extension of the search area when generating a population of normalized energy matrices. The degree of expansion of the search area is controlled by the Diagonal Penalty Factor (DPF). The authors demonstrated the method's effectiveness on a system that could not be identified in several frequency bands by the classical MCF method. After applying DESA, it was possible to fill in the problematic bands that were missing CLF and DLF values. The paper also proposes a way to minimize the errors introduced by using overly high DPF values.

In dermatological practice, disturbances of the skin microbiome are found in various infectious and inflammatory dermatoses featuring one or more skin lesions with loss of tissue, directly exposing skin layers to microbial invasions. Literature data on the incidence of cutaneous-onset sepsis are, however, scarce. This retrospective observational study assessed hospital admissions for primary skin lesions with vs. without bacterial infections and sepsis during 2020-2022 in the largest emergency hospital in NE Romania. Of 509 patients 441 had infected lesions, 78 had sepsis caused by venous ulcers microbial eczema cellulitis, superinfected bullous dermatoses, erysipelas, erythroderma. Cultured samples revealed S. aureus, P. aeruginosa, E. coli, with K. pneumoniae, S. β-hemolytic associated with sepsis even if rarer. Clinical manifestations included ulcerations, erosions, fissures, excoriations, bullae, vesicles, pruritus, tumefaction, edema, fever, chills, pain, adenopathy, mildly altered mental status. Underlying chronic heart failure, atrial fibrillation, anemia, type-1 diabetes mellitus were comorbidities associated with infection and sepsis. Significant associations and risk factors, including their combined effects, are discussed to draw attention to the need for further research and adequate management to prevent sepsis in adult patients of any age presenting with infected skin lesions (especially cellulitis) and comorbidities (especially type 1 diabetes mellitus and anemia).

Background: Retinal diseases such as neovascular age related macular degeneration (nAMD), diabetic macular edema (DME) or branch-/central retinal vein occlusion (B/CRVO) have significant implications for patients' social and psychological well-being. To gain a comprehensive understanding of patients' backgrounds and attitudes, we developed an exploratory questionnaire, the Basic Care and Patient Satisfaction Questionnaire (BPZ-9). Methods: The ALBATROS study aimed to assess the care situation of treatment-naïve patients diagnosed with either nAMD, DME or B/CRVO. The data collection took place at the beginning of treatment and after twelve months of anti-VEGF (vascular endothelial growth factor) therapy. The BPZ-9 instrument comprises nine questions to evaluate patients’ psychological and social situation and their satisfaction with treatment. Results: Data were collected from 1.478 nAMD (mean 78 years), 445 DME (67 years), 233 BRVO (70 years) and 144 CRVO (71 years) patients at 102 study centers throughout Germany. Approximately one third of the patients was living alone and six percent were in organized care. One in four patients had difficulties to walk and one in five needed accompany for treatment. Anxiety to lose vision was present in three out of four patients at the beginning and slightly decreased to two out of three over the 12-month treatment preriod. The distress of having a retinal disease was generally higher than the distress related to treatment. Most patients reported high treatment satisfaction (73%) and felt well informed (81%). Conclusion: Patients receiving anti-VEGF therapy commonly experience impaired mobility, dependency on others, distress and the fear of vision loss.

Many agricultural water uses crowd the river ecology of the river water, thus leading to irreversible habitat damage. This paper presents an agricultural economic loss calculation model that is based on river ecological basic flow (REBF) protection by introducing a typical crop water requirement coefficient. First, the water balance equation is used according to a set of REBF protection values to compute the agricultural water shortage that results in the REBF. Second, the agricultural water shortage that results in REBF protection and a typical crop water requirement coefficient are used to determine the food production generated by REBF protection. Finally, the loss of food production and the food market prices are used to determine the agricultural economic loss caused by the different protection levels of REBF. A case study of the Weihe River in China is conducted. The calculation model is used to compute the agricultural economic loss on the basis of REBF protection in the Baoji section of the Weihe River, and the change law of the agricultural economic loss that results in different levels of the REBF is discussed. In addition, changes in the canal water use coefficient and the crop structure that affect agricultural economic loss are analyzed. Results show that the spatial and temporal variations in the runoff affect the changes in time and space of the agricultural economic loss. The higher the REBF protection level, the higher the agricultural economic loss. In addition, agricultural economic loss can provide a quantitative basis for reasonable REBF protection. The size of agricultural economic loss helps the government sector in decision-making on REBF protection.

Aero-fuel centrifugal pumps are important power plants in aero-engines. Unlike most of the existing centrifugal pumps, a combination impeller is integrated with the pump to improve its performance. First, the critical geometrical parameters of combination impeller and volute are given. Then, the effects of combination impeller on flow characteristics inside the impeller and volute are clarified by comparing simulation results with that of the conventional impeller, where the effectiveness of selected numerical method is validated by an acceptable agreement between simulation and experiment. Finally, the experiment is performed to test the external performance of studied pump. A significant feature of this study is that the flow characteristics are significantly ameliorated by reducing the flow losses emerged in impeller inlet, impeller outlet and volute tongue. Correspondingly, the head and efficiency of combination impeller are higher with comparison to conventional impeller. Consequently, it is a promising approach in ameliorating flow field and improving external performance by applying a combination impeller to an aero-fuel centrifugal pump.

Microwave absorbers with lightweight, good stability and high efficiency have attracted much attention for their applications in many contemporary fields. In this work, the 3D porous (Ni@NO-C)n/NO-C composite absorber was prepared by a wet chemistry method with Ni chains and melamine as precursors, in which NO-C (N,O-doped carbon) encapsulated Ni particles are homogenously dispersed in the 3D porous networks of NO-C in the form of (Ni@NO-C)n chains. The special microstructure of the as-prepared material is proved to be benefit for the improvement of its microwave adsorption performance. The as-synthesized (Ni@NO-C)n/NO-C composite ab-sorber exhibited an effective absorption bandwidth of 4.1 GHz and an extremely large reflection loss of -72.3 dB. The excellent microwave absorbing performances can be ascribed to the coopera-tive consequence of dielectric loss and magnetic loss along with the balance between attenuation capability and impedance match.

Evaluating the integrity of structures is an important issue in engineering applications. The use of vibration-based techniques has become a common approach to assessing cracks, which are the most often occurring damage in structural elements. When involving an inverse method, it is necessary to know the influence of the position and the geometry of the crack on the modal parameter changes. The geometry of the crack, both in size and shape, defines the damage severity (DS). In this study, we present a method (DS-SHC) used for estimating the DS for closed and open transverse cracks in beam-like structures by using the intact and damaged beam deflections under its weight and a Stochastic Hill Climbing (SHC) algorithm. After describing the procedure of applying DS-SHC, we calculate for a prismatic cantilever beam the severities for different crack types and depths. The results are tested by comparing the DS obtained with DS-SHC with those acquired from dynamic tests made using professional simulation software. We obtained a good fit between the severities determined in these two ways. Afterward, we performed laboratory experiments and find out that the severities obtained with the DS-SHC method can accurately predict the frequency changes due to the crack. Hence, these severities are a valuable tool for damage detection.

The gap of standardization for conducted and field coupled electromagnetic interferences (EMI) in the 2–150 kHz frequency range can lead to Electromagnetic Compatibility (EMC) problems. This is caused by power systems such as PWM controlled rectifiers, photovoltaic inverters or charging battery units in electric vehicles. This is a very important frequency spectral due to interferences generate in a wide range of dives and, specifically, communication problems in the new technologies and devices incorporated to the traditional grid to convert it into Smart Grid. Consequently, it is necessary to provide new solutions to attenuate this kind of interferences, which involve finding new materials able to filter the electromagnetic noise. This contribution is focused on characterizing the performance of different cable ferrite compositions in order to determine the effectiveness of most common materials such as MnZn and NiZn and a new range based on nanocrystalline solutions. This analysis procedure is carried out through two methods: theoretical method by determining the insertion loss through measuring impedance parameter and proposing a new empirical technique based on measuring directly the insertion loss parameter. Therefore, the main aim of this characterization process is to determine the performance of these cable ferrites to reduce the interferences in this controversial frequency range. From the results obtained, it is possible to deduce that nanocrystalline cable ferrites provide the best performance to filter the electromagnetic noise in the 2-150 kHz frequency range.

The changes in storage loss, water distribution status, gel characteristics, TBARS, total volatile basic nitrogen, and total plate count of cooked reduced-phosphate silver carp surimi batter during cold storage at 4 °C were investigated. The storage loss, content of free water, pH, hardness, TBARS, total volatile basic nitrogen value and total plate count of all cooked reduced-phosphate silver carp surimi batters significantly increased (P < 0.05) with increasing the cold storage time. Meanwhile, the content of immobilized water, whiteness, springiness, and cohesiveness decreased significantly (P < 0.05). At the same cold storage time, the sample of cooked reduced-phosphate silver carp surimi batter has lower water mobility, darker colour and better texture characteristics, but the values of TBARS, total volatile basic nitrogen and total plate count were not significantly different (P > 0.05). To sum up, the use of potassium bicarbonate instead of sodium tripolyphosphate could produce the cooked reduced-phosphate silver carp surimi batter with better water holding capacity and gel characteristics during cold storage.

The conversion of native habitats into anthropogenic ones compromises the original composition and configuration of the landscapes, influencing ecological dynamics and affecting biodiversity. Increasingly, landscape ecology has shown that these effects can only be understood if accessed at adequate spatial scales, as the scale which landscape structure is evaluated influences species responses. Here, we investigated how three variables of landscape composition (proportion of forest, coffee crop and pasture) and two of configuration (number of fragments and mean nearest neighbor distance) interfere in the richness and composition of medium and large-sized mammals considering a multiscale approach. We recorded medium and large-sized mammal species in 13 landscapes with predominantly matrices of coffee and pasture in Minas Gerais, Brazil. Then, we built distance-based linear models to identify the scale of effect of each landscape variable for both response variables considering eight scales (from 250 m to 2000 m). Finally, we verified the influence of the landscape on the richness and composition of mammals, considering the landscape variables in their respective scales of effect. We found 67% of the probable species occurring in the region. The scales of effect varied among landscape variables probably due to the different variables affect different aspects of organisms' ecological requirements. The proportion of pasture in the landscape explained the variation in species richness, while the proportion of forest explained the variation in species composition. In addition, the proportion of pasture in the landscape had a positive influence on species richness, indicating that this matrix may favor the presence of generalist species of habitat and/or this result is due to the higher concentration of species in the fragments immersed in this matrix. These results suggest that considering different responses to biodiversity is important to understand different aspects of the landscape's influence on biodiversity. In addition, the composition of the landscape is fundamental for the perpetuation of species and, therefore, both forest cover and types of matrices in the landscape must be considered to improve species conservation strategies. Finally, generalizing a spatial scale can lead to misinterpretations about the influence of the landscape on biodiversity.

Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

Street view images can help us better understand the city environment and potential characteristics. With the development of computer vision and deep learning, the technology of semantic segmentation algorithms has become more mature. However, DeeplabV3+, which is commonly used in semantic segmentation, has shortcomings such as a large number of parameters, high requirements for computing resources, and easy loss of detailed information. Therefore, this paper proposes LM-DeeplabV3+, which aims to greatly reduce parameters and computations of the model while ensuring the segmentation accuracy. Firstly, the lightweight network MobileNetV2 is selected as the backbone network, and the ECA attention mechanism is introduced after MobileNetV2 extracts shallow features to improve the ability of feature representation; secondly, the ASPP module is improved, and on its basis, the EPSA attention mechanism is introduced to achieve cross-dimensional channel attention and important feature interaction; thirdly, a loss function named CL loss is designed to balance the training offset of multiple categories and better indicate the segmentation quality. This paper conducted experimental verification on the Cityspaces dataset, and the results showed that the mIoU reached 74.9%, which was an improvement of 3.56% compared to DeeplabV3+; the mPA reached 83.01%, which was an improvement of 2.53% compared to DeeplabV3+.

Liquidity providers for asset-pair pools with constant value distri- bution, such as Balancer pools, experience some impermanent loss whenever there is price divergence in the tokens’ fiat values. In this paper, we model this impermanent loss for geometric mean invariant pools by deriving a function of two parameters. We analyze our function graphically, showing that unevenly distributed pools with large weighting disparities best mitigate the risk of impermanent loss. We conclude by providing a general protocol for liquidity providers to choose a suitable pool based on their risk tolerance and profit goals.

Acute respiratory viruses (ARVs) are leading cause of diseases in human worldwide. High risk individuals including children and elderly could potentially develop severe illness that could result in hospitalization or death in the worst case. Most common ARVs are Human respiratory syncytial virus, Human Metapneumovirus, Human Parainfluenza Virus, rhinovirus, coronaviruses (including SARS and MERS CoV), adenoviruses, Human Bocavirus, enterovirus (-D68 and 71), and influenza viruses. The olfactory deficits due to ARVs infection is a common symptom among patients. This mini review provides an overview of the role of SARS-CoV-2 and other common ARVs in the development of human olfactory pathophysiology. We highlight the critical need for understanding the signaling underlying the olfactory dysfunction and the development of therapeutics for this wide-ranging category of AVRs to restore the altered or loss of smell in affected patients.

Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. Controlling and curing diabetes in its early stages is the most significant technique to avoid its effects of diabetes. However, lack of awareness and expensive clinical tests are the primary reasons to skip clinical diagnosis and take preventive methods in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, the study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes. Because clinical features are not always known to all people from lower-income countries. To find the best fit supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. The study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for growing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.

The results of applying drilling engineering tools in planned deviated and horizontal wells of oil and gas condensate fields have been examined. The basis for the implementation of a comprehensive approach was the information obtained from drilling horizontal wells in the West and East Siberia, Volga-Ural region, and Iraqi Kurdistan. A synthetic model is considered as an example of the data, incorporating geomechanical and geological properties. As a result of the conducted audit of the geomechanical model and drilling process, the methodologies used were further developed to obtain optimal drilling fluid densities, aiming to prevent cavity formation on well walls, hydraulic fractures, and other related issues. The implementation of the Python programming language was introduced in the stages of geomechanical model preparation, resulting in a time acceleration of up to 20%. During the pre-drilling stage, technological operations were synchronized with the geomechanical model, increasing the involvement of process participants through the synchronization of knowledge areas. The interrelation between drilling technology, drilling parameters, properties of flushing fluids, and rock stability analysis was established, ensuring a 5% increase in construction speed (through NPT reduction and modification of existing tools and technological operations) and accident-free drilling of complex deviated and horizontal wells, provided that the recommended drilling parameters are followed.

Second-level hospitals face peculiarities that hinder the implementation of the hearing rescreening protocol, which are not uncommon in other settings. This study analyzes the hearing rescreening process in this kind of hospital. A total of 1130 individuals were included. In this cohort, 61.07% were newborns in the hospital who failed their first otoemission test after birth (n=679) or were unable to have the test performed (n=11), then being referred to outpatient clinic. The remaining 38.93% were individuals who were born in another hospital with their first test conducted in the outpatient clinic (n=440). A high amount of rescreenings were made outside the recommended time frame, mainly in children referred from another hospital. There was a high rate of lost to follow-up especially with otolaryngologist referrals. Neonatal Hearing screening in second level hospitals is difficult because of the staffing and time constraints. This results in longer than recommended turnaround times and interferes with the timely detection of hearing loss. This is particularly serious in outpatients. Referral of children with impaired screening to out-of-town centers leads to unacceptable loss of follow-up. A legislative support for all these rescreening issues is necessary. We discuss these findings and propose some solutions.

We explore the importance of curvature in carbonaceaous species transformation and stability, using laser-induced vaporisation to evaporate and ionise a source of curved polyaromatic hydrocarbons: carbon nanobelts. Collision impacts between species cause mass loss and the resultant ions are catalogued via mass-spectrometry. To interpret the mass spectra, we perform a series of “in-silico” simulated systematic hydrogen-loss studies using density functional theory (DFT) modelling, sequentially removing hydrogen atoms using thermodynamic stability as a selection for subsequent dehydrogenation. In an initial sequence of H2 removal, the rings are maintained through stable carbyne chain and pentagon-chain formation, giving rise to circular strained dehydrobenzoannulene species. The chains subsequently break, releasing CH and C2. While theoretical closed-cage routes are identified, they are not observed experimentally. The results can serve as a useful guide to high-energy impact conditions observed in some astrochemical environments.

Abstract: Windtech device is a novel tool for measuring the sensation of the ‘cold’. Cold poses numerous challenges for industrial operations, human survival, and living convenience. The impact of the cold is not possible to be quantified just based on temperatures; however other factors such as wind speed, humidity, irradiance have to be taken into consideration. Efforts have been made to develop combined indices such as wind chill temperature (WCT), AccuWeather RealFeel®, and others. The presented article discusses these along with the industrial standards that emphasize on the quantification of the ‘cold’. The following article introduces the Windtech device and its operating principle involving ‘heated temperature’, where the ‘heated temperature’ is affected by environmental parameters including ambient temperature, humidity, wind velocity, and irradiance. The discussed Windtech device is calibrated for operation according to the ISO 11079:2007 standard.

In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN is closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information.

TThe passion of bodybuilding athletes for a symmetric, lean, heavily muscled body leads them to carry out exhausting exercise programs and restrictive eating regimens, sometimes resulting in disordered eating behaviors. This study investigates potential exacerbators on the development of disordered eating in bodybuilding and strength athletes. The study involved 103 Cypriot bodybuilding athletes of both sexes, performing at three levels: professional, recreational and strength athletes. The Eating Attitude Test 26 (EAT-26) and The Three Factor Eating Question-naire (TFEQ-R21) were used to evaluate disordered eating and eating behaviors respectively. The current study was performed under the auspices of the Hellenic Center of Education & Treatment of Eating Disorders (KEADD). The degree of deviation between the perceived ideal body weight and the actual body weight was associated with increased risk of eating disorder. Athletes who desired a lower body weight recorded higher scores on EAT-26 overall, (p=0.001), and the subscales of dieting (p=0.01) and bulimia. (p=0.001). Cognitive restraint and emotional eating scales of TFEQ-R21 were more pronounced in the non-professional athletes. (p=0.01). The emotional eating score was higher in women. There is a need for appropriate sport-specific, gender-specific preventive intervention to deescalate the risk of eating disorder, in both profes-sional and non-professional bodybuilding athletes.

The passion of bodybuilding athletes for a symmetric, lean, heavily muscled body leads them to carry out exhausting exercise programs and restrictive eating regimens, sometimes resulting in disordered eating behaviors. This study investigates potential exacerbators on the development of disordered eating in bodybuilding and strength athletes. The study involved 103 Cypriot bodybuilding athletes of both sexes, performing at three levels: professional, recreational and strength athletes. The Eating Attitude Test 26 (EAT-26) and The Three Factor Eating Questionnaire (TFEQ-R21) were used to evaluate disordered eating and eating behaviors respectively. The current study was performed under the auspices of the Hellenic Center of Education & Treatment of Eating Disorders (KEADD). The degree of deviation between the perceived ideal body weight and the actual body weight was associated with increased risk of eating disorder. Athletes who desired a lower body weight recorded higher scores on EAT-26 overall, and the subscales of dieting and bulimia. Cognitive restraint and emotional eating scales of TFEQ-R21 were more pronounced in the non-professional athletes. The emotional eating score was higher in women. There is a need for appropriate sport-specific, gender-specific preventive intervention to deescalate the risk of eating disorder, in both professional and non-professional bodybuilding athletes.

The aim of this study was to explore perspectives of occupation health personnels (OHPs) regarding education and training of mineworkers on occupational noise induced hearing loss (ONIHL) and its impact on mineworkers’ hearing. Qualitative, in-depth telephonic and face-to-face interviews were conducted with 16 OHPs comprising representatives from the state, employer and labour as well as audiologists and occupational health hygienists. Purposive and snowball sampling were utilized to recruit participants. Data were analysed using inductive thematic analysis. Findings revealed that mineworkers have a superficial awareness and knowledge of the impact of noise on their hearing and health. Moreover, OHPs are not knowledgeable on how mineworkers are educated on ONIHL and its latent consequences. Furthermore, language, low levels of education and literacy as well as the financial constraints were factors that had a negative impact on raising awareness and training mineworkers. There is a need to prioritize health literacy among mineworkers. Additionally, audiologists need to play an active role in educating mineworkers about the effects of excessive exposure to noise. There is a need to take into account literacy levels and language barriers in planning training material for mineworkers.

Energy storage systems (ESS) are penetrating into various sections of power system through different applications. ESS can be used either as a buffer for intermittent renewable energy sources or as a stand-alone distributed storage for load shifting. ESS use different types of storage devices such as lead-acid batteries, lithium ion batteries, flow batteries, and super-capacitors. Hybrid ESS consisting of few types of storage devices are also common in practice. Determining the load demand of such ESSs at various instances (charging profile) accurately is indispensable in most of the cases. Capacity loss is common phenomenon that occurs in all types of storage devices because of ageing. Capacity loss has to be accounted while determining the charging profile of storage devices for better accuracy. Data-driven modeling is an attractive approach for determining the load demand of ESS due to the availability of valuable data from smart grid technologies. In this paper, the application of different types of data-driven models to predict the current charging profile of the ESS based on previous charging profiles is examined. The proposed method can leverage on the existing data from smart grid and is a black box modeling approach.

Optic neuritis (ON) is the most common cause of vision loss in young adults. It manifests as acute or subacute vision loss, often accompanied by retrobulbar discomfort or pain on eye movements. Typical ON is associated with Multiple Sclerosis and is generally mild and steroid-responsive, whereas atypical forms are characterized by unusual features, such as optic disc edema, poor treatment response and bilateral involvement, and are often associated with autoantibodies against aquaporin-4 (AQP4) and Myelin Oligodendrocyte Glycoprotein (MOG). However, in rare cases AQP4 and MOG antibodies will come back negative, plunging the clinician in a diagnostic conundrum. Seronegative ON warrants a broad differential diagnosis, including autoantibody-associated, granulomatous, and systemic disorders. These rare forms need to be identified promptly, as their management and prognosis is greatly different. The scope of this review is to describe possible, rarer etiologies of ON when AQP4 and MOG antibodies are negative and discuss their diagnosis and treatment.

The power function distribution is a flexible waiting time model that may provide better fit for some failure data. This paper presents the comparison of the maximum likelihood estimates and the Bayes estimates of two-parameter power function distribution. The Bayes estimates are obtained, using conjugate priors, under five loss functions consist of square error, precautionary, weighted, LINEX and DeGroot loss function. The Gibbs sampling algorithm is proposed to generate samples from posterior distributions and in result the Bayes estimates are computed. The comparison of the maximum likelihood estimates and the Bayes estimates are done through the root mean squared errors. One real-life data set is analyzed to illustrate the evaluation of proposed methods of estimation. Finally, results from the simulation are discussed to assess the performance behavior of the maximum likelihood estimates and the Bayes estimates.

Soiling accumulated on a photovoltaic (PV) module can significantly reduce the transmittance of the cover glass, resulting in power losses and consequent economic losses. Natural atmospheric parameters influence the accumulation of soiling at the various geographic locations. In this paper, the approaches and outcomes of the research studies on either indoor (simulator-based) or outdoor (field-based) PV soiling have been thoroughly reviewed. Different parameters depicted for the power loss of PV modules are analyzed individually and presented. Moreover, this study delves into a detailed examination of the key factors influencing dust depositions on PV modules in various geographical regions, with a particular focus on their relationship with climatic conditions. This way, probable future research directions to quantify soiling losses are identified. In addition, different loss prevention and mitigation techniques are also reviewed. This makes it possible to highlight effective strategies and pinpoint potential future research lines in these areas.

At present Coronavirus disease-2019 (COVID-19) is one of the leading contributing factor to mortality and the impact of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection on the sinonasal tract has become more prominent, particularly with the rising awareness of olfactory dysfunction (OD). We extracted data from published papers available in electronic databases (Wiley online library, PubMed, and Nature). We used the following search terms alone or with combinations - Olfactory dysfunction, SARS-CoV-2, mechanism and treatments. We found worldwide up to 98% of patients confirmed OD due to COVID-19. Current studies have implied that regardless of the high self-reported recovery rate, 25–40% of patients after 1 or 2 months and approximately 15%–28% of patients at six months struggle to fully restore their sense of smell. Moreover, female sex, younger and older age, active smoking, and chronic lung disease are reported as the associated risk factors of OD. Although the pathophysiological mechanism of action(s) of the OD is yet to be explored in depth, central nervous system (CNS) entrance, olfactory bulb (OB) and sustentacular cell damage, neural routes inflammation, non-neuronal cells damage, decreased OB volume and deregulation of olfactory receptor genes are among the commonly reported mechanisms for the development of OD.

According to the ECCB, cancer represents the transition of a genomically damaged multicellular cell to a lower level of cell organization, controlled by the ancestral gene regulatory network (GRN) from the ancestral compartment of human and metazoan genomes. Cancer adopts ancient mechanisms and rules of the common ancestor, similar to those observed in parasitic amoebae. In cancer, the productive cell line generating CSCs is a non-gametogenic cancer germline. This germline proliferates through asymmetric cell division (ACD phenotype). CSCs lack proliferative capacity; they persist in hypoxic niches in a quiescent cell state or transition to an amplifying cell state that performs cyst-like polyploidization cycles, giving rise to precursors and CSCs capable of differentiation. Malignization arises from genomically dysfunctional tetraploid germline cells (DSCD phenotype) that proliferate in a precancerous stage through symmetric cell cycles. DSCD progeny undergo homotypic fusion, forming multinucleated genome repair syncytia (MGRS pathway) driving carcinogenesis. During tumorigenesis, metastasis, and recurrence, subsequent germline clones initiate a cascade of increasingly pathogenic CSCs through mechanisms such as heterotypic cell fusion, soma-to-germ transition, and polyploid giant cell structures (PGCCs). Irradiation and chemotherapies drive some of the very few surviving but dysfunctional CSCs through a PGCC repair pathway homologous to the MGRS pathway of carcinogenesis. The large number of PGCCs during metastasis and recurrence results from the perpetual germline cell damage and CSC poverty induced by oxygen levels above 6.0% O2 (germline hyperoxia).

One of the main technological and economic challenges for an engineer is designing high-quality products in manufacturing processes. Most of these processes involve a large number of variables included the setting of controllable (design) and uncontrollable (noise) variables. Robust Design (RD) method uses a collection of mathematical and statistical tools to study a large number of variables in the process with a minimum value of computational cost. Robust design method tries to make high-quality products according to customers’ viewpoints with an acceptable profit margin. This paper aims to provide a brief up-to-date review of the latest development of RD method particularly applied in manufacturing systems. The basic concepts of the quality loss function, orthogonal array, and crossed array design are explained. According to robust design approach, two classifications are presented, first for different types of factors, and second for different types of data. This classification plays an important role in determining the number of necessity replications for experiments and choose the best method for analyzing data. In addition, the combination of RD method with some other optimization methods applied in designing and optimizing of processes are discussed.

This study aimed to compare the differences in durability and prestress loss between normal-weight concrete (NC) and lightweight aggregate concrete (LWC) prestressed box girders, which were constructed at the same time in the same area, so as to verify the superiority of using synthetic lightweight aggregate (LWA) made from reservoir sediments in prestressed bridges. For the NCs and LWCs used in prestressed box girders, the basic mechanical properties were tested on the one hand, and the durability properties were tested on the other hand. Then, through the prestress monitoring system, the prestress loss of the two groups of the prestressed box girders was tracked. The results of the durability test confirmed that LWC can inhibit the penetration of air, water, and chloride ions by strengthening the interfacial transition zone between the aggregate and the cement paste, thereby improving its durability. Moreover, the magnetic flux prestress loss of the NC prestressed box girder reached 8.1%. In contrast, the magnetic flux prestress losses on both sides of the LWC prestressed box girder were 4.6% and 4.9%, respectively. This verified that, under the same environmental conditions, the use of LWC produced less of a prestress loss than the use of NC.

Controlled drainage (CD) is an important agricultural measure for maintaining soil moisture and nutrients, controlling groundwater level, and increasing crop yield. In arid regions, CD can be used to improve the water supply in agriculture and reduce environmental pollution. In this study, we investigated the effect of CD, including a drainage depth of 40 cm (CWT1) and 70 cm (CWT2) during the plant growth period, free drainage (FD), and open ditch drainage (OD) on the migration of water, nutrients, and salts in the soil; the dynamics of groundwater level; the loss of soil nitrogen; and the growth of oilseed sunflower plants. Compared with FD, CD increased the water and nutrient content in the soil, reduced nitrogen loss, and enhanced the ability of the soil to continuously supply nitrogen to the oilseed sunflower plants, which benefited plant growth at later growth stages and reduced environmental pollution. During the period between irrigation at the budding stage and harvest stage, the average soil water content in the 0–20 cm soil layer in CWT1 increased by 3.67%, 4.78%, and 0.55%, respectively, compared with that in CWT2, FD, and OD. The soil mineral content in CWT1 was 25.17%, 35.05%, and 17.78% higher than that in CWT2, FD, and OD, respectively, indicating that higher soil salinity occurred at the later stage of plant growth in CWT1, which actually had little effect on the plants due to their enhanced salt tolerance and increased need for water and nutrients at that stage. In addition, CD delayed the decline in groundwater level, which allowed the plants to use groundwater at later growth stages, and as a result the yield and water use efficiency were improved. CWT1 significantly increased oilseed sunflower yield by 4.52–11.14% and increased water use efficiency by 1.16–10.8%. Moreover, CWT1 also increased the survival rate of the oilseed sunflower plants by 2.62–2.92%, and the plants demonstrated good growth. Therefore, under CD conditions, plants used soil water and nitrogen more efficiently and, as a result, their productivity was increased, and the water quality was improved.

The study delves into the critical examination of the Size of Loss distribution patterns in the context of auto insurance during pre- and post-pandemics, emphasizing their profound influence on insurance pricing and regulatory frameworks. Through a comprehensive analysis of the historical Size of Loss data, insurers and regulators gain essential insights into the probabilities and magnitudes of insurance claims, informing the determination of precise insurance premiums and the management of case reserving. This approach aids in fostering fair competition, ensuring equitable premium rates, and preventing discriminatory pricing practices, thereby promoting a balanced insurance landscape. The research further investigates the transformative impact of the COVID-19 pandemic on these Size of Loss patterns, given the substantial shifts in driving behaviors and risk landscapes. Also, the research contributes to the literature by addressing the need for more studies focusing on the implications of the COVID-19 pandemic on pre- and post-pandemic auto insurance loss patterns, thus offering a holistic perspective encompassing both insurance pricing and regulatory dimensions.

A novel prediction model for crack development of reinforced concrete (RC) piles with localized chloride corrosion in marine environment is proposed. A discrete method is used to solve the corrosion pit radius model and a crack extension model is developed to investigate the initiation and extension of cracks. The maximum corrosion degree of reinforced concrete pile is predicted according to the limit crack criterion, and finally a sensitivity analysis is carried out on the important parameters of crack extension. The results show that the radius of corrosion pit, the depth corrosion pit and cross-sectional area loss of reinforcement gradually increase as the corrosion level increases. The loss of local reinforcement section at crack initiation decreases with the increase of the ratio of concrete cover to initial diameter, and increases with the increase of pitting factor. The required pit depth for reinforcement cracking increases with the increase of the ratio of concrete cover thickness to diameter. The loss of cross-sectional area of reinforcement and the radius of corrosion pit increase with the increase of initial diameter of reinforcement. Increasing the pitting factor will reduce the pit depth and make the crack width develop faster before reaching the limit crack width. Increasing the concrete cover thickness can provide an improvement in the propagation of cracks. A comparative analysis shows that the localized corrosion pattern is more in conformity with marine engineering practice.

The study presented here, through the use of GIS and physical variables, generate a new meth-odology for the calculation of erosivity (Factor R) for the qualitative evaluation of soil loss po-tential (laminar erosion) in the Northwest margin of the municipality of São Francisco, mostly located in the Pardo-MG river basin. The results obtained were statistically correct when com-pared to the traditional R calculation and the laminar erosion methodology. It is worth noting that the new proposal presented here indicated sites of qualitative loss of soil in the same places where there are closed-loop satellite signals of degraded Cerrado, something not identified in the traditional model. As conclusion it can be said that the obtained results were extremely sat-isfactory, being advised more studies to verify this new method proposed here in more places beyond the study area elaborated here

The implementation of One Health/EcoHealth/Planetary Health approaches has been identified as key (i) to address the strong interconnections between risk for pandemics, climate change and biodiversity loss, and (ii) to develop and implement solutions to these interlinked crises. As a response to the multiple calls of scientists in that direction, we have put forward seven long term research questions regarding COVID-19 and emerging infectious diseases (EIDs) that are based on an effective integration of environmental, ecological, evolutionary, and social sciences to better anticipate and mitigate EIDs. Research needs cover the social-ecology of infectious disease agents, their evolution, the determinants of susceptibility of humans and animals to infections, and the human and ecological factors accelerating infectious disease emergence. For comprehensive investigation, they include the development of nature-based solutions to interlinked global planetary crises, addressing ethical and philosophical questions regarding the relationship of humans to nature and regarding transformative changes to safeguard the environment and human health. In support of this research, we propose the implementation of innovative multidisciplinary facilities embedded in social-ecosystems locally: the “ecological health observatories” and the “living laboratories”. This work has been carried out in the frame of the EC project HERA (www.HERAresearchEU.eu) that aims to set the priorities for an environment, climate and health research agenda in the EU by adopting a systemic approach in the face of global environmental change.

Ultra-thin Fe–6.5wt.%Si ribbons with 35 μm in thickness were prepared by melt-spinning. The magnetic properties were investigated before and after annealing 1000 ºC. DC properties and low-frequency (400 Hz ~ 10 kHz) iron losses have significantly improved after heat treatment. A simplified formula based on Steinmetz law which can be used to predict the AC iron loss is presented. According to the results of some iron losses data, a simplified formula has been determined, and the extent of AC iron losses can be predicted. The results obtained from the formula predict AC iron loss to a good degree. The method developed in this work could be extended to other magnetic materials for predicting AC iron loss with greater ease.

Our findings indicate that all five phytocannabinoids reduce HG-HL-induced -cell loss likely through reducing apoptosis and pyroptosis. The protective effects of CBD, THCV, CBC, and CBN were seen in the GSIS impairment by HG-HL. Although all five phytocannabinoids tested in this research demonstrated the capability to inhibit β-cell dedifferentiation induced by HG-HL, CBD seems to be more effective compared to the other phytocannabinoids, as indicated by the specific biomarker responses of β-cells and progenitor cells to CBD.

In recent years in Bangladesh, there has been regular cyclonic event, flooding and erratic pre-monsoons precipitation that has hampered production greatly and forced Bangladesh to import salt from abroad to manage market deficiency. There is much uncertainty about the effects of climate change on the frequency and intensity of small-scale, sudden onset weather phenomena such as heavy rainfall and subsequent loss and damage (L&D). But, several studies indicate that an obvious strong relationship exists between irregular rainfall and associated L&D. Nowadays, severe changing rainfall patterns are observed in Bangladesh, which is rapid-onset in nature, but low exposed in terms of response. The current study explored a ‘double-exposed’ burden combined of both climatic (e.g., uneven rainfall) and non-climatic governance factors (e.g., imperfect trade policy, the absence of risk transfer mechanisms) which are hindering salt production and pushing the country from the aspiration of salt exporting to the net buyer. This chapter mainly assesses the impacts of L&D due to climatic events that are causing overwhelming effects on the well-being of marginal salt farmers at Kutubdia Upazila of Bangladesh. Data were mainly collected through Participatory Vulnerability Analysis (PVA), Key informant interviews (KII), and Sample Surveys (SS). This study would provide insights for improved disaster management policy and an appropriate adaptive measure to address such extreme events as well as to initiate a further study for understanding the nexus of ‘nature and market’ in building resilience among the marginal salt farmers.

We develop a technique for realizing lithium niobate on insulator (LNOI) waveguides of a multi-centimeter-length with a propagation loss as low as 0.027 dB/cm. Our technique relies on patterning a chromium (Cr) thin film coated on the top surface of LNOI into a hard mask with a femtosecond laser followed by the chemo-mechanical polishing for structuring the LNOI into the waveguides. The surface roughness on the waveguides is determined to be 0.452 nm with an atomic force microscope (AFM). The approach is compatible with other surface patterning technologies such as optical and electron beam lithographies or laser direct writing, enabling high-throughput manufacturing of large-scale LNOI-based photonic integrated circuits.

Under high-frequency operating conditions, the high-speed solenoid valve (HSV) experiences energy loss and heat generation, which significantly impact its operational lifetime. Reducing the energy loss of HSV without compromising its opening response characteristics poses a significant challenge. To address this issue, a finite element simulation model of HSV coupled with a current feedback model is constructed to investigate the synergistic effects of dynamic response and energy loss. Predictive models for the opening response time, HSV driving energy, and Joule energy using Back Propagation neural network (BPNN) are established. Furthermore, a multi-objective optimization study on the current driving strategy using Non-dominated Sorting Genetic Algorithm II (NSGA-II) is conducted. After optimization, although there was a 6.24% increase in the opening response time, both HSV drive energy and Joule energy were significantly reduced by 15.67% and 22.49%, respectively. The proposed multi-objective optimization method for HSV driving strategy holds great significance in improving its working durability.

Underwater wireless optical communications (UWOC) have attracted considerable attention in recent years as an alternative means for acoustic communication. However, optical path loss of light propagation from attenuation is large due to absorption and scattering in various water conditions. Identification of environmental effects especially tropical storms on underwater optical path loss is the key to the success of using optics for UWOC. Underwater inherent optical properties (IOPs) such as the beam attenuation coefficient for 470 nm light in the western North Pacific Ocean were measured from the U.S. Naval Oceanographic Office sea gliders with being deployed after Super Typhoon Guchol (7-20 June 2012)‘s passage during 25-30 June 2012 and no any typhoon passage during 9 January – 28 February 2014. The observed two sets (with and without super typhoon) of IOPs are taken as input into a recently developed Radiative Transfer Equation solver. The simulated normalized received powers for the two durations show large impact of typhoon passage on UWOC.

Background: Appendicular skeletal muscle (or lean) mass (ALM) estimated using dual-energy X-ray absorptiometry (DXA) is considered to be a preferred method for sarcopenia studies. However, DXA is expensive, has limited portability, and requires radiation exposure. Bioelectrical impedance analysis (BIA) is inexpensive, easy to use, and portable; thus BIA might be useful in sarcopenia investigations. However, a large variety of models have been commercially supplied by different companies, and for most consumer products, the equations estimating ALM are not disclosed. It is therefore difficult to use these equations for research purposes. In particular, the BIA equation is often age-dependent, which leads to fundamental difficulty in examining age-related ALM loss. The aims of the current study were as follows: (1) to develop and validate an equation to estimate ALM using multi-frequency BIA (MF-BIA) based on theoretical models, and (2) to establish sarcopenia cutoff values using the equation for the Japanese population. Methods: We measured height (Ht), weight, and ALM obtained using DXA and a standing-posture 8-electrode MF-BIA (5, 50, 250 kHz) in 756 Japanese individuals aged 18 to 86-years-old (222 men and 301 women as developing equation group and 97 men and 136 women as a cross validation group). The traditional impedance index (Ht²/Z₅₀) and impedance ratio of high and low frequency (Z₂₅₀/Z₅) of hand to foot values were calculated. Multiple regression analyses were conducted with ALM as dependent variable in men and women separately. Results: We created the following equations: ALM = (0.6947 × (Ht²/Z₅₀)) + (−55.24 × (Z₂₅₀/Z₅)) + (−10,940 × (1/Z₅₀)) + 51.33 for men, and ALM = (0.6144 × (Ht²/Z₅₀)) + (−36.61 × (Z₂₅₀/Z₅)) + (−9332 × (1/Z₅₀)) + 37.91 for women. Additionally, we conducted measurements in 1624 men and 1368 women aged 18 to 40 years to establish sarcopenia cutoff values in the Japanese population. The mean values minus 2 standard deviations of the skeletal muscle mass index (ALM/Ht²) in these participants were 6.8 and 5.7 kg/m² in men and women, respectively. Conclusion: The current study established and validated a theoretical and age-independent equation using MF-BIA to estimate ALM and provided reasonable sarcopenia cutoff values.

Human-driven biodiversity destruction are responsible for significant and sustained heritage losses in Africa. In Venda, northern South Africa, biodiversity losses are eroding the existence of sacred places. Such places define the essence of indigenous people’s identity and well-being. We highlight how developments in Venda such as mining and agricultural expansion since apartheid times have destroyed biodiversity in the broader landscape, undermining efforts to reduce hunger and poverty. Thathe forest, Lake Fundudzi and Phiphidi waterfalls are central to Venda mythology and legends, origins and identity and are key towards conserving current biodiversity and heritage losses.

In the FCC conversion of heavy petroleum fractions as atmospheric residues, the main challenge for refiners to achieve the quantity and quality of various commercial products depends essentially on the catalyst used in the process. A deep characterization of the catalyst at different steps of the process (fresh, regenerated, and spent catalyst) was investigated to study the catalyst's behavior including the physicochemical evolution, the deactivation factor, and kinetic-thermodynamic parameters. All samples have been characterized by various spectroscopy methods such as N2 adsorption-desorption, UV-visible spectroscopy, Raman spectroscopy, LECO carbon analysis, Scanning electron microscopy (SEM), X-ray diffraction (XRD), X‐ray fluorescence (XRF), nuclear magnetic resonance spectroscopy (NMR13C) analysis, and Thermogravimetric analysis. The results of N2 adsorption-desorption, UV-vis, Raman, LECO carbon, and SEM images showed that the main causes of catalyst deactivation and coking were the deposition of carbon species that covered the active sites and clogged the pores, and the attrition factor due to thermal conditions and poisonous metals. The XRD and XRF results showed the catalyst’s physicochemical evolution during the process and the different interlinks between catalyst and feedstock (Nickel, Vanadium, Sulfur, and Iron) elements which should be responsible for the coking and catalyst attrition factor. It has been found that, in addition to the temperature, the residence time of the catalyst in the process also has an influence on catalyst structure transformation. NMR13C analysis revealed that polyaromatic hydrocarbon is the main component in the deposited coke of the spent catalyst. Thermogravimetric analysis showed that the order of catalyst mass loss was fresh > regenerated > spent catalyst due to the progressive losses of the hydroxyl bonds (OH) and the structure change along the catalyst thermal treatment. Moreover, the kinetic and thermodynamic parameters showed that all zones are non-spontaneous endothermic reactions.

We propose a SUSY-inspired loss framework to address the generalizationrobustness trade-off in AI. Combining bosonic and fermionic components, the loss ensures smooth learning and robustness against adversarial attacks. Parallel transport stabilizes weight updates across non-Euclidean loss landscapes. Validation on CIFAR-10 demonstrates stable convergence and enhanced performance under FGSM and PGD attacks, confirming the effectiveness of the proposed approach. Keywords: Supersymmetry, Generalization, Robustness, Adversarial Attacks, Parallel Transport, Loss Optimization, CIFAR-10.

Generative Adversarial Networks (GANs) are powerful models for generative tasks but face significant challenges such as instability, mode collapse, and inefficient convergence. This paper introduces a theoretical framework leveraging Fermi-Dirac statistics, Ruppeiner geometry, phase transition dynamics, and graph theory to address these issues. The proposed theorems provide a robust mathematical foundation for enhancing GAN stability, diversity, and convergence. Experimental validation on the MNIST dataset demonstrates that the application of these theorems results in improved Inception Scores, reduced Fréchet Inception Distances, and stable training dynamics, as indicated by Ruppeiner curvature analysis. The findings suggest that these theoretical insights offer a comprehensive solution to the persistent challenges in GAN training, paving the way for more reliable and effective generative models.

Mandibular third molar is the most frequently impacted tooth. Mandibular third molar impaction bears consequences on the adjacent mandibular second molar (MSM) like bone loss. It can be identified initially using orthopantomography (OPG). The objective is to compare the changes in the bone level distal to MSM between patients with extracted and non-extracted impacted man-dibular third molars (IMTM) using OPG. In this retrospective case-control study, 160 archived (OPGs) of 80 patients visiting the Dental Hospital of the University of Barcelona (HOUB) were randomly selected. The participants were stratified into two groups. The study group involved measuring the bone level distal to the mandibular second molar before and after the extraction of the IMTM. The control group, on the other hand, had bone levels measured twice within 3–6-month period without the extraction of the IMTM. The mean ages of patients in study and control groups were 35.5 ± 15.45 and 33 ± 16.49, respectively. Study group demonstrated signifi-cant (p < 0.05) bone gain after extraction, whereas control group demonstrated significant bone loss (p < 0.0001) after follow-up. Bone improvements were observed distal to the mandibular second molar after the extraction of the IMTM compared to those in control group.

In high-traffic harbor waters, marine radar frequently encounters signal interference stemming from various obstructive elements, thereby presenting formidable obstacles in the precise identification of ships. To achieve precise pixel-level ship identification in the complex environments, a customized neural network-based ship segmentation algorithm named MrisNet is proposed. MrisNet employs a lightweight and efficient FasterYOLO network to extract features from radar images at different levels, capturing fine-grained edge information and deep semantic features of ship pixels. To address the limitation of deep features in the backbone network lacking detailed shape and structured information, an adaptive attention mechanism is introduced after the FasterYOLO network to enhance crucial ship features. To fully utilize the multi-dimensional feature outputs, MrisNet incorporates a Transformer structure to reconstruct the PANet feature fusion network, allowing for the fusion of contextual information and capturing more essential ship information and semantic correlations. In the prediction stage, MrisNet optimizes the target position loss using the EIoU function, enabling the algorithm to adapt to ship position deviations and size variations, thereby improving segmentation accuracy and convergence speed. Experimental results demonstrate that MrisNet achieves high recall and precision rates of 94.8% and 95.2%, respectively, in ship instance segmentation, outperforming various YOLO and other single-stage algorithms. Moreover, MrisNet has a model parameter size of 13.8M and real-time computational cost of 23.5G, demonstrating notable advantages in terms of convolutional efficiency. In conclusion, MrisNet accurately segments ships with different spot features and under diverse environmental conditions in marine radar images. It exhibits outstanding performance, particularly in extreme scenarios and challenging interference conditions, showcasing robustness and applicability.

The research paper provides a mathematical framework for understanding phase shift in wave phenomena, bridging theoretical foundations with real-world applications. It emphasizes the importance of phase shift in physics and engineering, particularly in fields like telecommunications and acoustics. Key equations are introduced to explain phase angle, time delay, frequency, and wavelength relationships. The study also introduces the concept of time distortion due to a 1° phase shift, crucial for precise time measurements in precision instruments. The research also addresses infinitesimal wave energy loss related to phase shift, enriching our understanding of wave behavior, and impacting scientific and engineering disciplines.

The images captured by UAVs during inspection often contain numerous small targets related to transmission lines, which are critical and vulnerable elements for ensuring the safe operation of these lines. However, due to various factors such as the small size of the targets, low resolution, complex background, and potential line aggregation, achieving accurate and real-time detection becomes challenging. To address these issues, this paper proposes a detection algorithm called P2-ECA-EIOU-YOLOv5 (P2E-YOLOv5). Firstly, in order to address the challenges posed by the complex background and environmental interference that impact small targets, an ECA attention module is integrated into the network. This module effectively enhances the network's focus on small targets while concurrently mitigating the influence of environmental interference. Secondly, considering the characteristics of small target size and low resolution, a new high-resolution detection head is introduced, which is more sensitive to small targets. Lastly, the network utilizes the EIOU-Loss as the regression Loss function to improve the positioning accuracy of small targets, as they tend to aggregate. Experimental results demonstrate that the proposed P2E-YOLOv5 detection algorithm achieves an accuracy P of 96.0% and an average accuracy (mAP) of 97.0% for small target detection in transmission lines.

Silicon has a theoretical specific capacity of 4200 mAh g−1, which is considered to have significant potential for anode materials in lithium-ion batteries (LIBs). However, the volume change that occurs in silicon during charging and discharging affects the development of commercial applications. In this work, a yolk-shell structured Si@void@C anode material has been developed to address this problem. The silicon nanoparticle yolk material is obtained by recycling kerf-loss (KL) Si waste from the photovoltaic industry, the carbon shell is prepared via a hydrothermal process with glucose, and the sacrificial interlayer is Al2O3. The synthesized material is used to fabricate anodes that show a high reversible capacity of 836 mAh g−1 at the current density of 0.1 A g−1 after 100 cycles, with a Coulomb efficiency of 71.4%. This work demonstrates a relatively low-cost approach to turn KL Si waste into value-added materials for LIBs.

Background: We aimed to assess the association between DII and PD and the mediation effect of DII in the association of PD with systemic inflammation. Using the National Health and Nutrition Examination Survey (NHANES) 2009-2010, 2011-2012 and 2013-2014, participants that received periodontal exam and provided dietary recall data were included. The inflammatory potential of diet was calculated via DII. Periodontitis was defined according to the 2012 case definition. The clinical outcomes of interest were mean periodontal probing (PPD), mean clinical attachment loss (CAL) and thresholds of PPD and CAL. White blood cells (WBC), segmented neutrophils and C-reactive protein (CRP) were used as proxies for systemic inflammation. The periodontal measures were regressed across DII values using adjusted multivariate linear regression. Adjusted mediation analysis appraised the influence of DII in the association of periodontitis and systemic inflammation. 10,178 participants were included. DII was significantly correlated with mean PPD, mean CAL, thresholds of PPD and CAL, WBC, segmented neutrophils and DII (p&lt;0.01). A linear regression logistic adjusted for multiple confounding variables confirmed the association between DII and mean PPD (B = 0.02, Standard Error [SE]: 0.02, p&lt;0.001) and CAL (B = -0.02, SE: 0.01, p&lt;0.001). The association of mean PPD and mean CAL with both white blood cells and segmented neutrophils were mediated by DII (from 2.1 to 3.5%, p&lt;0.001). In the 2009-2010 subset, the association of mean CAL with serum CRP was mediated by DII (52.0%, p&lt;0.01). In conclusion, inflammatory diet and PD may be associated. Also, the inflammatory diet significantly mediated the association of leukocyte counts and systemic inflammation with periodontitis.

This paper constructs a model of climate-related damage for small island developing states (SIDS). We focus on the loss of private productive capital stocks through extreme climate events. In contrast to most economic analyses of climate impacts, which assume temperature-dependent damage functions, we draw on the engineering literature to allow for a greater or lesser degree of anticipation of climate change when designing capital stocks and balancing current adaptation expenditure against future loss & damage. We apply the model to tropical storm damage in the small island developing state of Barbados and show how anticipatory behavior changes the damage to infrastructure for the same degree of climate change. Thus, in the model, damage depends on behavior as well as climate variables.

This paper considers estimating the ratio of two distributions with different parameters and common supports. We consider a Bayesian approach based on the Log--Huber loss function which is resistant to outliers and useful to find robust M-estimators. We propose two different types of Bayesian density ratio estimators and compare their performance in terms of Bayesian risk function with themselves as well as the usual plug-in density ratio estimators. Some applications such as classification and divergence function estimation are addressed.

Corona loss generated from conductors is one of the important design factors on ac transmission lines. Based on helical charge simulation method, the 3-D corona loss calculation model considering the conductors’ outer strands in the corona cage is established. With regard to 2-D corona-loss calculation method, the calculation model proposed in this paper can take the electric field strength’s nonuniformity along the axial direction of stranded conductor into consideration, furthermore, it can also calculate the corona loss when the conductor has sag. The calculation results of the LGJ-300/40 and LGJ-400/35 in the small corona cage well coincide with the measurement results, which indicates that the corona-loss calculation model is appropriate. In addition, the corona loss of the bundle conductors 4 × LGJ720 with sag in UHV corona cage is also analyzed.

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

Temporal lobe epilepsy is a common, chronic disorder with spontaneous seizures that is often refractory to drug therapy. A potential cause of temporal lobe epilepsy is primary brain injury, making prevention of epileptogenesis after the initial event an optimal method of treatment. Despite this, no preventive therapy for epilepsy is currently available. The purpose of this study was to evaluate the effects of anakinra, lamotrigine, and their combination on epileptogenesis using the rat lithium-pilocarpine model of temporal lobe epilepsy. The study showed that the treated and untreated animals showed no significant difference in the number and duration of seizures. However, the severity of seizures was significantly reduced after treatment. Anakinra and lamotrigine, alone or in combination, significantly reduced neuronal loss in the CA1 hippocampus compared to the control group. However, the drugs administered alone were found to be more effective for CA3 than their combination. The treatment alleviated the impairments in activity level, exploratory behavior and anxiety, but had a relatively weak effect on TLE-induced impairments in social behavior and memory. The efficacy of the combination treatment did not differ from that of anakinra and lamotrigine monotherapy. These findings suggest that anakinra and lamotrigine, alone or in combination, may have clinical utility in preventing epileptogenesis.

Background: One of the most astounding discoveries of recent times is the recognition that cancer embodies a transition from a higher level of metazoan cell organization to a more foundational pre-metazoic state. This shift is steered by genes housed within the ancestral genome compartment, pervasive across all metazoan genomes, encompassing humans, and governed by a pre-metazoic gene regulatory mechanism (aGRN). This work aims to highlight the emerging field of evolutionary cancer cell biology (ECCB), which points to the deep homology between cancer and protist life cycles tracing back to the common ancestor of amoebozoans, metazoans, and fungi (AMF). The ECCB analysis reveals the essence of the non-gametogenic germline of the AMF ancestor, which serves as a blueprint for all metazoan germlines and stem cell lineages and controls the life cycle of cancer. Every germ and stem cell lineage of humans and metazoans traces its lineage back to this Urgermline, transmitting crucial processes such as asymmetric cell cycling, differentiation, stemness, and phenomena like GST and SGT (aka EMT and MET) to their subsequent evolutionary descendants. Oxygen-sensitive germline and stem cells suffer DNA-DSB due to stress and oxygen ranges reminiscent of ancestral hyperoxia, leading to cell senescence. Cells that can overcome senescence can proliferate as DSCD cells with defective symmetric cell division, paving the way for malignancy and PGCC cancers. Conclusions: Understanding cancer from its evolutionary origins may help break some of the logjams in cancer prevention and open up new therapeutic pathways.

Early detection, isolation, and management of COVID-19 patients are crucial to contain the current pandemic. The CDC in USA recently included "sudden loss of taste (dysgeusia/ageusia) and smell (anosmia/hyposmia)” as symptoms of COVID-19. If these symptoms are reliable forerunner symptoms of COVID-19, then it may facilitate early detection and containment of the disease. Hence, we systematically evaluated the contemporary evidence on dysgeusia and anosmia as trigger symptoms in COVID-19. Ovid MEDLINE, EBSCO host, and Web of Science databases were searched between December 25, 2019-May 30, 2020.Of the 13 identified records, eight (totaling 11,054 COVID-19 patients), were included, as per the selection criteria. The studies emanated mostly from the European community, as well as China, the USA, and Iran. In total, anosmia and dysgeusia symptoms were present in 74.9 % and 81.3% ambulatory as well as hospitalized, mild-to-severe cases of COVID-19 patients, respectively. The European, US, and Iran data indicate that olfactory, and gustatory symptoms appear prior to general COVID-19 symptoms in a majority of the patients. To our knowledge, this is the first systematic review analyzing the prevalence of chemosensory dysfunction in COVID-19. Further, studies are essential to evaluate their utility as harbingers of COVID-19 onset, and to establish clinical practice guidelines.

This study measures the noise levels in a baseball stadium and analyzes baseball fans’ attitude of effect of recreational noise exposure on their hearing. In the baseball stadium, noise levels were measured in four seating sections using a sound level meter during the games. The LAeq average of the 16 measures produced 91.7 dBA, showing a significantly high noise level in the red and navy sections. As a function of frequency by LZeq analysis, the noise levels were significantly higher in low frequencies than other frequencies. For the survey sample, 688 randomly selected participants completed a 16-question survey on their noise exposure during the game and on the potential risk of hearing loss. Despite the very high noise levels, 70% of the respondents preferred sitting in either the red or the navy section to be closer to the cheerleaders and to obtain a good view. Most respondents reported that they did not consider wearing earplugs, and one-third experienced hearing muffled speech after the game. We conclude that the noise levels in baseball stadiums are high enough to cause hearing damage and/or tinnitus later, but expect these results to improve public education regarding safe noise exposure during popular sports activities.

Users of meteorological forecasts are often faced with the question of whether to make a decision now based on the current forecast or whether to wait for the next and hopefully more accurate forecast before making the decision. One would imagine that the answer to this question should depend on the extent to which there is a benefit in making the decision now rather than later, combined with an understanding of how the skill of the forecast improves, and information about the possible size and nature of forecast changes. We extend the well-known cost-loss model for forecast-based decision making to capture an idealized version of this situation. We find that within this extended cost-loss model, the question of whether to decide now or wait depends on two specific aspects of the forecast, both of which involve probabilities of probabilities. For the special case of weather and climate forecasts in the form of normal distributions we derive a simulation algorithm, and equivalent analytical expressions, for calculating these two probabilities. We apply the algorithm to forecasts of temperature and find that the algorithm leads to better decisions relative to three simpler alternative decision-making schemes. Similar problems have been studied in many other fields, and we explore some of the connections.

Background. In the last decade, it was concluded that glaucomatous optic neuropathy is part of the neurodegenerative diseases in which destructive neuronal lesions are located not only in the structure of the retina, but they are also present at the level of the central visual and nervous pathways. Patients with Primitive Open Angle Glaucoma may have experienced changes such as sensorineural hearing loss as well as balance changes. The purpose of this study is to evaluate the degree of involvement of the auditory system in patients with glaucoma, the structural changes in the optic nerve and, to establish correlation between the hearing loss and structural changes in the optic nerve. The subjects included in the study are grouped as follows: a group of patients with glaucoma that will be compared to a group of patients without glaucoma only as well as identi-fy-ing the evolution of structural changes in optic nerve to patients with glaucoma and to normal patients. Material and methods. It is a prospective, cross-sectional study on 32 eyes of 16 subjects with Primitive Open Angle Glaucoma in a study group and 24 eyes of 12 healthy subjects in con-trol group with a mean age of 62 years in both groups. Both groups underwent ophthalmo-logical, audiological and radio imaging evaluation: Pure-tone audiometry (PTA) were recorded, oph-thalmologic evaluations according to a standardized protocol and MRI cerebral examination was performed. Results: In the Study group patients, compared to the Control group, the average lev-els of the left PTA, the cup/disc (C/D) area in both eyes, the C/D ratio in both eyes are in-creased. The mean level of Retinal nerve fiber layer (RNFL) mean thickness was slightly lower in patients with glaucoma. Multivariate analysis demonstrates that gender can be a good predictor of C/D area, gender and age can be good predictors of PTA both eyes and C/D ratio in the right eye, and gender, age and background can be good predictors of disc area in the left eye. Conclu-sions: Pa-tients with Primitive Open Angle Glaucoma show changes in audiometry test in correla-tion with ophthalmological parameters demonstrating that the auditory system can be affected in Primitive Open Angle Glaucoma. This study emphasizes the problem of interdisciplinarity in medicine with the aim of ensuring the quality of life of patients with glaucoma. A good collabora-tion between ophthalmologist and otolaryngologist will be very useful for these patients.

Purpose: Proximal Femur Fractures (PFFs) in elder patients lead to decreased productivity. Skilled nursing facilities (SNFs) and inpatient rehabilitation facilities (IRFs) are non-home destinations for post-discharge disposition. This study aims to evaluate the loss of independence (LOI) following PFFs and examine the economic impact it entails. Method: Literature from various databases was collected and analyzed retrospectively. The inclusion criteria included patients age >18 years and articles published after 1990. All studies were screened and a PRISMA chart was used to demonstrate the search process. 24 studies were finally used for review. Results: LOI following PFFs significantly increases with age. Fractures in geriatrics avail a significant amount of post-care resources and had longer lengths of stay. Furthermore, six pre-operative risk factors were identified for non-home disposition including age >75, female, non-Caucasian race, Medicare status, prior depression, Charlson Comorbidity Index. Patients discharged directly to home have lower total cost compared to those discharged to rehabilitation units. Loss of independence increases with advancing age. Discussion: PFFs can lead to serious loss of independence among elderly patients with direct impact on economic, social, psychological aspects and the healthcare system.

The aim of the studies was to test the hydraulic pressure losses along the length, local pressure losses, absolute and relative roughness values of the pipeline walls made of modern polymer material. The article presents the results of the experimental studies of hydraulic resistance of pipelines from modern polymer materials, the method of theoretical determination of resistance coefficients in the VALTEC ISO 21003 metal polymer pipeline, the classification and method of determining the values of local hydraulic resistance coefficients of T-Junctions are given, the dependence for determining the Darcy λ hydraulic friction coefficient associated with the value of the equivalent hydraulic roughness coefficient of the inner surface of pipes and the Reynolds number. The experimental data of the hydraulic coefficient of friction Darcy λ was compared with the known theoretical relationships of A.D. Altschul, Colbrooke for smooth pipes, Colbrooke and White, an alternative to the Colbrooke-White, Offengenden equation for low-shear pipes and pipes from polymer materials with Reynolds numbers Re>10^4. Dependence of hydraulic coefficient of friction on relative equivalent roughness of pipeline walls and Reynolds number, specific energy loss per one linear meter on total flow rate in system of hydraulically short pipeline is obtained. The empirical dependence of values of coefficients of local resistance ζ=f(Q2/Q3) from a ratio of the divided expenses of Q2/Q3 of the equal-pass T-Junction α=90 the pipeline to a branch up, on pass in the direct T-Junction and division of streams at various internal pipeline diameters of VALTEC.

The aim of this study was to develop and validate a predictive early tooth loss multivariable model for periodontitis patients before periodontal treatment. A total of 544 patients seeking periodontal care at a university dental hospital were enrolled in the study. Teeth extracted after periodontal diagnosis and due to periodontal reasons were recorded. Clinical and sociodemographic variables were analyzed, considering the risk of short-term tooth loss. This study followed the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines for development and validation, with two cohorts considered as follows: 455 patients in the development phase and 99 in the validation phase. As a result, it was possible to compute a predictive model based on tooth type and clinical attachment loss. The model explained 25.3% of the total variability and correctly ranked 98.9% of the cases. The final reduced model area under the curve (AUC) was 0.809 (95% Confidence Interval (95% CI): 0.629 - 0.989) for the validation sample and 0.920 (95% CI: 0.891 - 0.950) for the development cohort. The established model presented adequate prediction potential of early tooth loss due to periodontitis. This model may have clinical and epidemiologic relevance towards the prediction of tooth loss burden.

Heart failure (HF) is a global health concern with rising incidence and poor prognosis. While the essential role of nutritional and dietary strategies in HF patients is acknowledged in the existing scientific guidelines and clinical practice, there are no comprehensive nutritional recommendations for optimal dietary management of HF. In this review, we discuss results from recent studies on the obesity paradox and the effects of calorie restriction and weight loss, intermittent fasting, the Western diet, the Mediterranean diet, the ketogenic diet, and the DASH diet on HF progression. Many of these strategies remain under clinical and basic investigation for their safety and efficacy, and there is considerable heterogeneity in the observed response, presumably because of heterogeneity in the pathogenesis of different types of HF. In addition, while specific aspects of cardiac metabolism, such as changes in ketone body utilization, might underlie the effects of certain dietary strategies on the heart, there is a critical divide between supplement strategies (i.e. with ketones) and dietary strategies that impact ketogenesis. This review aims to highlight this gap by exploring emerging evidence supporting the importance of personalized dietary strategies to prevent progression and improve outcomes in the context of HF.

The Spiking Neural Network (SNN) has attracted more and more attention recently. It adopts binary spike signals to transmit information. Benefitting from the information passing paradigm of SNNs, the multiplications of activations and weights can be replaced by additions, which are more energy-efficient. However, its ``Hard Reset" mechanism for the firing activity would ignore the difference among membrane potentials when the membrane potential is above the firing threshold, causing information loss. Meanwhile, quantifying the membrane potential to 0/1 spikes at the firing instants will inevitably introduce the quantization error thus bringing about information loss too. To address these problems, we propose to use the ``Soft Reset" mechanism for the supervised training-based SNNs, which will drive the membrane potential to a dynamic reset potential according to its magnitude, and Membrane Potential Rectifier (MPR) to reduce the quantization error via redistributing the membrane potential to a range close to the spikes. Results show that the SNNs with the ``Soft Reset" mechanism and MPR outperform their vanilla counterparts on both static and dynamic datasets.

Tissue bioengineering has been assuming a pivotal role in implementing alternatives to conven-tional ossicular middle ear replacement prostheses to overcome extrusion while preserving acoustic properties. This in vitro study aims to explore for the first time in current literature the feasibility of a biohybrid middle ear prosthesis composed of titanium surrounded by bone ex-tracellular matrix as bio-coating. We have hereby studied the adhesion and proliferation of human adipose-derived mesenchymal stem cells (hASC) on titanium scaffolds in vitro. Moreover, we identified the osteogenic differentiation of hASC using immunofluorescence assay to analyze osteoblasts gene expression profile (Alp, Runx2, Col1a1, Osx, and Bglap) and we accounted the presence of collagen as a marker of hASC ability to secrete extracellular matrix. We utilized scanning electron microscopy to evaluate the presence of extracellular matrix on the scaffolds. Our preliminary data demonstrated the titanium ability to support human adipose-derived mesenchymal stem cell colonization, proliferation, and osteoblastic differentiation, to obtain a biohybrid device. Our experience seems encouraging, we advocate further in vivo research to corroborate our results towards bone transplantation.

The article proposes a method for determining the p-value of correlations in compositional data, i.e., those data that arise as a result of dividing the original values by their sum. Data organized in this way are typical for many fields of knowledge, but there is still no consensus on methods for interpreting correlations in such data. In a space closed by normalizing quantity, correlation coefficients behave differently than under normal conditions: their probabilities of occurrence do not coincide with those inherent in the standard scale of estimates. In the tens of the new millennium, almost all newly emerging methods for estimating correlation in compositional data began to require mandatory log-transformation of the variable values. In the method proposed here there are no log-transformations. We return to the early stages of attempting to solve the problem and rely on negative shifts in correlations in the multinomial distribution. In modeling the data, we use a hybrid method that combines the hypergeometric distribution with the distribution of any other law. During our work on the calculation method, we found that the number of degrees of freedom in compositional data measures discretely only when all the normalizing sums are equal and that it decreases when the sums are not equal, becoming a continuously varying quantity. Estimation of the number of degrees of freedom and the strength of its influence on the magnitude of the shift in the distribution of correlation coefficients is the basis of the proposed method.

There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assist safe driving during adverse climate conditions. In computer vision, previous work has demonstrated the effectiveness of deep learning in predicting weather conditions from outdoor images. However, training deep learning models to accurately predict weather conditions using real-world road-facing images is difficult due to: (1) the simultaneous occurrence of multiple weather conditions; (2) imbalanced occurrence of weather conditions throughout the year; and (3) road idiosyncrasies, such as road layouts, illumination, road objects etc. In this paper, we explore the use of focal loss function to force the learning process to focus on weather instances that are hard to learn with the objective to help address data imbalance. In addition, we explore the attention mechanism for pixel based dynamic weight adjustment to handle road idiosyncrasies using state-of-the-art vision transformer models. Experiments with a novel multi-label road weather dataset show that focal loss significantly increases the accuracy of computer vision approaches for imbalanced weather conditions. Furthermore, vision transformers outperforms current state-of-the-art convolutional neural networks in predicting weather conditions with a validation accuracy of 92% and F1-score of 81.22%, which is impressive considering the imbalanced nature of the dataset.

The KARS gene encodes the aminoacyl-tRNA synthetase (aaRS) which activates and joins the lysin with its corresponding transfer RNA (tRNA), through the ATP-dependent aminoacylation of the amino acid. The KARS gene mutations have been linked to diverse neurologic phenotypes such as: neurosensorial hearing loss, leukodistrophy, microcephaly, developmental delay or regression, peripheral neuropathy, cardiomyopathy, impairment of the mitochondrial respiratory chain, hyperlactatemia, among others. This article presents the case of a Colombian pediatric patient with two pathological missense variants in a compound heterozygous state in the KARS gene.

This article reveals a best possible design for hybrid dispersion compensating fiber (HyDCF) with high birefringence established on modified broadband compensating structure through S, C and L telecommunication bands. The simulation outcome exhibits relatively higher birefringence of 3.76×10-2 at wavelength of 1550 nm. The suggested fiber also has dispersion compensation characteristics in an inclusive series of wavelengths which covers 1400-1625 nm. The reported design can achieve dispersion quantity of – 606 ps/ (nm.km) at 1550 nm effective wavelength. The reported fiber design matches the relative dispersion slope (RDS) 0.003694 nm-1 similar to single mode fiber at 1550 nm operating wavelength. This fiber demonstrates negatively flattened effective dispersion of – 2.703 ± 0.734 ps/ (nm.km) within 180 nm flat band ranging from 1460-1640 nm wavelength. It is also convenient to optical high bit rate communication systems. The low confinement loss is found 3.756×10-10 dB/m at the operating wavelength. This design also achieves highly nonlinear coefficient of 50.34 W-1km-1. In some cases, it can also be used in sensing applications.

Failure of various weight-loss programs and long-term maintenance of favorable body composition in all kinds of people is high, since the majority go back to old dietary patterns. Many studies have documented the efficacy of a plant-based diet (PBD) for body mass management, but there are opinions that maintaining a PBD is difficult. We aimed to evaluate the long-term success of a whole-food plant-based (WFPB) lifestyle program. We investigated the differences in the obesity indices and lifestyle of 151 adults (39.6 ± SD 12.5 years), who were on our program for short (0.5–<2 years), medium (2–<5 years), or long term (5–10 years). Body-composition changes were favourable for all three groups, both genders and all participants. There were no differences in relative body-composition changes (BMI, body fat percentage and muscle mass index (MMI)) between the three groups. All participants improved their BMI (baseline mean pre-obesity BMI range (kg/m²): 26.4 ± 5.6 to normal 23.9 ± 3.8, p < 0.001), decreased body mass (–7.1 ± 8.3 kg, p < 0.001) and body fat percentage (–6.4 ± 5.6 % points, p < 0.001). Those with the highest BMI at baseline lost the most of: a) BMI units, b) total body mass and c) body fat (a) (kg/m²) (–5.6 ± SD 2.9, –2.4 ± 1.8 and –0.9 ± 1.5), b) (kg) (–16.1 ± SD 8.8, –7.1 ± 5.4 and –2.5 ± 4.5) and c) (% points) (–9.5 ± SD 5.7, –6.6 ± 4.6 and –4.7 ± 5.3) for participants who had baseline BMI in obese, overweight and normal range, respectively; p_{baseline vs. current} < 0.001 for all). 85.6% (101 out of 118) of parents of underage children (< 18 years), introduced WFPB lifestyle to their children. WFPB lifestyle program provides long-term lifestyle changes for reversal of obesity and is effective transferred to the next generation.

Modeling and assessing the sustainability of machining systems has been considered to be a crucial approach to improving the environmental performance of the machining processes. As the most common machining system, the computer numerical control (CNC) milling system is a typical man-machine cooperative system where the activities of the machine tool and operator generate material and energy consumption. However, the energy consumption of the operator in the CNC milling system has often been ignored in most existing research. Therefore, existing methods fail to provide a comprehensive understanding of the sustainability of the CNC milling system. To fill this gap, an exergy loss assessment method is proposed to investigate the sustainability of the CNC milling system, where the energy consumption of the operator, the energy consumption of the machine tool, and material consumption are taken into consideration. The key performance indexes of the energy consumption of the operator, the energy consumption of the machine tool, the exergy loss, and the specific exergy loss (SEL) are analyzed and modeled. To demonstrate the feasibility of the proposed method, a case study was carried out on a three-axis machining center (XH714D), in which SEL was found to be 88.04J/mm3. The proposed method is effective to assess the sustainability of the CNC milling system, and the established exergy loss models build a good basis for exergy efficiency optimization.

Concern about the planet's future has motivated governments around the world to adopt policies that promote more responsible energy consumption. Reducing emissions of the principal cause of climate change man-made greenhouse gases requires greater system efficiency and the utilization of renewable energy sources. The generation of electricity is the single largest source of hazardous pollutants, followed by the transportation sector. This is true due to the extreme reliance on petroleum and its derivatives that these industries have. Electric mobility is receiving more attention from businesses, governments, and academic institutions as a means to reduce the negative impacts of current energy consumption. Without measures to address common technical issues, electric vehicles would strain electrical infrastructure. In order to determine the best strategy for switching operations and feeder reconfiguration, this research presents a helpful hybrid genetic algorithm particle swarm optimization (HGAPSO) method. Meet transmission limits while decreasing real power losses and enhancing the system's bus voltage profile. The effects of HGAPSO on power losses and voltage distributions in the network are analyzed. Then, a report is produced contrasting GA and PSO. This cross-breeding of GA and PSO led to HGAPSO. This important problem can be solved more effectively with the use of new or improved algorithms. To find the optimal reconfiguration and properly rearrange transmission network interconnection, it employs a plethora of heuristic optimization algorithms. The objective is to limit bus voltage variations while maintaining the system's radial topology and minimizing power usage. The reliability and performance of the procedure were evaluated using MATLAB's IEEE 33-bus communication network. The outcomes demonstrate the effectiveness of the proposed technique, which cuts down on power waste during standalone runs and speeds up the processing time.

This article presents the theoretical study, numerical simulation and the fabrication of a phase shifter and stub resonator in microstrip ridge gap waveguide (MGCW) technology with the use of liquid crystal (LC) in its substrate as a reconfigurable material. The phase shifter and the stub resonator are filled with LC and thanks to LC's dielectric anisotropy properties, the phase shift and resonance response can be easily controlled using an external electric or magnetic bias field. The phase shifter was designed to operate in the range of 10 to 20 GHz and the resonator was designed to operate in the range of 7.8 to 8.8 GHz. The phase shifter's insertion losses, associated with both parallel and perpendicular LC's permittivity, as well as the phase response were computed and measured. Finally, the figure of merit (FoM) was extracted. The resonator's frequency response, associated with both parallel and perpendicular LC's permittivity, was computed. The resonator's frequency responses, provided different polarization voltages, were measured and compared to the simulation results. The technological issues were discussed. The good agreement between simulation and measurement results establishes this technology as a viable approach to the development of RF reconfigurable devices.

Chlorhexidine digluconate (CHX) has shown the ability to significantly reduce inflammation and marginal bone loss (MBL) at 1-year follow-up but limited data exists regarding its long-term efficacy in peri implant stability. The objective was to compare the long-term effects of a placebo gel (16 patients in Group A) or a 0.20% CHX gel (15 patients in Group B) used at each previous surgical and prosthetic stage. Follow-ups were conducted in 2022, investigating by biological, radiological, clinical, mechanical evaluation the long-term effects. Data were statistically analyzed. The research achieved a 96.7% implant success rate over five years, but 41.9% of patients didn't attend annual oral hygiene check-ups. The average MBL was 1.04 ± 0.39 mm, with no significant differences between the two groups. Notably, patients who attended regular periodontal check-ups experienced significantly less MBL than those who didn't (p &lt;0.05). At five years, the effects of CHX were reduced, with both groups showing moderate bone loss. The results suggest early disinfection could enhance both short and long-term outcomes. In fact, patient with initial minor MBL preserve this advantage also at 5 years of follow-up. Additionally, the data underscore the importance of annual check-ups in early detection and management of biological and mechanical complications.

In the field of computational fluid dynamics (CFD) for rotorcraft, two significant challenges are resolving the complex vortex structures in rotor wakes, and representing the moving rotor blades in the ambient airflow. This study addresses the first challenge by using a third-order unstructured finite volume solver, which, compared to its second-order counterpart, has substantially lower dissipation. Consequently, even relatively coarse meshes are capable of resolving small vortices. With this background flow field solver, the second challenge is addressed by modeling each rotor as an Actuator Disk Model (ADM), or describing individual rotor blades as actuator lines, designated as the Actuator Line Model (ALM). Both of these models are equipped with an improved correction for aerodynamic losses at blade tips, which is thoroughly presented in the methodology section. The numerical experiments section centers on analyzing errors linked to various sampling approaches. Additionally, the article discusses comparisons between vortex theory and ALM, specifically regarding calculations for fixed-wing aircraft. Furthermore, high-order precision and parallel efficiency are exemplified in scenarios encompassing rotors engaged in both hovering and forward flight rotors. The results in this paper demonstrate that the combination of the ALM/ADM with the improved tip loss correction and the third-order finite volume solver presents a new way of developing efficient tools for the aerodynamic analysis of helicopter rotors.