Intermediate CAG expansions in the gene ataxin-2 (ATXN2) are a known risk factor for ALS, but little is known about their role in FTD risk. Moreover, their contribution to the risk and phenotype of patients might vary in populations with different genetic background. The aim of this study was to assess the relationship of intermediate CAG expansions in ATXN2 with the risk and phenotype of ALS and FTD in the Spanish population. Repeat-Primed PCR was performed in 620 ALS and 137 FTD patients in three referral centers of Spain to determine the exact number of CAG repeats. In our cohort, ≥27 CAG repeats in ATXN2 was associated with a higher risk of developing ALS (odds ratio [OR] = 2.666 [1.471-4.882]; p = 0.0013), but not FTD (odds ratio [OR] = 1.446 [0.558-3.574]; p = 0.44). Moreover, ALS patients with ≥27 CAG repeats in ATXN2 showed shorter survival rate compared to those with <27 repeats (hazard ratio [HR] 1.74 [1.18, 2.56], p = 0.005), more frequent limb onset (odds ratio [OR] = 2.34 [1.093-4.936]; p = 0.028), as well as family history of ALS (odds ratio [OR] = 2.538 [1.375-4.634]; p = 0.002). Intermediate CAG expansions of ≥27 repeats in ATXN2 are associated with ALS risk, but not with FTD, in the Spanish population. ALS patients carrying an intermediate expansion in ATXN2 show more frequent limb onset, but worse prognosis than those without expansions. In patients carrying C9orf72 expansions, the intermediate ATXN2 expansion might increase the penetrance and modify the phenotype.

Exogenous melatonin, a nutraceutical for maintaining a healthy sleep-wake cycle and managing sleep disorders, requires large, repeated doses due to low bioavailability and short half-life. This necessitates the development of a sustained-release formulation with a longer half-life and sustained plasma concentration. Therefore, exogenous novel 5 mg sustained-release melatonin capsules (Melatonin-SR, test product) were formulated. This open-label cross-over study compared the pharmacokinetics (maximum concentration [Cmax], time to reach Cmax [Tmax], area under the curve [AUC] and elimination half-life [t1/2]) and safety of Melatonin-SR with 5 mg immediate-release melatonin capsules (Melatonin-IR, reference product) after single-dose oral administration in healthy fasting adults. Sixteen participants (aged 18-45 years) were randomized (1:1) to receive either Melatonin-SR or Melatonin-IR in two periods with a 7-day washout period. Melatonin-SR reported lower Cmax (11446.87 pg/mL) compared to Melatonin-IR (22786.30 pg/mL). The mean Tmax of Melatonin-SR and Melatonin-IR was 1.26 h and 0.87 h, respectively. The mean t1/2 of Melatonin-SR (5.10 h) was prolonged by five-fold compared to Melatonin-IR (1.01 h). One adverse event (vomiting) was reported following administration of the Melatonin-IR. Melatonin-SR resulted in higher and sustained plasma melatonin concentrations for an extended period and was well-tolerated. Hence, Melatonin-SR may be a promising nutraceutical for maintaining healthy sleep.

The hyper-arid environment is characterized by high inter-annual climatic fluctuations. The yearly average rainfall can change substantially for several years, forming wet or dry sub-periods. Observing rainfall trends over a sub-period can lead to a false perception of a change in the trend but may in fact represent a periodic cycle when examined on a larger time scale. We aimed to better characterize the rainfall regime prevailing in the hyper-arid Arava Valley (Israel/Jordan), and to examine the response of vegetation to annual rainfall. We hypothesized that annual and perennial vegetation would respond differently to wet and dry sub-periods, and that grazing activities will impact vegetation growth. We used a time series of monthly rainfall, from which we calculated Standard Precipitation Index (SPI), and calculated proxies of perennial and annual vegetation over the last four decades using Landsat-derived Normalized Difference Vegetation Index (NDVI). We found no clear trend in rainfall amounts during this period, however we did identify wet and dry sub-periods which were statistically distinct in rain and in vegetation patterns from each other. The highest levels of correlation between rainfall and the NDVI derived proxies were found when examining average rainfall over a period of two- three years for the annual vegetation and over four years for perennial vegetation. Using the Mann-Kendall test, we identified a time lag of two to four years, with the proxies of annual vegetation responding faster than the proxies of perennial vegetation, to shifts between wet and dry sub-period. In addition, we found a consistent difference between natural vegetation cover in Jordan (grazed) and Israel (non-grazed), favoring the development of natural vegetation on the Israeli side. We conclude that integrating between long-term remote sensing satellite imagery and climatic records revealed the greater resilience of perennial vegetation in the hyper-arid region to climatic fluctuation, and enabled us to identify the vegetation’s sensitivity to anthropogenic impact.

Since the creation of the first parallel electrical power systems around the world, the rotor angle stability of synchronously operating generators has been one of the most crucial and challenging problems. In modern electricity networks, involving Kazakhstani, where the renewable energy technologies are rapidly penetrating, and the issue of stability takes on even greater importance due to the technical shortcomings of inverter-based generation. In this framework, an analysis of rotor angle transient stability was carried out when replacing existing synchronous generators with doubly fed induction generators under a certain pre-emergency mode. A critical proportion of active power generation by DFIG units was identified at which transient stability can still be maintained due to the sufficient stored kinetic energy of the synchronous machines remaining in operation. In addition, 2 simple solutions were investigated to improve transient stability, such as an increased time of the automatic reclosure operation and the use of special load shedding automation. For the analysis, a real power system of Mangystau region in Kazakhstan was considered, and the PowerWorld software was used.

COVID-19 has substantially influenced healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. Healthcare practitioners have used AI systems for investigating, anticipating, and predicting diseases, through means including medication development, clinical trial analysis, and pandemic forecasting. This study proposes the use of AI to predict disease severity in terms of hospital mortality among COVID-19 patients. Methodology: A cross-sectional study was conducted the approval from the Research Ethics Committee of King Abdulaziz University (KAU), Saudi Arabia. The study used sequential sampling approaches to include 50 Real-Time Polymerase Chain Reaction (RT-PCR) positive COVID-19 patients from KAU's coronavirus isolation wards. A pre-designed form was used to collect each patient's demographic information, including age and gender, signs and symptoms, illness severity (mild, moderate, severe), and laboratory findings. Furthermore, the length of the hospital stay and the result, whether the patient recovered or died, were reported. Results: The study involved 50 patients with varying degrees of disease severity, most of whom suffered from fever, fatigue, cough, sore throat, and diarrhoea. Laboratory analysis of COVID-19 patients showed increased white blood cell and platelet counts, with C-reactive protein levels above normal. Elevated LDH levels indicated possible tissue damage, while ferritin levels were elevated. Other enzymes were in the normal range, but bilirubin levels were slightly elevated. Overall, the patient's lab results indicated inflammation and possible blood clot formation. The study evaluated the predictive accuracy for outcomes and mortality of COVID-19 patients based on factors such as CRP, LDH, Ferritin, ALP, Bilirubin, D-dimers, and hospital stay (p-value ≤0.05). The predictive accuracy mortality of patients with COVID-19 using AI showed Hospital stay, D-Dimers ALP, Bilirubin, LDH, CRP, and Ferritin significantly affected hospital mortality. (p ≤ 0.0001). Conclusion: Artificial Intelligence is crucial for identifying early coronavirus infections and monitoring patient conditions. It improves treatment consistency and decision-making via the development of algorithms. AI can track the crisis at various scales, facilitate research, and aid in developing treatment regimens, prevention strategies, and drugs and vaccines. It also aids in monitoring health and facilitating research on the virus.

Energy poverty in the Global South generally focuses on the lack of basic access to modern fuels and energy carriers. Impoverished people rely primarily on traditional biomass as a source of energy. This situation of basic resource deprivation, in which almost one third of the world's population lives, masks other relevant features of the problem. Current assessments of energy poverty in impoverished areas and the mitigation strategies being implemented derive from the development agenda and, with variations in detail and scope, highlight electricity connections and access to clean cooking fuels as guarantors of progress. However, a comprehensive understanding of energy poverty requires going beyond basic access, building on the interactions between the supply of energy sources and carriers, the provision of energy services and its impact on decent living conditions. Several studies in the last decade have attempted to construct an assessment framework centered on energy services to deal with these interactions. This work discusses the relevant dimensions in the framework (supply, services and impact in wellbeing), reviews the multidisciplinary work available in each aspect, presents a range of proposed taxonomies, and discusses the different issues. A detailed framework is proposed for the integrated assessment of the supply of energy carriers and energy equipment, the provision of relevant energy services, and the improvements obtained in living conditions.

Petit vert (scientific name: Brassica oleracea var. gemmifera DC. × Brassica oleracea var. acephala DC.) is a new variety of vegetable created by crossbreeding kale and brussels sprouts (Brassica oleracea species). The present study aimed to identify biologically active compounds in extracts of the outer leaves of Petit vert by purification and to examine their biological activities. Dried and powdered outer leaves of Petit vert were extracted, fractionated, and purified to isolate active compounds. Nuclear magnetic resonance and mass spectrometries were performed to elucidate the structures of five compounds (1‒5). The compounds isolated from Petit vert leaves were glycosides that contained kaempferol, quercetin (flavonol), or sinapinic acid (phenylpropanoid).　Glucose uptake in cultured C2C12 murine myoblasts in the absence of insulin was significantly increased by these compounds, kaempferol, sinapic acid, and ferulic acid, while that in the presence of insulin was also significantly increased by compounds 3 and 4, kaempferol, and sinapic acid. The present study reports for the first time the isolation of five compounds containing sinapic acid from the outer leaves of Petit vert and their stimulation of glucose uptake in cultured C2C12 murine myoblasts. The results obtained herein suggest the potential of these compounds to effectively attenuate hyperglycemia and maintain muscle strength by promoting glucose metabolism in muscle cells.

With the use of linear regression modeling, this study sought to forecast and analyze rainfall patterns in Ethiopia. The dataset included historical rainfall data as well as pertinent independent variables. The R-squared score and mean squared error (MSE) were used to assess the effectiveness of the linear regression model. The results showed that the model's ability to forecast and analyze rainfall patterns was not very good. Only around 5.3% of the variance in rainfall could be explained by the chosen independent variables, according to the R-squared score of 0.053. This low number indicated that there was little correlation between the selected variables and rainfall, and that a sizable percentage of the variation in the dependent variable was not captured by the model. The model's accuracy in forecasting rainfall was shown to be quite low by the MSE error, with significant squared discrepancies between the expected and actual amounts. These results support the notion that the linear regression model as it is now designed is not appropriate for studying or forecasting Ethiopia's rainfall patterns. Alternative modeling approaches, the inclusion of other pertinent factors, and the assurance of the availability of high-quality data that faithfully captures the intricate dynamics of rainfall in the area all require more investigation.

With the compelling popularity of integrated sensing and communication (ISAC), WiFi sensing has drawn increasing attention in recent years. Starting from 2010, WiFi CSI-based wireless sensing has enabled various exciting applications such as indoor localization, target imaging, activity recognition and vital sign monitoring. In this paper, we retrospect the up-to-date achievements of WiFi sensing using commodity-off-the-shelf (COTS) devices over the past 5 years in detail. Specifically, this paper first presents the background of CSI signal and related sensing models. Then, recent researches are categorized from two perspectives, namely according to their application scenario diversity and corresponding sensing methodology difference respectively. Next, this paper points out the challenges faced by WiFi sensing including domain dependency and sensing range limitation. Finally, three imperative research directions are highlighted, which are critical for realizing more ubiquitous and practical WiFi sensing in real-life applications.

Left displaced abomasum (LDA) is a multifactorial disease of cattle that occurs mainly during the transition postpartum period and is characterized by a decrease in milk production and an increased risk of culling. Despite the widespread prevalence of this pathology, its etiology remains unclear. It is believed that unbalanced nutrition, disturbances in energy and calcium metabolism in the post-calving period lead to impaired motility of the gastrointestinal tract of cows. However, studies have been conducted confirming the hereditary nature of predisposition to this disease. Currently, the search for genetic determinants reliably associated with predisposition to LDA is actively underway. The aim of our study is to identify genetic associations characterizing the genomic variability of susceptibility to LDA in Holstein cattle in the Leningrad region of the Russian Federation. The object of the study was 360 highly productive dairy cows divided into two groups: with LDA and healthy ones (control). Fourteen candidate SNPs were found to be significantly associated with leftward displacement of the abomasum (P<1e-4). When performing functional annotation of genes containing associated polymorphisms or located close to them, candidate genes presumably associated with the development of LDA were identified: ABCB11, SRP72, RGS18, SOX4, GSG1L, FBXL19, PNPLA4.

Manual material handling (MMH) significantly impacts worker health and productivity, often leading to musculoskeletal disorders (MSDs) primarily in the lower back. As a novel assistive technology, exoskeletons may serve as ergonomic tools to mitigate these work-related MSDs. It is essential to examine exoskeletons from the users’ perspectives before their widespread implementation in occupational settings. This study investigates the effectiveness of a passive back-support exoskeleton (BExo) in reducing perceived physical exertion and improving ergonomic safety in manufacturing context. Twenty-two college students were recruited to perform MMH tasks in a controlled lab environment, both with and without the BExo, followed by completing a survey questionnaire on various aspects of the BExo. Using ANOVA, the study analyzed biomechanical exertion across various body parts and tasks. The findings indicate that the BExo substantially alleviates discomfort in the low back, shoulders and knees, thereby, enhancing ergonomic posture and reducing fatigue. These results underscore the potential of passive exoskeletons to boost workers’ safety and efficiency, providing valuable insights for future ergonomic strategies in industrial setting.

Globally, habitat fragmentation has increased the proximity between wildlife, humans, and emerging predators such as free-ranging dogs. In these fragmented landscapes, encounters between primates and dogs are escalating, with primates often falling victim to dog attacks while navigating patchy landscapes and fragmented forests. We aim to investigate how these primates deal with the simultaneous threats posed by humans and predators, specifically focusing on the adaptive strategies of Central Himalayan Langur (CHL) in the landscape of fear. To address this, we conducted a behavioral study on the CHL in an agro-forest landscape, studying them for a total of 3912 hours over two consecutive years. Our results indicate that, compared to their most common resting behavior, CHL allocate more time to feeding and locomotion, and less time to socializing in the presence of humans and predatory dogs. Additionally, they exhibit increased feeding and locomotion and reduced social behavior in agro-forest or open habitats. These behavioral patterns reflect adaptive responses to the landscape of fear, where the presence of predators significantly influences their behavior and resource utilization. This study suggests measures to promote coexistence between humans and wildlife through the integration of effective management strategies that incorporate both ecological and social dimensions of human-wildlife interactions.

Understanding vaccine hesitancy in organ transplant recipients (OTR) is critical, given clear, alt-hough attenuated, benefits from vaccination. Adult OTR were surveyed regarding vaccine-related values and a novel outcome variable called Vaccine Acceptance Composite Score (VACS) was built as the average Likert score of 7 domains of vaccination confidence. Of 46 OTR included (93.5% kidney transplant recipients), 32.6% were female, 13.3% Black, 6.77% Hispan-ic/Latino/a/x; median age was 58 years. Patients were most concerned about COVID-19 vac-cine-associated risks (46.3%), its potential effect on allograft (47.6%) and motives of government officials involved with vaccine policy (55.6%). Politically conservative patients were likely to have a significantly lower VACS, while those who lived with someone ≥65 had a higher VACS. The VACS was not significantly associated with race, income, religious beliefs, comorbidities, COVID-19 history, or influenza vaccination status. Higher VACS was significantly associated with ≥3 and ≥4 COVID-19 immunizations. This study highlighted political beliefs and elderly household members as correlates of vaccine acceptance among OTR. The VACS may be a useful tool to help standardize multifaceted analyses in vaccination-focused behavioral research, as well as identify individuals and groups at risk for vaccine hesitancy, who may benefit from tai-lored outreach and educational interventions.

Stroke is the number one cause of morbidity in the United States and number two cause of death worldwide. There is a critical unmet medical need for more effective treatments of ischemic stroke, and this need is increasing with the shift in demographics to an older population. Recently, several studies have reported the therapeutic potential of stem cell derived exosomes as new candidates for cell-free treatment in stoke. This review focuses on the use of stem cell-derived exosomes as a potential treatment tool for stroke patients. Therapy using exosomes can have a clear clinical advantage over stem cell transplantation in terms of safety, cost, and convenience, as well as reducing bench-to-bed latency due to fewer regulatory milestones. In this review article, we focus on 1) the therapeutic potential of exosomes in stroke treatment, 2) the optimization process of upstream and downstream production, and 3) preclinical application in stroke animal model. Finally, we discuss the limitations and challenges faced by exosome therapy in future clinical applications.

Angiostrongylus cantonensis, a zoonotic parasite, can invade the human central nervous system (CNS) and cause acute eosinophilic meningitis or eosinophilic meningoencephalitis. Mice infected with A. cantonensis show elevated levels of pro-inflammatory cytokines, plasminogen activators, and matrix metalloproteinase-9, resulting in disruption of the blood-brain barrier (BBB) and immune cell infiltration into the CNS. Caveolin-1 (Cav-1) regulates the permeability of the BBB, which affects immune cells and cerebrospinal fluid. This intricate interaction ultimately fuels the progression of brain damage and edema. This study aims to investigate the regulatory role of Cav-1 in the pathogenesis of meningoencephalitis induced by A. cantonensis infection. We investigate pathological alterations by triphenyl-tetrazolium chloride, brain water content, BBB permeability, Western blot analysis, and gelatin zymography in BALB/c mice after A. cantonensis. The study evaluates the critical role of Cav-1 regulation through the TLR4 / MyD88 signaling pathway, modulates tight junction proteins, influences BBB permeability, and contributes to brain damage in A. cantonensis-induced meningoencephalitis.

High-resolution wave measurements at intermediate water depth are required to improve coastal impact modeling. Specifically, such data sets are desired to calibrate and validate models, and broaden the insight on the boundary conditions that force models. Here, we present a wave data set collected in the North Sea at three stations in intermediate water depth (6-14~m) during the 2021/2022 storm season as part of the RealDune/REFLEX experiments. Continuous measurements of synchronized surface elevation, velocity and pressure were recorded at 2-4~Hz by Acoustic Doppler Profilers and an Acoustic Doppler Velocimeter for a 5~month duration. Time series were quality-controlled, directional-frequency energy spectra were calculated, and common bulk parameters were derived. Measured wave conditions vary from calm to energetic with 0.1-5.0~m sea-swell wave height, 5-16~s mean wave period and W-NNW direction. Nine storms, i.e. wave height beyond 2.5~m for at least six hours, were recorded including the triple storms Dudley, Eunice and Franklin. This unique data set can be used to investigate wave transformation, wave nonlinearity and wave directionality for higher and lower frequencies (e.g. sea-swell and infragravity waves) to compare with theoretical and empirical descriptions. Furthermore, the data can serve to force, calibrate and validate models during storm conditions.

Quantifying an imaging modality’s ability to reproduce results is important for establishing its utility. In Magnetic Resonance Spectroscopic Imaging (MRSI), new acquisition protocols are regularly introduced, which improve upon their precursors with respect to signal-to-noise ratio (SNR), total acquisition duration, and nominal voxel resolution. This study has quantified the within-subject and between-subject reproducibility of one such new protocol (reduced-Field-of-View-Density-Weighted-Concentric-Ring-Trajectory (rFOV-DW-CRT) MRSI) by calculating the Coefficient of Variance of data acquired from a test-retest experiment. The Posterior Cingulate Cortex (PCC) and the Right Superior Corona Radiata (SCR) were selected as the Regions of Interest (ROIs) for Grey Matter (GM) and White Matter (WM), respectively. CVs for between-subject and within-subject are consistently around or below 15% for Glx, tCho, and Myo-Ins and below 5% for tNAA and tCr.

Concrete-filled steel columns have been extensively used in the world due to having all suitable characteristics of concrete and steel, more ductility, increasing concrete confinement using steel wall, large energy-absorption capacity and appropriate fire behavior. In present paper, concrete-filled steel square columns have been simulated under the influence of blast load using ABAQUS software. These responses will be compared for scaled distances based on the distance to source and weight of explosive material. As result, it can be seen that although concrete deformation has been restricted using steel tube, but inner layer of concrete has been seriously damaged and column displacement will be decreased by increasing scaled distance. We also concluded that concrete-filled steel columns have high ductility and blast resistance.

Chronic eosinophilic pneumonia (CEP) is an idiopathic disorder characterized by an abnormal and marked accumulation of eosinophils in the interstitial and alveolar spaces of the lung. Idiopathic chronic eosinophilic pneumonia is reported to comprise anywhere from 0-2.5% of cases within the registries of interstitial lung disease. Diagnosis is based on the clinical constellation of symptoms, characteristic radiographic findings and peripheral blood or BAL eosinophilia, in the absence of infection or drug-induced eosinophilia. There is no consensus on the dose and duration of treatment, but most authors recommend initial doses of prednisone at 0.5–1 mg/kg/day with gradual tapering of the dose for total treatment duration of 6–12 months.

Objective: The main purpose of this study was to investigate the mental health of Cypriot citizens, living in the current difficult period of economic recession. Specific objective was to investigate the different factors (gender, age, socio-economic factors, etc.) that may affect the levels of emotional distress, anxiety and depression in visitors of the Rural Health Centre of Kofinou. Materials and Methods: The sample consisted of total 300 Cypriots who visited Kofinou Health Centre in the period between August and September 2015. For the middle-aged citizens the Greek version of the HADS scale (Hospital Anxiety Depression Scale) was administered to 150 persons (Michopoulos I., 2007), while for the visiting senior citizens (aged over 65 years) the Greek version of the GDS scale (Geriatric Depression Scale) was used (Fountoulakis K., et al., 1999). All analyses were performed with the social science analysis package SPSS (version 21). Results: HADS: A total of 150 people of average age 47±11,5 years (23-64), were 56% women, while the 77.3% stated they had drastic reduction of income as well as 46.7% suffered from chronic disease. The 36.6% and 28.7% of the visitors showed moderate and severe forms of anxiety and depression, accordingly. Higher emotional distress is associated with lower educational level (p<0,001). Moreover, patients with low income have higher levels of anxiety (p=0,003), depression (p=0,036) and total emotional distress (p=0,007), while those with chronic disease have a higher stress (p<0,001), depression (p<0,001) and total emotional distress symptoms (p<0,001) compared to non - patients. GDS: 150 patients out of which 77 were women (51.3%). The average age of participants was 72±5,5 years. 93(62%) participants declared a drastic reduction of income due to the financial crisis while 139 (92.7%) stated they had chronic disease. 53 participants (35.3%) think they have symptoms of depression after the economic crisis. The women showed higher level of geriatric depression symptoms than men (p<0.001). Higher levels of depression is associated with lower income (p=0.001). Conclusions: The study shows that stress levels, depression and emotional distress are increased in specific population groups. The main factors affecting the mental health of the participants are the presence of chronic disease, income and level of education.

Recently, deep neural networks (DNNs) have been widely used in various fields such as autonomous vehicles and smart homes. Since these DNNs can be directly implemented on edge devices, they offer advantages such as real-time processing in low-power and low-bandwidth environments. However, deployment of DNNs in embedded systems, including edge devices, exposes them to threats such as fault injection attacks. In this paper, we introduce two methods to induce misclassifications by using clock and voltage glitch-based fault attacks in devices where DNN models are executed. As a result of experiments on a microcontroller implemented with a DNN for image classification, we show that glitch injection attacks can lead with high probability to serious misclassifications. Furthermore, we propose a countermeasure to glitch attacks on the softmax function, and confirm that this method is effective in preventing misclassifications.

The coronavirus SARS-CoV-2 is the causative agent for the COVID-19 first registered in Wuhan, China and responsible for more than 6 million deaths worldwide. Currently, RT-PCR is the gold standard method for diagnosing COVID-19. However, serological tests are needed for screening acute disease diagnosis and screening large populations during the COVID-19 outbreak. Herein we described the development of an in-house enzyme-linked immunosorbent assay (ELISA) for detecting levels of anti-Spike-1-RBD IgM antibody in well-defined serum/plasma panel for screening and identifying subjects infected with SARS-CoV-2 in a Latin population. We found that, compared to the RT-PCR as standard method, the in-house CovIgM-ELISA displayed sensitivities of 96.15% and 93.22% for samples collected up to 30 or 60 days after infection, respectively, as well as 95.59% specificity with 97.3% accuracy. The agreement kappa value (κ) of our CovIgM-ELISA was substantial when compared to RT-PCR (κ =0.873) and to SCoV-2 DetectTM IgM ELISA (κ = 0.684). The IgM levels detected in the population positively correlate with the neutralizing activity against the Wild-type, Alpha and Delta variants but failed to neutralize Omicron. These data indicate that our in-house CovIgM-ELISA is a compatible performing assay for the detection of SARS-CoV-2 infection.

Swarm electrification-driven communities face significant challenges, including implementing advanced distributed control in areas with limited ICT access and establishing trust among villagers hesitant to grant access to their assets. This paper proposes a distributed DC microgrid architecture for P2P energy exchange in these communities, ensuring stability and effective exchange operation. By implementing a Blockchain marketplace specifically designed to suit the rural context, the proposed architecture ensures tracing of exchange transactions to fairly settle participants. Validation experiments demonstrate its efficacy in achieving peak shaving. It provides 11% of the requester's total demand from the community even while maintaining the constraint of reducing discharge-charge cycles to one per day, thereby preserving battery life. Additionally, the solution reduces prosumer production losses by 16% of the total PV production.

Addressing the issue of low bonding strength in Ti/Mg laminated composites due to interfacial oxidation, this study employs a differential temperature rolling method using longitudinal induction heating to fabricate Ti/Mg composite plates. The entire process is conducted under an argon gas protective atmosphere, which prevents interfacial oxidation while achieving uniform deformation. The effects of reduction on the mechanical properties and microstructure of the composite plates are thoroughly investigated. Results indicate that as the reduction increases, the bonding strength gradually increases, mainly attributed to the increased mechanical interlocking area and a broader element diffusion layer. This corresponds to a transition from brittle to ductile fracture at the microscopic tensile-shear fracture surface. When the reduction reaches 47.5%, the Ti/Mg interfacial strength reaches 63 MPa, which is approximately a 20% improvement compared to the bonded strength with previous oxidation at the interface. Notably, at a low reduction of 17.5%, the bonding strength is significantly enhanced by about 1 time. Additionally, it is found that a strong bonded interface at high reduction is beneficial in hindering the propagation of interfacial cracks during tensile testing, enhancing the ability of the Ti/Mg composite plates to resist interfacial delamination.

Glioblastoma is a highly malignant brain tumor consisting of heterogeneous cellular population. The transformed metabolism of glioblastoma cells supports their growth and division on the background of their milieu. One might hypothesize that the transformed metabolism of a primary glioblastoma could be well adapted to limitations in the variety and number of substrates imported into the brain parenchyma and present it their microenvironment. Additionally, the phenotypic heterogeneity of cancer cells could promote the variations among their metabolic capabilities regarding the utilization of available substrates and release of metabolic intermediates. With the aim to identify the putative metabolic footprint of different types of glioblastoma cells, we exploited the possibility for separation of polar and ionic molecules present in culture media or cell lysates by hydrophilic interaction liquid chromatography (HILIC). The mass spectrometry (MS) was then used to identify and quantify the eluted compounds. The introduced method allows the detection and quantification of more than 150 polar and ionic metabolites in single run, those may be present either in culture media or cell lysates and provide data for polaromic studies within metabolomics. The method was applied to analyze the culture media and cell lysates derived from two types of glioblastoma cells, T98G and U118. The analysis revealed that even the both types of glioblastoma cells share several common metabolic aspects, they also exhibit differences in their metabolic capability. This finding agrees with the hypothesis about metabolic heterogeneity of glioblastoma cells. Furthermore, the combination of both analytical methods, HILIC-MS, provides a valuable tool for metabolomic studies based on the simultaneous identification and quantification of a wide range of polar and ionic metabolites - polaromics.

To investigate the composition of caproic acid-producing bacterial communities in sub-high-temperature Daqu, we analyzed four different Daqu samples from Sichuan. After enriching the samples with a specialized medium, we performed high-throughput sequencing of 16S rRNA gene amplicons on the metagenomic DNA from both the Daqu samples and the enrichment cultures. Our analysis focused on identifying and characterizing the caproic acid-producing bacterial communities present in Daqu. The results revealed that enrichment increased both the diversity and abundance of potential caproic acid-producing bacteria in Daqu. The predominant caproic acid-producing bacteria, with a relative abundance greater than 1%, included Clostridium sensu stricto 18 (2.19% to 69.00%), Clostridium sensu stricto 15 (1.76% to 7.65%), as well as smaller amounts of Clostridium sensu stricto 12, Clostridium sensu stricto 1, Clostridium sensu stricto 10, Clostridium sensu stricto 7, and Caproiciproducens. Specifically, Q1 Daqu was characterized by the presence of Clostridium sensu stricto 18 and Clostridium sensu stricto 15. Q2 Daqu featured Clostridium sensu stricto 1, Caproiciproducens, and Clostridium sensu stricto 18. Q3 Daqu included Clostridium sensu stricto 18 and Caproiciproducens. Q4 Daqu contained Clostridium sensu stricto 18 and Clostridium sensu stricto 1. These findings indicate that there are variations in the composition of caproic acid-producing bacterial communities among different sub-high-temperature Daqu samples from Sichuan. This preliminary analysis provides a theoretical basis for further studies on caproic acid bacteria in Daqu and offers insights into the development of high-quality Daqu, which may enhance the quality of Nongxiangxing liquor.

The search for materials with exceptional properties has led to significant advances in the field of non-carbon diamond-like conductive thin films (DLC-CTFs). These films, synthesized by chemical vapor deposition (CVD) and other techniques, are being actively researched for their potential in high-tech applications. This literature review discusses the synthesis methods, material properties, and applications of DLC-CTFs, highlighting the role of non-carbon dopants in enhancing electrical conductivity. This literature review aims to provide a comprehensive overview of the historical development, synthesis techniques, material properties, characterization methods, and applications of DLC-CTFs, with a focus on the impact of non-carbon doping on their conductive properties. By reviewing the literature. The review also addresses the challenges faced and suggests future research directions.

False news refers to false, fake, or misleading information presented as real news. In recent years, there has been a noticeable increase in false news on the Internet. The goal of this paper was to study the automatic detection of such false news using machine learning and natural language processing techniques and to determine which techniques work the most effectively. This article first studies what constitutes false news, and how it differs from other types of misleading information. We also study the results achieved by other researchers on the same topic. After building a foundation to understand false news, and the various ways of automatically detecting it, this article provides its own experiments. These experiments were done on four different datasets, one that was made just for this article, and using 10 different machine learning methods. The results of this article were satisfactory and provided answers to the original research questions set up at the beginning of this article. This article could determine from the experiments that passive-aggressive algorithms, support vector machines, and random forests are the most efficient methods for automatic false news detection. This article also concluded that more complex experiments, such as using multiple levels of identifying false news or detecting computer-generated false news, require more complex machine learning models.

This paper presents a novel normative framework for assessing granular base materials in rigid pavements, incorporating evaluations of California Bearing Ratio (CBR) alongside hydraulic conductivity tests. A critical component of this research adheres to Federal Highway Administration (FHWA) standards, which specify a permeability coefficient range of 0.05 to 0.20 cm/sec. Our investigations indicate that increased compaction energy inversely affects permeability, highlighting a sophisticated interplay between compaction dynamics and hydraulic characteristics. The study further elucidates that CBR values are profoundly impacted by the content of coarse-grained soil, advocating for a tailored approach to base layer assessment. Multivariable CBR analysis facilitated the identification of an optimal water content, underscoring that maintaining a minimum 80% CBR—while minimizing water content—can mitigate fatigue effects and enhance structural performance under heightened energy conditions. This research proposes bespoke, scientifically validated standards that integrate local geotechnical nuances, aimed at refining material selection processes and extending the durability of pavement infrastructures through foundational yet meticulous geotechnical evaluations.

The purpose of this work is to analyze heat-treated ash wood at temperature of 185 ºC, in order to analyze its dimensional stability. For comparison, native ash/ not thermally treated wood, was be used. In order to analyze heat-treated ash wood, some main properties such as water absorption, swelling in thickness, roughness, Brinell hardness, modulus of rupture and elasticity, color change toward black with CIELab space, and many others properties were determined and processed. The results obtained after processing the experimental data have showed that heat-treated ash wood had good physical properties as absorption and swelling in thickness, and the slight increase in elasticity offers new fields of use. As a general conclusion of the work, it was established that the ash wood can be properly thermally treated, the dimensional stability characteristics being very good, and the decrease in bending resistance being almost imperceptible.

Every year, the Montreal police are confronted with countless crimes committed by criminals. These crimes affect the quality of life of city residents and impose a socio-economic burden on the city. In this study, we conduct a comparative analysis based on several machine learning algorithms to develop a model to predict the crime category in Montreal. The performance of algorithms such as eXtreme Gradient Boosting (XGBoost), Decision Trees (DT) and Random Forest (RF) were analyzed. The performance analysis takes into account the performance metrics such as precision, accuracy, recall and F1-score. This analysis was based on crime data in Montreal from 2015 to 2023. This data is characterized by a strong imbalance between crime categories. To address the data imbalance problem, a data balancing approach based on the SMOTE-ENN algorithm was adopted. In the exploratory data analysis phase, temporal trends by crime category were highlighted. The results of the analysis showed that the XGBoost algorithm outperformed the other two. Specifically, the XGBoost algorithm achieves an accuracy of 92%, while DT and RF achieve an accuracy of 86% and 84%, respectively. As a result, XGBoost was deployed via a web application using the Flask and Swagger UI Python frameworks. This study provides the Montreal police with an effective tool to better utilize their resources in fighting crime. In addition, policymakers in the city of Montreal can use this tool to identify high-risk areas and give them more attention.

The article delves into the landscape of the plant-based food industry, exploring innovative market entry strategies and their interplay with the evolving food market and shifting consumer preferences and lifestyles. Adopting a multidimensional approach, the study reflects diverse perspectives at play. Through a comparative analysis of strategic promotion and growth strategies employed by both innovative startups and established industry giants in the plant-based meat alternatives sector in Germany, the study aims to capture the challenges faced by the 'newer' wave of plant-based alternatives. This examination is particularly relevant for navigating the complexities of entering a competitive market. Striving to penetrate evolving consumer lifestyles, the study draws insightful comparisons with their "older" counterparts. In addition, through in-depth interviews with selected companies, the study investigates the entry methods of startups venturing into the German market, aiming to identify parallels and contrasts in market strategies with established brands. This provides valuable insights into innovation within the alternative food market. Furthermore, the study explores the process of brand "platformization" by scrutinizing the websites of the selected companies and analyzing customers' online reviews of their brand experience. This comprehensive analysis unveils distinctive patterns in communication strategies, brand positioning, and consumer engagement, offering insights into plant-based innovation shaping new, caring lifestyles.

Mechanical ventilation is a life-saving treatment for critically ill patients who are struggling to breathe independently due to injury or disease. Globally, large numbers of Individuals have always required mechanical ventilation per year. The Covid-19 pandemic elevated the significance of the mechanical ventilation which have played a significant role in sustaining Covid 19 infected critically ill patients who could not breathe on their own. The pandemic drew the attention of the world to the shortage of ventilators globally.This research work presents the formulation of a detailed Port Hamiltonian model of a mechanical ventilator integrated to the human respiratory system. The interconnection and coupling conditions for the various subsystem within the mechanical ventilator and the coupling between the mechanical ventilator and the human respiratory system is also presented.A structure preservation discretization is provided along side numerical simulations and results. The obtained results are found to be comparable to results presented in literature.The future work will include the design of suitable controllers for system.

In this study, we utilized the UniProt database to extract breast cancer genes Total 2658 genes in Homo sapiens are obtain. Then 166 genes are taken and after delating duplicates 158 genes were remains. These genes were then analyzed for their biological pathways using Gene Ontology enrichment analysis through the Genecodis platform, resulting in the identification of 12 unique genes involved in disease pathways. Protein-protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software, identifying ANLN,TREM1,LZTFL1,OXSM,PLA2G3,TAS2R13,ABCB10,ECT2,TRPC,BCAS3,PLA2G3,ZDHHC7 and ERBB2 as potential gene targets for breast cancer.

Vaccines are one of the most effective medical interventions, playing a pivotal role in treating infectious diseases. Although traditional vaccines comprising of killed, inactivated, or live attenuated pathogens have resulted in protective immune responses, negative consequences of their administration have been well-appreciated. Modern vaccines have evolved to contain purified antigenic proteins, epitopes, or antigen-encoding mRNAs, rendering them relatively safe. However, reduced humoral and cellular responses pose major challenges to modern vaccine platforms. Protein nanoparticle (PNP)-based vaccines have garnered substantial interest in recent years for their ability to present repetitive array of antigens for improving immunogenicity and enhancing protective responses. Discovery and characterization of naturally occurring PNPs from various living organisms such as bacteria, archaea, viruses, insects, and eukaryotes, as well as computationally designed structures and approaches to link antigens to the PNPs have paved the way for unprecedented advances in the field of vaccine technology. In this review, we focus on some of the widely used naturally occurring and optimally designed PNPs for their suitability as promising vaccine platforms for displaying native-like antigens from viral pathogens for protective immune responses. Such platforms hold great promise in combating emerging and re-emerging infectious diseases and enhancing vaccine efficacy and safety.

: This study examines the prospects of residential water demand management policy regulations in Ethiopia and their implications for sustainable water resource management. This study aims to evaluate the existing policy regulations and analyze their effectiveness in promoting efficient water use and conservation practices in residential areas. By considering the potential challenges and opportunities associated with these regulations, the research provides insights into the future of residential water demand management in Ethiopia and suggests strategies for enhancing sustainable water resource management. Integrated literature and policy document review, alongside observation and interviews, was used to assess the viability of residential water demand management policies. The assessment of current policy regulations for residential water demand management in Ethiopian urban towns has revealed significant shortcomings in promoting water conservation activities. Hence, there is a need for significant improvements in the current water resource policy guidelines, which lack specificity, particularly in residential water demand management conservation strategies. The lack of awareness among residents regarding the importance of conserving their limited water resources is evident. To address these challenges and ensure the sustainability of the water supply, the government and policy developers must engage with various stakeholders, including the community, in the process of revising current policy documents. A more precise and tailored approach is necessary for its effective implementation.

Identification and segmentation of rock particles is very important in geology, mining, engineering construction, and environmental monitoring. Traditional methods for recognizing the rock particle characteristics are time-consuming, labor-intensive, and limited by the geographical scope of the sampling sites, which makes it more difficult to represent the spatial distribution of the rock particles accurately. Recent advancements in deep learning techniques offer promising solutions to automate this process. This study aims at utilizing state-of-the-art (SOTA) Mask R-CNN deep learning algorithm from the Detectron2 framework, to capture global features of rocks that will enable the recognition of rock particles more efficiently, just from having the rock images. These images are subject to image processing techniques such as a Gaussian filter, to denoise and smoothen the image, and an Illumination Adaptive Transformer (IAT) framework, to adjust the lighting exposure; all of which add to improve the quality of the image dataset. Afterward, the Mask R-CNN and Detectron2 models are utilized in training the images using transfer learning. Experimental results from evaluating the performance of the proposed algorithms showcase the effectiveness of the approach, thereby highlighting its potential to revolutionize rock particle segmentation across various domains.

Improving the level of farmers' participation in collective action is an important way to promote rural revitalization and improve the sustainable livelihood level of farmers in underdeveloped areas. Based on 312 questionnaires of farmers in Shanxi Province, this paper investigates the status of farmers' participation in collective action, their sustainable livelihood level and the sustainable livelihood effect of collective action participation. The results show that: farmers' willingness to participate in collective action is strong, and the participation effect is obvious, but the participation frequency is low, and the participation time is less; The farmers' sustainable livelihood level is relatively low, Empirical research shows that the level of collective action participation will have a significant positive impact on Farmers' livelihood risk prevention, livelihood capital accumulation, livelihood mode selection and livelihood income improvement, and the frequency, time and value perception of participation in collective action will have varying degrees of impact on Farmers' sustainable livelihoods. In order to promote farmers' active participation in collective action, it is proposed to strengthen the construction of collective organizations Provide incentives and improve the quality and skills of farmers to enhance the collective concept and other policy recommendations.

This study introduces a low-cost solution provided by a specialized source measure unit (SMU), offering an alternative to conventional source meter units. Through precise IV curve measurements for graphene-channel FETs, the SMU demonstrates its capability in accurately characterizing these devices with minimal noise and high accuracy. This cost-effective solution presents a promising avenue for researchers and developers seeking reliable tools for sensor development and characterization, providing valuable insights into the behavior of graphene-channel FETs and the effect of surface illumination without the need for expensive equipment. Additionally, the SMU was validated with known passive and active components, along with probe station integration for microscale connection, ensuring its effectiveness in capturing accurate measurements. The SMU’s focus on collecting IV curves, coupled with its ability to identify device defects, such as Schottky junctions, contributes to its utility in quality testing for semiconductor devices. Its low-cost nature makes it accessible for various research endeavors, enabling efficient data collection and analysis for graphene-based sensor applications.

This review explores the combination of renewable energy production and agriculture through agrivoltaic, with a focus on its impact on fruit crops. As the world seeks alternatives to fossil fuels, agrivoltaics offers a promising solution by integrating solar panels with farming practices. The study examines various agrivoltaic configurations with different fruit crops, emphasizing their influence on microclimatic conditions beneath the panels and the effects on crop produc-tion. In particular, it underscores the need for tailored solutions to balance energy yield and crop productivity and quality, with different shading levels affecting fruit quality/production. De-spite ongoing research, a comprehensive understanding of agrivoltaics' potential benefits and challenges remains crucial for its successful application and implementation in sustainable ag-riculture.

Due to advances in power electronics technology and the evolution of automation devices, the number of electrical devices that are sensitive to power quality is rapidly increasing, and for this reason, users are increasing their demand for high quality. To meet power quality demands, many power conversion devices are used, including dynamic voltage restorers (DVRs). DVRs are rec-ognized as devices that can effectively manage problems such as voltage segment, swells, and harmonics. DVR control requires many samples for harmonic compensation, which has the dis-advantage of being complicated to implement due to fast digital signal processing computation and the application of the cyclic discrete Fourier transform. In this paper, a high-precision DVR system configuration is proposed that compensates for harmonics using a periodically equivalent time-interval sampling technique and a novel circular structured memory filter. The proposed circular structured multi-pointer memory filter is an effective filter algorithm for high-precision input voltage measurement because it can remove noise and compensate for the delay of the phase angle of the filter in voltage measurement. A simulation and DVR prototype system was built, and the feasibility and effectiveness of the phase angle multi-filter voltage detection method and the compensation method were verified by experiments.

The reactions of the angular ligand 4,4’-oxybis(N-(pyridin-3-yl)benzamide (L1) and 1,4-naphthalenedicarboxylic acid (1,4-H2NDC) with divalent metal salts yielded three distinct coordination polymers (CPs): {[Zn2(L1)(1,4-NDC)2]MeOH}n, 1, {[Cu(L1)(1,4-NDC)(H2O)]3H2O}n, 2, and {[Cd(L1)(1,4-NDC)]2H2O}n, 3. Complex 1 features a 2-fold interpenetrated 3D framework with the (412.63)-pcu topology, while complex 2 reveals a 1D triple-strained helical chain, and complex 3 displays a 3-fold interpenetrated 3D framework with (66)-dia topology. Additionally, the reactions of the flexible ligand N‚N’-bis(3-methylpyridyl)adipoamide (L2) afforded {[Co4(L2)0.5(1,4-NDC)3(H2O)3(µ3-OH)2]·EtOH·2H2O}n, 4, {[Zn2(L2)(1,4-NDC)2]·2CH3OH}n, 5, and [Cd(L2)(adipic)(H2O)]n (H2adipic = adipic acid), 6, exhibiting a self-catenated 3D framework with the (420.68)-8T32 topology, a 2D layer with the (413·62)-(4,4)IIb topology, and a 2D layer with the (44·62)-sql topology, respectively. The structural diversity observed in complexes 1 - 6 highlights the pivotal influence of the metal center on the degree of entanglement in CPs within mixed-ligand systems. The thermal stability and luminescent properties of complexes 1 - 3, 4, and 6 are also discussed.

Indigenous Peoples in Canada are disproportionately affected by chronic hepatitis C virus (HCV) infection. HCV prevalence in Indigenous Peoples is estimated at approximately five to 10-fold higher than it is in non-Indigenous persons, while the reported rate of newly diagnosed HCV cases in First Nations communities was six times the respective rate in the general Canadian population in 2022. This review explores the reasons underlying the disproportionate burden of hepatitis C virus (HCV) infection in Indigenous Peoples, including significant over-representation of Indigenous Peoples in the major risk categories for HCV acquisition, such as substance abuse, incarceration, homelessness or inadequate shelter, and disruption of family/social supports. The impact of these risk factors is aggravated by many access barriers to healthcare services despite the availability of universal healthcare system and free curative antiviral therapies. These stem from the legacy of colonialism, discrimination and disenfranchisement, and are exacerbated by racism in the justice and healthcare systems, stigmatization and victimization. Recent recognition of historical harms and early steps towards nation-to-nation reconciliation along with support for culturally safe, wholistic and Indigenous People-led wellness programs instill hope that elimination strategies to eradicate HCV infection in Indigenous populations will be successful in Canada.

Jump rope is a widely-applied basic training in various sports, yet it is understudied biomechanically. This study investigates the impact of cycle-tempo-induced motor control changes in elite jump rope athletes, addressing the biomechanical gap of cyclic skill control. The hypothesis posited two accelerations per jump cycle—one in front of and one behind the body—and anticipated that increased cycle frequency would alter the distribution of acceleration time within a cycle. Using 3D motion analysis, kinematic parameters were obtained and analyzed. The results confirmed the presence of two distinct accelerations per cycle. As tempo increased, the percentage of rear acceleration time increased while front acceleration time decreased, along with peak velocities increasing significantly (p&lt;0.01). Rope trajectory analysis indicated a consistent movement pattern across tempos, primarily in the sagittal plane. Variations in skill control showed shorter contact phases and reduced vertical range of motion for the center of gravity and feet at higher tempos (p&lt;0.05), along with significant reductions in joint range of motion for the lower limbs (p&lt;0.01). These findings enhance the understanding of motor control adaptations to different tempos and have practical implications for developing coaching programs aimed at optimizing performance, stability, and efficiency in jump rope training.

We propose a new methodology for long-time biopotential recording based on MEMS multisensor integrated platform featuring a commercial electrostatic charge transfer sensor. That family of sensors was originally intended for presence tracking in automotive, so the existing setup has been engineered for the acquisition of electrocardiogram, electroencephalogram, electrooculogram and electromyography, designing a dedicated front-end and writing a proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in domestic environment will be discussed in detail. The excellent results candidate this technology to provide a low power, unexplored solution to biopotentials acquisition. The technology breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, they open to additional possibilities for wearable sensing and strengthen the role of MEMS technology in medical wearable applications for long time synchronous acquisition of a wide range of signals.

The widespread proliferation of sensor nodes in the era of Internet of Things (IoT) coupled with increasing usage of wireless spectrums especially the ISM band makes it difficult to deploy real-life IoT. Currently, the cognitive radio technology enables sensor nodes communicate with each other through the licensed spectrum bands as well as the free ISM bands. Cognitive radio networks (CRSNs) are considered as a promising solution to the problem of spectrum under utilization and artificial radio spectrum scarcity. The paradigm of dynamic spectrum access allows secondary users (SUs) to utilize wireless spectrum resources which belong to primary users (PUs) with minimal interference to PUs. Due to the dynamic spectrum availability and quality, routing for SUs in multi-hop CRSNs is a challenge. In this paper, we introduce novel routing metrics that estimate both the future spectrum availability and the average transmission time with the consideration of both the global statistical spectrum usage and local instant spectrum resources. In our novel routing metrics, one retransmission is allowed and considered to reduce the probability of rerouting upon PU's arrival. Then, we propose two routing algorithms for multi-hop CRSNs. Finally, we conduct simulations, whose results show that our proposed algorithms lead to a significant performance improvement over the reference algorithm.

(1) Background: Elite sport can increase vulnerability to developing mental health pathologies. The purpose of the study is to determine the frequency at which these behavioural disorders appear in elite footballers and evaluate their relationship with other addictions. (2) Methods: A cross-sectional study was conducted between November 2020 and January 2022 on 306 participants. The variables of gambling and gaming were studied. The different groups were compared using Chi-squared tests. Probabilities exceeding 95% (p-values &lt; 0.05) and residuals results greater than 2 or less than −2 were considered significant. (3) Results: There were significant differences between the two groups in terms of alcohol (p &lt; 0.001), tobacco (p &lt; 0.001), and cannabis (p &lt; 0.018) consumption. We also found differences between those who had a history of a nervous disease (p &lt; 0.015). 6.6% of the of football players had a probable diagnosis of gambling disorder compared to 1% in the general population (p &lt; 0.007). Among the pathological and non-pathological cases of gambling in the football population, significant differences were found between those with a salary of €900–1,500/month (p &lt; 0.033) or a history of a nervous pathology (p &lt; 0.001). (4) Conclusions: This study showed that professional football players were vulnerable to mental health pathologies related to gaming.

Both aging and chronic obstructive pulmonary disease (COPD) are strongly associated with changes in the metabolome; however, it is unknown whether there are common aging/COPD metabolomic signatures and if accelerated aging is associated with COPD. Plasma from 5,704 subjects from COPDGene and 2,449 subjects from SPIROMICS were profiled using the Metabolon global metabolomics platform (1,013 annotated metabolites). Post-bronchodilator spirometry measures of airflow obstruction (forced expiratory volume at one second (FEV₁)/forced vital capacity (FVC) < 0.7) were used to define COPD. Elastic net regression was trained on never and former smokers with normal spirometry and no emphysema to create a metabolomic age score which was validated in SPIROMICS subjects. Our metabolic age score was strongly associated with chronic age in the validation cohort (correlation coefficient 0.8). COPD subjects with accelerated aging (> 7 years difference between metabolic and actual age) had more severe disease compared with those who had decelerated aging (< -7 years difference between metabolic and actual age). COPD and aging metabolites were shared more than expected (P < 0.001) with overrepresented included amino acid and glutathione metabolism pathways. These findings suggesting a common mechanism between aging and COPD and that COPD is associated with accelerated metabolic aging.

The field of biosensors has found great use for surface plasmon resonance (SPR), which has attracted attention for its multiple applications. With the goal of early cancer detection at the level of a single living cell, this research numerically investigates a very sensitive D-shaped fiber-optic biosensor based on surface plasmon resonance (SPR). The titanium oxide (TiO2) is coated on gold (Au), which is utilized as a thin-film plasmonic material in the suggested biosensor structure. The guiding of light in the fiber is modeled using the finite element method (FEM) when six different types of healthy human cells and their malignant counterparts are exposed to its sensitive region. Refractive index (RI) differences between cancer cells and healthy cells are found through the analysis of the optical spectra and their amplitudes. This enables us to identify small changes in the optical characteristics of cells that could be a sign of pathogenic alterations. In this work, we concentrated on three forms of cancer: skin cancer (basal cell) and breast cancer (MDA-MB-231 and MCF-7). The results show which parameters are optimal to ensure that the sensor performs well when used to detect the three types of cancer under study. When the suggested sensor’s performance is compared to SPR sensors that have previously been published in the literature, it becomes clear that our sensor offers great promise for early cancer detection.

(1) Background: Mixed Reality is a technology that has gained attention due to its unique capabilities for accessing and visualizing information, becoming a valuable tool for medicine, particularly for the operating room and surgical learning; (2) Methods: A systematic review was conducted following the PRISMA guidelines to answer the research questions established using the PICO framework; (3) Results: Although implementation of Mixed Reality applications in the operations room presents some challenges, when used appropriately, it can yield remarkable results. It can make learning easier, flatten the learning curve for several procedures, and facilitate various aspects of the surgical processes; (4) Conclusion: Technical complexity, equipment costs, and steep learning curves present significant obstacles to the widespread adoption of Mixed Reality and computer-assisted evaluation. The possibility of integration of distinct medical imaging modalities and innovative functionalities holds promise for clinical applications. However, it is important to consider issues related to usability, bias, and statistical analyses need consideration. Mixed Reality offers significant benefits, but there are still open challenges such as ergonomic issues, limited field of view, and battery autonomy that must be addressed to ensure widespread acceptance.

Gliomas are the most common primary brain tumors in adults. Despite multidisciplinary treatment approaches, the survival rates for patients with malignant glioma have only improved marginally, and few prognostic biomarkers have been identified. Peroxisome proliferator-activated receptor γ (PPARγ) coactivator-1α (PGC-1α) is a crucial regulator of cancer metabolism, playing a vital role in cancer cell adaptation to fluctuating energy demands. In this study, the clinicopathological roles of PGC-1α in gliomas were evaluated. Employing immunohistochemistry, cell culture, siRNA transfection, cell viability assays, western blot analyses, and in vitro and in vivo invasion and migration assays, we explored the functions of PGC-1α in glioma progression. High PGC-1α expression was significantly associated with an advanced pathological stage in patients with glioma and with poorer overall survival. The downregulation of PGC-1α inhibited glioma cell proliferation, invasion, and migration and altered the expression of oncogenic markers. These results conclusively demonstrated that PGC-1α plays a critical role in maintaining the malignant phenotype of glioma cells and indicated that targeting PGC-1α could be an effective strategy to curb glioma progression and improve patient survival outcomes.

Inherited cardiomyopathies are a diverse group of heart muscle diseases caused by genetic mutations that result in structural and functional abnormalities of the myocardium. Understanding genotype-phenotype correlations in these conditions is vital for personalized cardiac medicine, enabling targeted therapeutic strategies and predictive diagnostics. This review explores the major types of inherited cardiomyopathies—hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and restrictive cardiomyopathy (RCM)—and provides detailed insights into how different genetic mutations manifest as clinical features. The integration of multi-omics approaches and advanced disease modeling techniques has enhanced our ability to dissect these correlations. The review also discusses the implications of these findings for personalized medicine, including tailored therapeutic strategies, predictive diagnostics, and future research directions.

Assessing carbon stocks in tropical forests is crucial for understanding their role in mitigating climate change. (1) Background: However, existing assessments have underestimated certain factors that can substantially influence carbon dynamics. (2) Methods: This study focuses on one such factor - buttress roots from a tropical forest. (3) Results: Our findings reveal that a significant proportion of trees (69.57%) had 3 to 5 buttress roots per tree. The total biomass of the buttress roots and the aboveground portion of the trees with buttress roots were calculated to be 8.5 tonnes/ha and 10.7 tonnes/ha respectively. The buttress root biomass accounted for 16.18% of the total tree biomass. It was observed that the presence of buttress roots during both the rainy and dry seasons increased the soil organic carbon content by an average of 20.8% in the upslope areas with buttress roots. During the rainy season, the presence of buttress roots did not affect the heavy fraction organic carbon in the soil layers in a statistically significant way. Regardless of the season, the soil respiration rate in the areas without buttress roots was higher than that in the areas with buttress roots. The presence of buttress roots had a positive effect on soil nutrient concentration in all seasons, creating a healthier environment for trees. Tree species with buttress roots had on average 20% higher organic carbon content during both the wet and dry seasons. (4) Conclusions: These findings highlight the importance of considering buttress roots in carbon stock assessments and forest management strategies. By integrating buttress roots into carbon accounting models, we can obtain more accurate estimates of carbon stock potential and develop more effective conservation and restoration strategies for tropical forests.

The migration of gallstones from the gallbladder into the gastrointestinal tract has been recognized for over 100 years. The most common way gallstone reaches the digestive tract is through a fistula between the gallbladder and the duodenum. Large stones become impacted in the duodenum and result in gastric outlet obstruction (Bouveret's syndrome). In this report, we present a 81-years old patient with the cholecystoduodenal fistula, which was diagnosed by ultrasound and computed tomography and effectively surgically managed. Even Bouveret syndrome is rare, with a timely diagnosis followed with an appropriate treatment maybe we could decrease morbidity and mortality rates of this complication.

Nystagmus induced by applying an intense vibratory stimulus to the skull (SVIN) indicates vestibular functional asymmetry. In unilateral vestibular loss, a 100-Hz bone-conducted vibration given to either mastoid immediately causes a primarily horizontal nystagmus. Test is performed in darkness to avoid visual fixation (VF) but there is no data about how much VF affects the often-intense SVIN. The aim is to analyze the amount of reduction of SVIN when VF is allowed during testing. Thus, all patients seen in a tertiary hospital for vertigo or dizziness with positive SVIN were included. SVIN was recorded for 10 seconds for each condition: without VF (aSVINwo) and VF (aSVINw). We obtained an aSVINwo and aSVINw as average slow-phase velocities (SPV) without and with VF. VF index (FISVIN) was calculated as the ratio of SPV. Among the 124 patients included, spontaneous nystagmus (SN) was found in 25% and the mSPV (without VF) of SN was 2.62.4º/s. Mean FISVIN was 0.270.29. FISVIN was 0 in 42 patients and &gt;0 FISVIN &lt;1 in 82 (mean FISVIN 0.390.02). Fixation suppression was found in all patients with SVIN in cases of peripheral vestibulopathy. FISVIN clearly delineates two populations of patients: with or without complete visual reduction of nystagmus.

Introduction – Gender influences the presentation and progression of various diseases. Studies regarding the impact of gender on the course and outcome of HIV demonstrate conflicting data. Studies may be biased due to socioeconomic status differences between females and males and uneven gender representation, among other factors. In order to minimize the effect of non-biological elements this study cohort compares HIV males and females with similar ethnicity and social class in 2 Israeli AIDS centers. Methods –the study is a retrospective study. The cohort consists 334 females and 223 males diagnosed with HIV during the years 2000-2015. All patients were immigrants from Ethiopia who acquired HIV heterosexually and had a similar socioeconomic status. Data regarding demographics, clinical status, virological (viral load; VL), immunological (CD4) and treatment modalities were collected for each patient. Results - At the time of HIV diagnosis, males were older (43.2 ±13.2 vs. 35.4±11.95 years; p

To advance probiotic research, a comprehensive understanding of bacterial interactions with human physiology at the molecular and cellular levels is fundamental. Lacticaseibacillus rhamnosus LGG® is a bacterial strain that has long been recognized for its beneficial effects on human health. Probiotic effector molecules derived from LGG®, including secreted proteins, surface-anchored proteins, polysaccharides, and lipoteichoic acids, that interact with host physiological processes have been identified. In vitro and animal studies have revealed that specific LGG® effector molecules stimulate epithelial cell survival, preserve intestinal barrier integrity, reduce oxidative stress, mitigate excessive mucosal inflammation, enhance IgA secretion, and provide long-term protection through epigenetic imprinting. Pili on the cell surface of LGG® promote adhesion to the intestinal mucosa and ensure close contact to host cells. Extracellular vesicles produced by LGG® recapitulate many of these effects through their cargo of effector molecules. Collectively, the effector molecules of LGG® exert a significant influence on both the gut mucosa and immune system, that promotes intestinal homeostasis and immune tolerance.

Theoretical and experimental approaches have been applied to study the polymer physics under-lying compaction of DNA in the bacterial nucleoid. Knowledge of the compaction mechanism is necessary to obtain a mechanistic understanding of the process of segregation of replicating chromosome arms (replichores) during the cell cycle. In the first part of this review light microscope observations are discussed that have demonstrated that the nucleoid has a lower refractive index and thus a lower density than the cytoplasm. A polymer-physical explanation for this phenomenon was given by the theory of Theo Odijk pro-posed in 1998. By assuming a phase separation between nucleoid and cytoplasm and by imposing equality of osmotic pressure and chemical potential between the two phases, a minimal energy situation is obtained in which soluble proteins are depleted from the nucleoid, thus explaining its lower density. The theory is compared with recent views for DNA compaction that are based on mere exclusion of polyribosomes from the nucleoid or on the transcriptional activity of the cell. These new views raise the question of whether they can still explain the lower refractive index or density of the nucleoid. In the second part of this review the question is discussed how DNA segregation occurs in Esche-richia coli, in the absence of the so-called active ParABS system, present in the majority of bacteria. How is entanglement prevented of nascent chromosome arms, generated at the origin in the pa-rental network of the E. coli nucleoid? The observations of the groups of Sherratt and Hansen in 2006, that the four nascent chromosome arms synthesized in the initial replication bubble, segre-gate to opposite halves of the sister nucleoids implies that extensive intermingling of daughter strands does not occur. Based on the hypothesis that leading and lagging replichores synthesized in the replication bubble, fold into micro-domains that do not intermingle, a passive "four-excluding-arms model" for segregation is proposed. This model implies that the key for seg-regation already lies in the structure of the replication bubble at the very start of DNA replication; it explains the different patterns of chromosome arms as well as the segregation distances between replicated loci as experimentally observed.

: We explore the integration of smart pixel light modulators (SPLMs) into bidirectional optical neural networks (BONNs), highlighting their advantages over traditional spatial light modulators (SLMs). SPLMs enhance BONN performance by enabling faster light modulation in both directions, significantly increasing the refresh rate of neural network weights to hundreds of megahertz, thus facilitating the practical implementation of the backpropagation algorithm and two-mirror-like BONN structures. The architecture of an SPLM-based BONN (SPBONN) features bidirectional modulation, simplifying hardware with electrical fan-in and fan-out. Scalability limitations of the projection system restrict input and output array sizes but achieve high throughput, up to 4.3 × 10¹⁷ MAC/s with 100 layers. Energy assessments showed that the SPLM array, despite its higher power consumption compared to the SLM array, is manageable via effective heat dissipation. Smart pixels with programmable memory in the SPBONN provide a cost-effective solution for expanding network node size and overcoming scalability limitations without the need for additional hardware.

Cadmium (Cd) contaminated soil is a serious environmental problem due to its effect on crop production, soil microbial community, and risk to human health. The commercial amendments are widely used in the immobilization of heavy metal-contaminated soil because of their cost-effectiveness, eco-friendliness, and community acceptance. This study is conducted to assess the effect of sole commercial amendments including bentonite (B), talc (T), activated carbon (AC), cornstarch (CS), and their combination on exchangeable Cd content in soil, Cd uptake by the plant, and soil microbial community. The results indicated that CS and its composites (T+CS and B+CS) showed the highest effect on reducing Cd mobility in soil (up to 10% in CS), decreasing the Cd uptake by lettuce (up to 78% in T+CS), and increasing the microbial community in soil compared to the control. Furthermore, results from Biolog Ecoplate and realtime-Polymerase chain reaction indicated that the mixture amendments (B+CS and T+CS) showed the highest effect on the microbial community by increasing soil microbial population maximum of 3.81-fold difference (in B+CS) compared to the control. The results indicated that applying composite amendments provided an efficient option for immobilizing Cd in soil, reducing the Cd uptake by plants, and increasing microbial community.

This case study examined performance of rural cassava SMEs managed by small scale farmers in Busia and Kisumu Counties in the lake region of Kenya. Formal survey, focus group discussions and key informant interviews were used to collect data from farmers and stakeholders in the region. The data were analysed descriptively using proportions (percentages and proportions), means and standard deviation and inferentially, using statistical tests of significance. The findings revealed gross inefficiencies in the management of cassava SMEs. Grass-root SMEs lack professional managers and exhibit inadequate organizational, quantity and quality management, high operational costs, lack of trust from farmers, inadequate financing, weak technical, agribusiness skills and weak linkages with markets. The findings challenge promotion of farmer-led organizations in rural areas to perform additional roles such as processing and marketing functions beyond their core business which is production. To remain relevant in a competitive value chain, grass-root farmer-led SMEs need to be re structured into viable economic entities with clear market-orientation. Ordinary smallholder farmers should specialize in producing sufficient quantity and quality of agricultural commodities, while processing and marketing functions are taken up by professional entrepreneurs. This study recommends an entrepreneur model rather than farmer-led SMEs as potentially best suited in creating efficiency in the cassava value chain in the lake region of Kenya.

Excretory-secretory products (ESPs) are the main research targets for investigating the hosts and helminths interaction. Parasitic worms can migrate to parasitic sites and avoid the host immune response by secreting this product. Angiostrongylus cantonensisis an important food-borne zoonotic parasite that causes severe neuropathological damage and symptoms, including eosinophilic meningitis or meningoencephalitis in humans. Benzaldehydes are organic compounds composed of a benzene ring and formyl substituents. This compound has anti-inflammatory and antioxidation properties. Previous studies showed that 3-hydroxybenzaldehyde (3-HBA) and 4-hydroxybenzaldehyde (4-HBA) can reduce apoptosis in A. cantonensis ESPs treated astrocytes. These results on the protective effect underlying benzaldehyde have primarily focused on cell survival. The study was designed to investigate the molecular mechanisms of endoplasmic reticulum stress (ER stress) and oxidative stress in astrocytes in A. cantonensis ESPs treated astrocytes and to evaluate the therapeutic consequent of 3-HBA and 4-HBA. First, we initially established the RNA-seq dataset in each group, including Normal, ESPs, ESPs+3-HBA, and ESPs+4-HBA. We also found that benzaldehyde (3-HBA and 4-HBA) can stimulate astrocytes to express ER stress-related molecules after ESP treatment. The level of oxidative stress could also be decreased in astrocytes by elevating antioxidant activity and reducing ROS generation. These results suggested that benzaldehyde may be a potential therapeutic compound for human angiostrongyliasis to support brain cell survival by inducing the expression levels of ER stress- and oxidative stress-related pathway.

Among the most popular learning management systems available worldwide is Moodle [1]. This current study examines how learners' attitudes toward English as a foreign language (EFL) and their language proficiency are affected by Moodle's interactive language learning activities. Thirty-three undergraduate students participated in this study. We investigated the effects of engaging language learning exercises that were practiced on Moodle using an experimental re-search design. To find out if the experimental and control groups differed significantly from one another on pre- and post-measures regarding the development of language skills and attitudes toward language classrooms, a number of statistical tests were employed. We conducted data analysis using SPSS software. Results demonstrated that there were differences favoring the ex-perimental group in the language skills developments and attitudes of learners toward the lan-guage classroom. There were no apparent differences in the forming structures, speaking, or listening of the control group's learners. On the other hand, there were notable differences in the overall score, writing and reading skills, and lexical item mastery. Additionally, significant dif-ferences in the language acquisition growth of the experimental and control groups were found using an Independent Samples T-test in the post-test, with the experimental group benefiting.

Keywords: conservation; cultural pluralism; environmental regulation; community-based natural resource management; Indigenous law

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

Organisms in the wild have cryptic life stages that are sensitive to changing environmental conditions and can be difficult to survey. In this study, I used mark-recapture methods to repeatedly survey Anaea aidea (Nymphalidae) caterpillars in nature, then modeled caterpillar demography as a hidden Markov process to assess if temporal variability in temperature and density influence the survival and growth of A. aidea over time. Individual encounter histories result from the joint likelihood of being alive and observed in a particular stage, and I included hidden states by separating demography and observations into parallel and independent processes. I constructed a demographic matrix containing the probabilities of all possible fates for each stage, including hidden states, e.g., eggs and pupae. I observed both dead and live caterpillars with high probability. Peak caterpillar abundance attracted multiple predators, and survival of fifth instars declined as per capita predation rate increased through spring. A time lag between predator and prey abundance was likely the cause of improved fifth instar survival estimated at high density. Growth rates showed an increase with temperature, but the most likely model did not include temperature. This work illustrates how state-space models can include unobservable stages and hidden state processes to evaluate how environmental factors influence vital rates of cryptic life stages in the wild.

In recent years, the introduction of electric vehicles as a means of transportation has been gradually growing in most developing and developed countries, especially in terms of energy efficiency, energy diversification, and environmental sustainability. While developed countries have been trying to keep up with electric vehicle usage and buying trends, most developing countries, especially African countries, are still riddled with severe power outages and electricity load shedding. The growing adoption of electric vehicles (EVs) has led to a corresponding increase in the availability of charging infrastructure, thereby exerting notable impacts on the power grids of most African countries. Recent evidence suggests that various charging strategies and grid integration approaches are being developed to mitigate the negative impacts of electric vehicle (EV) charging and enhance the advantages of integrating EVs with the grid. However, transportation researchers have struggled to develop applicable and sensible mitigation strategies to tackle this issue, which has seriously rendered the sustainability of electric vehicle usage in Africa in serious doubt. The primary aim of this research is to provide a conceptual theoretical framework based on previous and current literature reviews on electric vehicles to examine the challenges facing the adoption of electric vehicles in Africa. In addition, we explored the environmental and economic benefits of electric vehicles in Africa regarding urban sustainability and charging infrastructure on the existing transportation infrastructure in the African transportation network. This study contributes to the expanding body of literature on electric vehicles by emphasising the significance of incorporating sustainability principles in the adoption and usage of electric vehicles in Africa. It also examines the effects of electric vehicle usage on the existing transportation infrastructure.

Background P53 gene mutation is a very common event in human neoplasia, and genetic mutations in the P53 gene in a single allele are responsible for a hereditary cancer susceptibility syndrome (Li Fraumeni). These variants encode distinct isoforms of the p53 protein, which may disrupt its transcriptional activity. These point mutant proteins are more stable than the normal protein and the mutant product accumulates at a high level that allows obtaining important information about p53 gene expression in malignant cells, especially in Chronic Lymphocytic Leukemia, (CLL). By Enzyme Linked Immune-Absorbent Assay method, (ELISA), was analyzed the frequency of p53 protein expression to 20 eligible patients diagnosed with CLL for to investigate the relationship of this protein in the stages of the disease and the impact on treatment response and survival. In ELISA technique was used the specific antibodies for isoform p53 protein, PAb 240 antibodies. These antibodies bind specifically to denatured p-53 protein. Species reactivity is for human in conformity with the prospect from Manual, Catalog No. LS-F174, Bio-Rad. Results The average concentrations of p53 proteins in 17 of 20 cases were found 16.76 μg / dl, with CV = 0.5% and the probability index p = 0.034. Very high pathological values in the 3 cases of isoform p53 protein were calculated in 2 men, (PM) in the value of 60 µg / dL, respectively at 40 µg / dL and in the case of females, (PW), in 40 µg / dL value, with the transformation into Diffuse Large Lymphoma, (DLL). Conclusion This ELISA method has proven to be a useful prognostic tool for the application of personalized treatment of on-immune therapy, in cases diagnosed with the type B-CLL.

This study explores an optimal strategy for muscle force estimation by integrating simplified cost functions with detailed muscle models, demonstrating significant advantages over traditional complex methods. Using elbow flexion as a case study, we characterize the interplay between muscle models and cost functions, showing that accurate muscle force estimations can be achieved effectively without electromyography (EMG) data while also substantially reducing computational demands. Validated against established bio-mechanical data, our models confirm their accuracy in predicting muscle behavior. This approach has profound implications for enhancing rehabilitation protocols and athletic training, providing a more accessible and adaptable tool for muscle force analysis. It broadens the applicability of muscle force estimation in clinical and sports settings and paves the way for future innovations in bio-mechanical research.

Trichinellosis is a major worldwide health concern caused by T. spiralis infection without highly efficient and safe therapeutic method so far. To solve the problem of uncomplete drug treatment schemes and the rise of anthelmintic resistance, a potential protective effect of probiotic strains against zoonotic T. spiralis infection was investigated in the framework of a new therapeutic strategy aimed at using probiotics to control parasitic zoonoses. The study was focused on L. reuteri protected against T. spiralis infection in intestinal phase throughout the detection of adult worm number, inflammatory factors mRNA expression, antioxidant enzyme expression and Nrf2 signal pathway protein expression. Mice were divided randomly into 3 groups: 1) Control group: no treatment; 2) Ts group: T. spiralis infection; 3）LAB group: T. spiralis infected after orally administered L. reuteri for 7 days. Probiotics bacteria were administered daily in a dose of 109 CFU/mL, and mice were infected with 400 T. spiralis larvae in the experimental groups. The number of adult worms was also significantly increased after administration of probiotics. Meanwhile, L. reuteri alleviate the inflammatory response caused by T. spiralis infection in intestinal has been confirmed. In addition, the promotion of antioxidant enzyme expression can be inferred that L. reuteri regulate Nrf2 signal pathways against T. spiralis infection. Our study demonstrates that L. reuteri provide protection against T. spiralis infection in intestinal. Therapeutic approaches with probiotic strains admission could help to reduce the risks of trichinellosis or complement classical anti-parasite treatments.

This study aims to provide an understanding of the preferences of student tourists regarding service attributes related to domestic accommodation. To clarify how lodging firms formulate policies for the student travel market, a “Relative to an Identified Distribution” (RIDIT) analysis in which responses are treated as ordinal data instead of equally spaced data is performed to examine the Likert-scale data obtained from 296 university students in Taiwan. In our RIDIT analytical results, respondents’ preferences are sorted in a descending importance. Of the examined attributes, “Cleanliness of rooms” is the attribute most prioritized by the students, followed by “Located in a safe neighborhood/feeling safe in the room,” and “Close to scenic area for meeting the trip requirement.” The attribute least prioritized by the students is “Interaction with other guests.” This analysis provides conclusions that differ from those obtained through commonly used techniques including the arithmetic average and exploratory factor analysis. The preference rankings indicate that managers should focus on the most prioritized service attributes and implement the necessary measures to enhance their service quality and maximize customer satisfaction.

Distributed tracing is a cutting-edge technology for monitoring, managing, and troubleshooting native cloud applications. It offers a more comprehensive and continuous observability, surpassing traditional logging methods, and is indispensable for navigating modern complex software architectures. However, the sheer volume of generated traces is staggering in distributed applications, and direct storage and utilization of every trace are impractical due to associated operational costs. This entails a sampling strategy to select which traces warrant storage and analysis. Historically, sampling methods have included a rate-based approach, often relying heavily on a manual configuration. There is a need for a more intelligent approach, and we propose a hierarchical sampling methodology to address multiple requirements concurrently. Initial rate-based sampling mitigates the overwhelming volume of traces, as no further analysis can be performed on this level. In the next stage, more nuanced analysis is facilitated based on the previous foundation, incorporating information regarding trace properties and ensuring the preservation of vital process details even under extreme conditions. This comprehensive approach not only aids in the visualization and conceptualization of applications but also enables more targeted analysis in later stages. As we delve deeper into the sampling hierarchy, the technique becomes tailored to specific purposes, such as simplifying application troubleshooting. In this context, the sampling strategy prioritizes the retention of erroneous traces from dominant processes, thus facilitating the identification and resolution of underlying issues. The focus of this paper is to reveal the impact of the sampling on troubleshooting efficiency. Leveraging intelligent and explainable artificial intelligence solutions enables the detection of malfunctioning microservices and provides transparent insights into root causes. We advocate for using rule-induction systems, which offer explainability and efficacy in decision-making processes. By integrating advanced sampling techniques with machine learning-driven intelligence, we empower organizations to navigate the complexities of large-scale distributed cloud environments effectively.

Boost-derived converters are most popular in Power Factor Correction AC-DC conversion applications and alternative energy generation systems. The proper operation of such converters relies on housekeeping power supplies providing auxiliary power for their control circuitry. The purpose of this study is to present a design-oriented analysis of a simple, passive, isolated, self-bias circuit that can provide a stabilized voltage for the control circuits of boost-derived converters. The circuit’s output voltage inherently follows the main regulated output and therefore, does not require an additional active control. The paper presents a detailed analysis of the circuit and offers design guidelines. Following the presentation of the basic idea several variations of the circuit, that can be used in special applications, are presented. The feasibility of the proposed bias circuits was verified by simulation and experiment on a laboratory prototype.

Objective Rhinosinusitis is a chronic condition characterized by frequent episodes of recurrence; its main treatment is pharmacological and this therapy should be continued for a long time until stabilization of the problem. Some patients, however, often must resort to targeted therapies for disease recurrences, which is why targeted preventive treatments are sought. Methods In the study performed, Pistacia lentiscus oil was used. Two groups of patients were studied with endoscopy, SNOT 22 and Nasal Cytology to evaluate nasal cellularity and ciliar motility. Results Pistacia lentiscus oil has the ability to reduce mucosal inflammation, by destroying and preventing the formation of biofilm. Conclusion The study showed that daily administration of Pistacia lentiscus oil significantly reduced the presence of biofilm. Moreover, continuous use of a lentiscus oil wash significantly reduced patients’ symptoms and improved their quality of life according to the Snot 22 questionnaire.

Recently, there has been considerable research on deepfake detection. However, most existing methods face challenges in adapting to the advancements in new generative models within unknown domains. In addition, the emergence of new generative models capable of producing and editing high-quality images, such as Diffusion, Consistency, and LCM, poses a challenge for traditional deepfake training models. These advancements highlight the need for adapting and evolving existing deepfake detection techniques to effectively counter the threats posed by sophisticated image manipulation technologies. In this paper, our objective is to detect deepfake videos in unknown domains using unlabeled data. Specifically, our proposed approach employs Meta Pseudo Labels (MPL) with Supervised Contrastive Learning, so-called SupCon-MPL, allowing the model to be trained on unlabeled images. MPL involves the simultaneous training of both a Teacher model and a Student model, where the Teacher model generates Pseudo Labels utilized to train the Student model. This method aims to enhance the adaptability and robustness of deepfake detection systems against emerging unknown domains. Supervised Contrastive Learning utilizes labels to compare samples within similar classes more intensively, while encouraging greater distinction from samples in dissimilar classes. This facilitates the learning of features in a diverse set of deepfake images by the model, consequently contributing to the performance of deepfake detection in unknown domains. When utilizing the ResNet50 model as the backbone, SupCon-MPL exhibited an improvement of 1.58% in accuracy compared to traditional MPL in known domain detection. Moreover, in the same generation of unknown domain detection, there was a 1.32% accuracy enhancement, while in the detection of post-generation unknown domains, there was an 8.74% increase in accuracy.

The increasing world steel production has contributed to the generation of million tons of mining residues. But, the huge demand of aggregates for the execution of pavements and the lack of raw material for this purpose, the research for new materials to be used in this area has become vital. The sandy residue that was studied was collected at Mariana– MG county, which was the result of the iron ore processing of the company Samarco S.A. This research has as objective the evaluation of the mechanical behaviour of asphalt mixtures utilizing residue from the processing of iron ore in substitution of the fine aggregate in the asphalt mixtures of the Asphaltic Concrete type, aiming its use in the rolling layer of a road pavement. To do so, the Marshall dosage of three asphalt mixtures and the mechanical tests characterized by the resilient modulus and fatigue life at a controlled tension were performed. It was also performed the determination of the optimum content by the Superpave dosage method conducted in two of these mixtures to obtain the Compaction Index and the mechanical behaviour by the Flow Number value. Concluded that the residue is compatible with all current standards for the use in asphalt mixtures, being able to be used in the CBUQ rolling layer, showing an improvement in the behaviour compared to the standard mixture.

In vitro glucose uptake in yeast cell facilitated by Abelmoschus esculentus L. (okra seed) for management of type 2 diabetes was studied. Plant material was collected, identified, processed, and stored for further use. 80% methanol was employed for extraction and sonicated to release antidiabetic-bioactive component in solution and was filtered, concentrated, freeze-dried, and fractionated using standard techniques. Glucose uptake at an initial concentration of 5mM/L and 10mM/L by the crude extract was consistent to that of the known standard drug while at 25mM/L glucose concentration was equivalent with the crude extract. Also, at 0.625 mg/mL the linear equations, and R2 demonstrations that the crude extract was higher in dose predictability than the standard drug as presented by the equation; y = 35.754x - 57.822, and R² = 0.9502 (95%). The extract-fractions were employed to evaluate the ability of yeast cell line culture to take up glucose from the system through DPPH, FRAP, lipid peroxidation and anti-diabetes effect of extract-fraction assays. Extract-fractions were found to poses antioxidant activity high enough to inhibit stress-related diseases. The extract fractions were active as drug candidates both at low and high concentrations and were better compared with the standard drug and standard antioxidant was comparable. The high bioactive extract fractions require encapsulation with a nanoparticle as a drug candidate for type 2 diabetes patients. And an animal trial of the drug candidate may be necessary to monitor the in vivo performances of the extract fractions and subsequent human volunteered trial.

Agriculture and rural regions in Europe face a number of economic, social and environmental challenges. Rural areas are active players in the EU’s green transitions. Weeding is one of the most important factors of agricultural production. New weeding techniques are being developed to enhance sustainability. Among them laser based weeding seems a promising alternative for use of chemicals. WeLASER technique is a novel technique of weed control. Its successful implementation depends on many factors related to the innovation itself, policy context, farming conditions and users’ attitudes. Survey was carried out to provide the insight on the attitudes toward the innova-tive (laser) weed control tool (device - autonomous robot). The CATI method was selected for the survey of farmers opinion and carried out in three countries: Denmark, Spain and Poland. Statistical methods were applied to analyze the results. The study provided knowledge on how farmers see the barriers and opportunities of implementing the device in practice. Positive attitudes of farmers were observed but with high expectations related to quality of the technique and systemic conditions of its implementation.

Soil-structure interaction (SSI) could affect the seismic response of structures. Since liquid storage tanks are vital structures and must continue their operation under severe earthquakes, their seismic behavior should be studied. Accordingly, the seismic response of liquid storage tanks founded on half space soil is scrutinized under different earthquake ground motions. To better comparison, the six considered ground motions are classified based on their pulse like characteristics, into two groups, named far and near fault ground motions. To model the liquid storage tanks, the simplified mass-spring model is used and the liquid is modeled as two lumped masses known as sloshing and impulsive, and the interaction of fluid and structure is considered using two coupled springs and dashpots. The SSI effect, also, is considered using a coupled spring and dashpot. Besides, four types of soils are used to consider wide variety of soil properties. To this end, after deriving the equations of motion, the MATLAB programming is employed to obtain the time history responses. Results show that although the SSI effect leads to decrease the impulsive displacement, overturning moment and normalized base shear, the sloshing (or convective) displacement is not affected by such effects due to its long period.

Nano surface finishing can be achieved from the recently developed electrophoretic deposition-assisted polishing (EPDAP) process. This state-of-art work presents a comparison of the effect of surface roughness and material removal rate on the grit size, axial load, rotational speed, polishing time, etc. for SS304, SS316L and alumina ceramics. The EPDAP process can assess the surface's characteristics' quantity, and quality on different materials which have been tried on SS316L, SS304, inner bore of SS304, and Al2O3 in our laboratory. Uniform dispersion of homogeneous micro-sized abrasive grains was accomplished by electrophoretic deposition (EPD) to create the polishing tool employed in this process. Polishing time, axial loading, grit size, and speed of rotation of the polishing tool was selected as the control parameters. The polishing tool undergoes erosion of blunt-edged-abrasive grains due to abrasion during the EPDAP process. Therefore, the polishing tool was continuously revitalized by adding fresh abrasives. The analysis of the experiments is conducted using ANOVA and the evolutionary algorithm teaching-learning method. EPDAP process revealed improved surface reflection as well as improvement in surface finish. Thus, optimization of the polishing parameters was conducted for the experiments. The surface roughness achieved from the experiments is 0.04557 microns for SS316L, 0.038 microns for SS304, and 0.404 microns for Al2O3. Therefore, 93.92%, 97.64 %, and 73.75 % of improvement in the surface finish were achieved in SS316L, SS304, and Al2O3, respectively.

In this paper, we establish the existence and uniqueness results of the fractional Navier-Stokes evolution equation using the Banach fixed point theorem, where the fractional order β is in the form of the Atangana-Baleanu Caputo fractional order. The iterative method combined with the Laplace transform and Sumudu transform are employed to find the exact and approximate solutions of the fractional Navier-Stokes equation of a one-dimensional problem of unsteady flow of a viscous fluid in a tube. Mathematica software is used to present the solutions graphically.

This research paper introduces a novel analytical and geometrical modelling approach for calculating the stiffness matrix of knitted composites. Knitted composites have gained significant attention due to their unique mechanical properties and potential applications in various industries. This paper provides a comprehensive overview of knitted composites, encompassing different types, shapes, and categories. Furthermore, it explores various modelling techniques, including analytical, geometrical, and experimental models, to accurately predict the stiffness properties of knitted composites. A thorough literature review is conducted, and detailed explanations, case studies, and relevant citations are provided to support the claims made in this paper.

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

The present study aimed to elucidate the neural mechanisms underpinning the visual recognition of morphologically complex verbs in Korean, a morphologically rich, agglutinative language with inherent polymorphemic characteristics. In an fMRI experiment with a lexical decision paradigm, we investigated whether verb inflection types (base, regular, and irregular) are processed through separate mechanisms or a single system. Furthermore, we explored the semantic influence in processing inflectional morphology by manipulating semantic ambiguity (homonymous vs. unambiguous) of inflected verbs. The results showed equivalent activation levels in the left inferior frontal gyrus for both regular and irregular verbs, challenging the dichotomy. Graded effects of verb regularity were observed in the occipitotemporal regions, with regular inflections eliciting increased activation in the fusiform and lingual gyri. In the middle occipital gyrus, homonyms showed decreased activation relative to unambiguous words, specifically for base and irregular forms. Furthermore, the angular gyrus exhibited significant modulation by all verb types, indicating a semantic influence during morphological processing. These findings support single-system theories and the connectionist framework, challenging the assumptions of purely orthographic morphological decomposition and dual-mechanism accounts. Furthermore, they provided evidence for a semantic influence during morphological processing, with differential reliance on semantic activation for regular and irregular inflections.

The main objective of this study is to analyze the impact of investment in Research & Development on the performance and value of companies, that is, to try to explain how R&D investment influences the companies’ performance. For this purpose, a dynamic panel data model was estimated using the Generalized Method of Moments, which made possible to measure the impact of R&D investment on performance through the Return on Operating Assets ratio for the Portuguese companies that invested the most between 2012 to 2019. The results show that the investment in R&D is not statistically significant in explaining the operating return on assets. However, the lagged financial performance in one period, the leverage, the asset turnover ratio and the accounts payable turnover show to have impact on the variable under study.

This paper presents our approaches for the SMM4H’24 Shared Task 5 on the binary classification of English tweets reporting children’s medical disorders. Our first approach involves fine-tuning a single RoBERTa-large model, while the second approach entails ensembling the results of three fine-tuned BERTweet-large models. We demonstrate that although both approaches exhibit identical performance on validation data, the BERTweet-large ensemble excels on test data. Our best-performing system achieves an F1-score of 0.938 on test data, outperforming the benchmark classifier by 1.18%.

Airborne distributed position and orientation system (ADPOS), which integrates multi-inertia measurement units(IMUs), data-processing computer and Global Navigation Satellite System (GNSS), serves as a key sensor in new higher-resolution airborne remote sensing, such as array SAR, multinode imaging loads and so on. ADPOS can provide multinode high accuracy spatio-temporal reference information to realize multinode motion compensation with the various state estimation methods such as Central Difference Kalman Filtering (CDKF), modifed CDKF. However, these methods focus on noise optimization based on known nonlinear models and its linear minimum variance estimation criterion, which cannot gain better estimation performance for high-dimensional nonlinear systems. For this reason, the variational optimization process based on conjugate gradient algorithm is proposed for the estimation of filtering update step, which is more efficient than linear minimum variance estimation criterion. Therefore, aiming at ADPOS with high-dimensional and nonlinearity, Central Difference Variational Filtering (CDVF) using variational optimization based on conjugate gradient algorithm for mean correction in the filtering update stage is devoted to advancing the estimation accuracy of multinode motion parameters. A real ADPOS flight test has been conducted, the experimental results show that the accuracy of the slave motion parameters has a distinct progress than the up to date CDKF, and the compensation performance is distinct.

The population growth of major cities in Iran and the lack of attention to scientific and managerial principles in urban planning in recent years have created numerous issues for citizens and urban planners, pushing cities towards instability. Tehran, as the capital of Iran, is among the cities facing numerous urban challenges. It appears that these factors will present serious challenges to the city's future sustainability. Many articles have assessed and analyzed indicators of sustainability in cities, but less attention has been paid to indicators of instability and real issues that pose challenges to urban planning sustainability. In this regard, the present study aims to identify the challenges of sustainability in urban planning by re-evaluating indicators related to urban sustainability and assessing indicators of instability in urban planning from a realistic perspective. The results indicate that the major indicators of urban planning instability have been summarized into three economic, social, and environmental indices. In the economic index, the unstable components in urban planning include uncertainty in economic policies, housing prices, non-standard housing, income, employment, unemployment, cost of living, and female-headed households. In the environmental index, the unstable components in urban planning consist of climate change, heat islands, floods, transportation management, energy security, noise and air pollution, urban traffic, water quality and quantity, waste disposal quality, urban green infrastructure, environment as an aesthetic issue, and changing the approach to the environment with changes in individuals. In the social index, the unstable components in urban planning encompass social interaction, access to facilities and services, access to social harms services, sense of identity and belonging, neighborly relations, social segregation, public participation in city affairs, and security and welfare.

Let R be a commutative ring and m, n be positive integers. We define a proper submodule N of an R-module M to be (m,n)-closed if for r∈R and b∈M, r^{m}b∈N implies rⁿ∈(N:_{R}M) or b∈N. This class of submodules lies properly between the classes of prime and primary submodules. Many characterizations, properties and supporting examples concerning this class of submodules are provided. The notion of (m,n)-modules is introduced and characterized. Furhermore, the (m,n)-closed avoidance theorem is proved. Finally, the (m,n)-closed submodules in amalgamated modules are studied.

A novel process design for damage-free and highly accurate positional integration of an optical multi-axial force sensor into a hollow tube by means of rotary swaging is introduced. Numerical simulations reveal relevant process phenomena of thin disc joining inside a pre-toothed hollow tube and help to find an optimal process design. Experimental trials show a significant effect of axial material flow and the number of tools in a rotary swaging process. By taking these effects into account, a successful form- and force-fit joining of the sensor carrying discs into the tube can be achieved. A successful joining of an optical sensor for bending forces and torque measurement shows hysteresis-free sensory behavior and thus backlash-free joining of the sensor carrier discs. The paper finishes with presenting an approach for closed-loop position controlling during the joining process and numerically investigation of the process design.

With the rapid advance in quantum computing, quantum security is now an indispensable property for any cryptographic system. In this paper, we study how to prove the security of a complex cryptographic system in the quantum random oracle model. We first give a variant of Zhandry’s compressed quantum random oracle (CStO), called compressed quantum random oracle with adaptive special points (CStOs). Then, we extend the on-line extraction technique of Don et al (EUROCRYPT’22) from CStO to CStOs. We also extend the random experiment technique of Liu and Zhandry (CRYPTO’19) for extracting the CStO query that witnesses the future adversarial output. With these preparations, a systematic security proof in the quantum random oracle model can start with a random CStO experiment (that extracts the witness for the future adversarial output) and then convert this game to one involving CStOs. Next, the on-line extraction technique for CStOs can be applied to extract the witness for any on-line commitment. With this strategy, we give a security proof of our recent compact multi-signature framework that is converted from any weakly secure linear ID scheme. We also prove the quantum security of our recent lattice realization of this linear ID scheme, by iteratively applying the weakly collapsing protocol technique of Liu and Zhandry (CRYPTO 2019). Combining these two results, we obtain the first quantum security proof for a compact multi-signature.

At 8000 m and above, a ‘death zone’ reputation afforded protection for the fragile ecosystems of the Himalaya and Korakoram ranges for thousands of years. After decades of high-altitude mountaineering, environmental impacts have been documented such as associated carbon-intensive air travel and contamination from waste. Despite the growing popularity, high-risk and fragile context of the sport, the behaviour, perspectives and experiences of high-altitude mountaineers, and the social and economic consequences have not been researched. Following an interpretive paradigm and using purposive sampling, this qualitative study conducted in-depth interviews and open-ended survey questions to collect the experiences of sixteen high-altitude mountaineers. Analysis of the qualitative data was informed by Grounded Theory with the coding process leading to the overarching category of ‘leave no trace’. The results show how the high risks to life, and growing popularity of the activity influence mountaineer’s ability to act sustainably and increase unsustainable behaviour despite a desire to ‘leave no trace’ on the mountain. Meanwhile, climate change has already and will increasingly have profound impacts on the risks associated with 8000m mountaineering. Allowing more time for acclimation would help to address the root causes of unsustainable behaviours along with better enforcement of regulations.

While the concept of a circular economy offers the solutions necessary to enable economies to move away from consumptive, wasteful practices and towards more sustainable alternatives, in practise, few economies have been able to transition to circularity. The purpose of this paper is to investigate the feasibility of a circular economy on a small-scale university campus, leveraging both research by sustainability-focused academics, and attitudes of students who generally care more about sustainability than older cohorts [1]. It begins with a literature review based on five identified pillars of circularity [2]: waste management; data management; business operations and infrastructure; policy and regulation; and education and collaboration. A qualitative methodology was adopted, involving a series of interviews with campus representatives either working or researching some element of sustainable circularity. By bringing together dedicated scholars with knowledge and capacity and a motivated consumer target, all in one location, we present a framework for a Campus Circular Economy (CCE) with potential for replication to advance sustainable consumption in campus environments and beyond.

Our world is increasingly challenged by managing the impacts of natural disasters, particularly floods, which are frequent, dangerous, and costly. Traditional flood mapping methods, reliant on Optical satellites (MSI), struggle under cloud cover which is typical during such events. Synthetic Aperture Radar (SAR) offers a promising alternative with its cloud-penetrating capability, though its use has been limited due to complexity and data labeling challenges. This project aims to develop a SAR-based flood segmentation model that can rapidly and accurately map floods globally, with high adaptability to different flood types and regions. By utilizing deep learning and a novel transfer-learning technique that combines the strengths of Optical/MSI and SAR data, the model seeks to bypass the challenges of manual labeling and improve mapping accuracy. Initial results show the model's effective generalization across various flood events, with superior performance indicated by an Intersection over Union (IoU) of 0.72, outperforming existing methods and demonstrating promising capabilities in precise flood mapping.

This article explores the potential of the Unreal Engine game engine as a tool for creating highly realistic 3D visualizations of GIS data, specifically in the context of significant historical areas. The research will focus on the computational techniques used in the 3D visualization of GIS data and the Unreal Engine software and its potential applications in geomatics. The article also presents several relevant scientific studies and projects that have successfully used game engines for similar purposes, explores the potential for using GIS data within a game engine, including plug-ins that provide additional functionality to GIS software, and the use of procedural modeling to create 3D objects through the CityEngine software plug-in. Finally, the research is applied to a dataset of the historical Vltava Valley covering an area of 1670 km2 to demonstrate the capabilities of Unreal Engine in creating realistic visualizations of large historical landscapes, and the resulting visualizations are presented. This practical application of the research provides valuable insights into the potential of the Unreal Engine as a powerful tool for creating highly realistic 3D visualizations of large historical areas with potential applications in the field of geomatics.

It is well-known that the canonical quantization of $\varphi^4_4$ fails. Here is how to fix it along with many other problems.

This review focuses on social play, a complex behavior that is often difficult to categorize. Although play has been typically associated with positive emotions, emerging research indicates that it relates to various emotional systems, ranging from attachment to conflict. Play oscillates between compe-tition and cooperation, and includes a spectrum in-between, thus quantitatively identifying and demonstrating the emotional nature of play remains challenging. We examined some examples from human and non-human animal studies and explored the emotional and neuro-hormonal systems involved in play. We considered ethological data possibly indicating the emotional states underlying play and we focused on the cooperative and competitive elements of play. We investigated the relationship between play and affiliative/aggressive behaviours, the communicative meaning of play signals (especially primate play faces), and the motor and possibly emotional contagion func-tion of rapid motor mimicry during play. Of all the literature on play, this review selects and com-bines the studies in an innovative way, to present the methods, tools, and evidence indicative of the emotional nature underlying play, which is much more complex than previously thought.

Protein 53 (P53) shows different functions either to support or to contrast malignant growth. P53 history tells that discoveries of molecular features have contributed to the emergence of P53 functions. Conversely, the shift of paradigms demonstrates that P53 functions coexist even if are in conflict with one another. Since 1979, five paradigms have been assembled then deconstructed by taking into account P53 functions of viral protein, oncogene, tumor suppressor gene, transcription factor, apoptotic factor. By summarizing the past five disassembled P53 paradigms, in this investigation the current paradigm involving P53 in the modulation of a composite bio-morphological entity namely, tumor immune microenvironment (TIME) is analyzed. The aims of this investigation were to provide an overview of solid and validated knowledge occurring in all P53 paradigms and to determine P53 abilities to modify TIME through manipulation of viruses. As a result, definitive P53 evidence concerns its genetic localization, amino acids composition, its isoforms and subcellular expression in organelles. P53 “mutome” assembles all mutations affecting this complex protein. Lastly, both categories of tumor promoting and non-oncogenic viruses related to P53 were scrutinized. In conclusion, paradigms teach that P53 is undruggable because its drugs inevitably target all P53 functions.

In recent years, transformer-based models have gained prominence in multivariate long-term time series forecasting (LTSF), demonstrating significant advancements despite facing challenges such as high computational demands, difficulty in capturing temporal dynamics, and managing long-term dependencies. The emergence of LTSF-Linear, with its straightforward linear architecture, has notably outperformed transformer-based counterparts, prompting a reevaluation of the transformer's utility in time series forecasting. In response, this paper presents an adaptation of a recent architecture termed extended LSTM (xLSTM) for LTSF. xLSTM incorporates exponential gating and a revised memory structure with higher capacity that has good potential for LTSF. Our adopted architecture for LTSF termed as xLSTMTime surpasses current approaches. We compare xLSTMTime's performance against various state-of-the-art models across multiple real-world datasets, demonstrating superior forecasting capabilities. Our findings suggest that refined recurrent architectures can offer competitive alternatives to transformer-based models in LTSF tasks, potentially redefining the landscape of time series forecasting.

Electric road systems (ERS) are a group of technologies that allow powering adequately-equipped road transport vehicles with electricity from the road infrastructure while in motorway traffic. They can be categorised in three technology groups: overhead catenary, ground conductive and ground inductive, depending on the used mode of power transfer. The supplied energy is used for propulsion and for charging the vehicle batteries to be used once the vehicle leaves the electrified road section. Also, another energy source, e.g. diesel, natural gas or hydrogen, can be used while away from ERS. This research investigates the potential impacts of implementing the different ERS technologies on the Rotterdam-Antwerp motorway corridor that links the two largest ports in Europe. The aim is to identify which of the routes between the ports is best suited for implementation of ERS, whether there are substantial differences in economic performance of the different ERS technologies, see what ERS vehicle traffic volumes are required and potentially available for successful implementation, what investment is required to build the system and whether the ERS operator can be profitable, and could transport operators operate their trucks on ERS profitability in this corridor setting. The research shows that that the route between Rotterdam and Antwerp that runs on motorway E19 is the best to be electrified from an economic standpoint. Our calculations show that the traffic on Rotterdam-Antwerp corridor is sufficient for economically justifying ERS infrastructure rollout and operation. For transport operators, that happen to have specific client base, e.g. where they usually serve clients from one of the ports along the electrified route, the construction of ERS on the route can prove to be very lucrative if they adopt the technology early.