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

Oilseed plants of the Brassicaceae plant family are cultivated for food, feed and industrial purposes on large-scale in Europe. This review gives an overview of current market-oriented applications of new genomic techniques (NGTs) in relevant Brassicaceae oilseed crops based on a literature survey. In this respect, changes in oil quality, yield, growth and resistance to biotic and abiotic stress are goals in oilseed rape (B. napus) and camelina (C. sativa). Environmental risk assessment scenarios are developed for specific NGT applications in Brassicaceae oilseed crops with either a changed oil composition or with fitness related traits. In case of a changed oil composition an increase or decrease of polyunsaturated fatty acids (PUFA) demonstrates risks for health and survival of pollinators. In case of fitness related traits other risks were identified, i. e. an increased risk of spread and persistence of NGT plants. Furthermore, there are indications for potential disturbance of interactions with the environment, involving signalling pathways and reaction to stress conditions. It is shown that for a comprehensive risk assessment the technological potential of NGTs, the plants’ biology and the scale of releases have to be considered in combination. Therefore, the release of NGT plants into the environment for agricultural purposes will require risk assessment and monitoring of single traits as well as of combinatorial and long-term cumulative effects. In addition, risk management should develop concepts and measures to control and potentially restrict the scale of releases. This is especially relevant for NGT Brassicaceae in Europe which is a centre of diversity of this plant family.

The aim of the current study has been to assess externalizing problems from a gender perspective in the juvenile population. This objective has been evaluated though the Child and Adolescent Assessment System questionnaire (SENA), to measure the dimension of externalized problems. A total of 128 Spanish students took part in the survey, comprising 58 (45.3%) male and 67 (52.3%) female, aged between 12 and 16 years old (M= 13,7; Sd= 1,14). The sample was taken from three High- Schools (state and private-subsidized) in Madrid, Spain. The research for this study was conducted through a descriptive, inferential, quantitative, ex post facto design. The data analysis was performed using SPSS 27.0. The result revealed an increase in externalized problems: Attention (M= 2.65, Sd=.88), Anger Control (M= 2.27, Sd=.85) and Hyperactivity- Impulsivity (M= 2.26, Sd=.68). In addition, female students score highly in Attention Problems (F= 4.124; Sig.: .004), Hyperactivity-Impulsivity (F: 6.18; Sig.: <.001), and Anger Control (F: 2.3; Sig.: .062). This study highlights the impact and increase on students externalized mental health problems currently. It is essential to have a protective environment and promote the development of emotional intelligence, encouraging the mental health in young people. This article is part of the findings of the research project “Therapeutic Innovation Classroom for the accompaniment of teenager with Serious Mental Disorder” (Reference: 2021V/EU-UNED/02).

This article presents a succinct overview of the progress, obstacles, and uses of semantic segmentation and instance segmentation within the forestry domain. The objective of this review is to conduct a critical analysis of the current literature pertaining to segmentation techniques and provide a methodical summary of their impact on forestry-related activities, including but not limited to tree species classification, forest inventory, and ecological monitoring such as retrieval of dominant tree species. Through the process of synthesizing pivotal discoveries from multiple studies, this comprehensive analysis provides valuable perspectives on the present status of research and highlights prospective areas for further exploration. The primary topics addressed encompass the approach employed for executing the examination, the fundamental discoveries associated with semantic segmentation and instance segmentation in the domain of forestry, and the ramifications of these discoveries for the discipline. The results indicate that the utilization of semantic segmentation methods has been efficacious in the field of forestry for the precise identification of tree species. Such methods also aid in tracking of deforested regions over the course of time by separating other land-use classes from forested regions. Additionally, the employment of instance segmentation techniques exhibits potential in the demarcation of individual trees. Instance segmentation offers promising results due to deep learning models based on forest point clouds. However, several challenges persist in the successful implementation of semantic segmentation methods such as the presence of occlusions, overlapping branches, and intricate structures hampers the accurate segmentation of trees. Additionally, instance segmentation approaches that utilize models are mostly trained by using laser scanning data based on forest types which are typically trained on specific laser scanning data and forest types that create limitations in generalization from high to low resolution point clouds. Due to this reason, the existing approaches often struggle with handling these complex structures, leading to the need for manual methods for extracting measurements from forest point clouds. The review culminates by underscoring the necessity for additional research to tackle current obstacles and augment the precision and relevance of segmentation methodologies in the field of forestry. In general, the present article provides a significant reference for scholars and professionals who are interested in utilizing segmentation techniques in the field of forestry.

In conventional Federated Learning (FL), clients work together to train a model managed by a central server, intending to speed up the learning process. However, this approach imposes significant computational and communication burdens on clients, particularly with complex models. Additionally, while FL strives to protect client privacy, the server's access to local and global models raises security concerns. To address these challenges, Split Learning (SL) separates the model into parts handled by the client and the server, though it suffers from inefficiencies due to sequential client participation. To overcome these issues, SplitFed Learning (SFL) was proposed, which combines the parallelism of FL with the model-splitting strategy of SL, enabling simultaneous training by multiple clients. Our main contribution is the theoretical analysis of SFL, which, for the first time, includes non-i.i.d. datasets, non-convex loss functions, and both full and partial client participation. We provide convergence proofs for a state-of-the-art SFL algorithm based on conventional convergence analysis assumptions for FL. Our results prove that we can recover the linear convergence rate of conventional FL for the SFL algorithm with the distinction that increasing the number of local steps or clients may not speed up the convergence in SFL.

Reticular and cluster materials often feature complex formulas, making a comprehensive overview challenging due to the need to consult various resources. While datasets have been collected for metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and zeolites, among others, there remains a gap in systematically organized information for polyoxometalates. This paper introduces a carefully curated dataset of 1984 polyoxometalate (POM) and related cluster metal oxide formula instances, currently connecting over 2,500 POM material instances. These POM instances incorporate 75 different chemical elements, with compositions ranging from binary to octonary element clusters. This dataset not only enhances accessibility to polyoxometalate data but also aims to facilitate further research and development in the study of these complex inorganic compounds.

This paper presents a two-stage ADC based on pseudo-differential operational transconductance amplifier (OTA), which is designed for the readout circuit of X-ray linear array sensor. This hybrid ADC employs an incremental sigma-delta ADC and a cyclic ADC, achieving a good trade-off between accuracy and conversion speed. The two stages share the same hardware to reduce power consumption and die area. A common-mood feedback module is used to suppress the influence of charge injection, and the effectiveness is demonstrated by detailed theoretical analysis. A test chip of 14-bit ADC is fabricated in 0.35μm CMOS technology. The measured root mean square (RMS) value of DNL is 0.254 LSB, and the maximum value of INL is -0.776/+1.56 LSB. The measured effective number of bits (ENOB) is 13.43 bits.

The aim of this study was to examine the oxidation of human serum albumin (HSA) caused by oxidative stress after an exhaustive exercise such as ultra-marathon race. Thus, blood samples from 12 adult runners who underwent a 103 km mountain ultra-marathon race were collected pre- and 24, 48 and 72 h post race. HSA was partially purified using affinity chromatography and then was subjected to Western blot analysis for disulfide dimers determination, indicating oxidation. The results were correlated with those from a previous study in which the same samples were analyzed using different oxidative stress markers and a good correlation with protein carbonyls (PC) at all time points was observed. Moreover there was a significant correlation with static oxidation-reduction potential (sORP) at 24 h, and a negative correlation with capacity oxidation-reduction potential (cORP) at 24 and 48 h. In addition, an individual analysis of albumin dimers exhibited great inter-individual differences. This inter-individual variability in the oxidation of HSA may suggest different interventions (e.g. through diet) in order to confront the effects on athletes’ organism after a strenuous exercise. In conclusion, this study supported the importance of the assessment of albumin dimers as a predictive marker for exercise-induced oxidative stress.

With increasing research focus on industrial anomaly detection, numerous methods have emerged in this domain. Notably, memory bank-based approaches, coupled with kNN distance metrics, have demonstrated remarkable performance in anomaly detection (AD) and anomaly segmentation (AS). However, upon examination of the Back to the Feature (BTF) method applied to the Mvtec-3D AD dataset, it was observed that while it exhibited exceptional segmentation performance, its detection performance was lacking. To address this discrepancy, this study base improves the implementation of BTF, especially the improvement of the anomaly score metric. It posits that different "clusters" necessitate distinct k values in kNN distance metrics. For simplify, this assumption is distilled into the proposition that AD and AS tasks impose differing requirements on the k value in kNN distance metrics. Consequently, the paper introduces the BTM method, which utilizes distinct distance metrics for AD and AS tasks. This innovative approach yields superior AD and AS performance (I-AUROC 93.0%, AURPO 96.9%, P-AUROC 99.5%), representing a substantial enhancement over the BTF method (I-AUROC 5.7% ↑, AURPO 0.5% ↑, P-AUROC 0.2% ↑).

This study aims to apply a Recommendation System with Content Based Filtering method with Jaccard Similarity and Cosine Similarity algorithms on the E-Learning Platform. Recommendation systems deal with how to provide personalized recommendations to users efficiently. The Content Based Filtering method with Jaccard Similarity and Cosine Similarity algorithms can be used to calculate the similarity value between E-Courses on the E-Learning Platform. Implementation for Recommendation System using Google Colaboratory with Python programming language. In the application of the Recommendation System dataset, Coursera Free Dataset consists of 975 instances. The recommendation results use the Jaccard Similarity algorithm with an average similarity value of 0.3 while the value of Cosine Similarity with the average similarity value is 0.6 where the similarity value of Cosine Similarity is higher. Based on the results of the Mean Absolute Error in the low recommendation system, the average MAE value for all iterations of Jaccard Similarity algorithm is 0.013 and for the Cosine Similarity algorithm the average MAE value for all iterations is 0.014. This shows that the Recommendation System with Jaccard Similarity and Cosine Similarity algorithms can be used on the E-Learning Platform to provide efficiency solutions for personalized recommendations.

Bluetongue is not a contagious disease. It spreads very quickly and uncontrollably, considering that it is a vec-tor-borne disease transmitted by the genus Culicoides. Climate change has led to the fact that vectors and dis-eases have spread to the top of Northern Europe, causing great economic losses in livestock production. Even greater problem is controlling the disease, because numerous species of domestic and wild ruminants are sus-ceptible to bluetongue. The most effective tool against bluetongue disease is vaccination. There is no ideal vaccine against bluetongue virus (BTV) due to the serotype diversity of the strains. Thus, there is need for a vaccine for at least for 24 clinically significant serotypes. Sometimes it is difficult to obtain the desired amount of vaccine against a defined serotype on the market, and this led to the need for a new vaccine against blue-tongue disease. We tested an experimental inactivated novel vaccine against BTV4. Master seed BTV4 has been examined and characterized by sequencing. The novel BTV4 vaccine induce the onset of immunity at least at day 21 after the application of the first dose in more than 80% of vaccinated individuals, while specific antibodies against BTV are detected by the ELISA test for more than a year. Along with our laboratory results, the preliminary results of safety and efficacy trials are also presented. The side effects of novel vaccine are within the limits of permissible local reactions without generalized changes of health status, while the serology and challenge test leads to the conclusion that novel vaccine protects high percentage of vaccinated individuals against BTV4 or causes a significant reduction in the intensity and duration of the clinical signs in the vac-cinated sheep. Based on the trial results, new vaccine has given encouraging results in quality, safety and pre-liminary efficacy tests. Thus, we believe that a new vaccine against BTV is on the horizon. Keywords: bluetongue virus, inactivated vaccine, candidate, quality of vaccine, safety, efficacy We confirm that neither the manuscript nor any parts of its content are currently un-der consideration for publication with or published in another journal. All authors have approved the manuscript and agree with its submission to Vaccines.

The correct selection of the value of p is a complex and iterative procedure that requires experience in the interpretation of the obtained interpolated maps. Inverse Distance Weighting is a method applied to the porosities of the K and L hydrocarbon reservoirs, discovered in the Neogene (Lower Pontian) subsurface sandstones in the northern Croatia (Pannonian Basin System). They represent small and large data samples. Also a standard statistical analysis of the data was made, followed by the qualitative-quantitative analysis of the maps, based on the selection of different values of the power distance exponent (p-value) for the K and L reservoirs maps. According to the qualitative analysis, for a small data set, p-value could be set on 1 or 2 giving the most optimal result, while for a large data set, a p value of 3 and 4 could be applied. By quantitative analysis, for the case of small data p=2 is recommended, resulting in a root mean square error value of 0.03458, a mean absolute error of 0.02013 and a median absolute deviation of 0.00546. Oppositely, a p-value of 3 and 4 is selected as appropriate for a large data set, with root mean square errors of 0.02435 and 0.02437, mean square errors of 0.01582 and 0.01509 and median absolute deviations 0.00896 and 0.00444. Eventually for a small data set, it is recommended to use a p-value of 2, and for a large data set, a p-value of 3 or 4.

Linear polyamides, known as nylons, are a class of synthetic polymers with a wide range of applications due to their outstanding properties, such as chemical and thermal resistance or mechanical strength. These polymers have been used in various fields: from common and domestic applications, such as socks and fishing nets, to industrial gears or water purification membranes. By their durability, flexibility and wear resistance nylons have been started to be used in addictive manufacturing technology as a good material choice to get sophisticated devices with precise and complex geometric shapes. Furthermore, the emergence of triboelectric nanogenerators and the development of biomaterials have highlighted the versatility and utility of these materials. In order to enhance the triboelectric performance and the range of applications, nylons show a potential use as tribo-positive materials. Because of the easy control of their shape they can be subsequently integrated into nanogenerators. The use of nylons has also extended into the field of biomaterials, where their biocompatibility, mechanical strength and versatility have paved the way for groundbreaking advances in medical devices as dental implants, catheters and non-absorbable surgical sutures. By means 3D bioprinting nylons have been used to develop scaffolds, joint implants and drug carriers with tailored properties for various biomedical applications. The present paper aims to collect these recently specific applications of nylons by reviewing the literature produced in the last decades, with a special focus on the newer technologies in the field of energy harvesting and biomedicine.

Optimization is critical in various fields like smart vehicles, and transportation. Federated Learning (FL) has emerged as an effective approach in the coordination of autonomous vehicles, but traditional methods such as FedAvg can create performance disparities across clients. This paper addresses this fairness issue through the $q$-Fair Federated Learning ($q$-FFL) framework, adjusting model performance across clients using a tunable fairness parameter. We propose a modified FedAvg algorithm for $q$-FFL that maintains comparable convergence rates, ensuring more balanced client outcomes. Additionally, we explore incentive mechanisms in FL using a Stackelberg game model, incorporating a fairness coefficient to encourage equitable participation. Building on prior works, we redefine client utility functions to address communication and computation costs, ensuring fair resource allocation. The proposed framework achieves both global and local fairness, maintaining a unique Nash equilibrium in the modified game setting.

Bovine Respiratory Disease Complex, a complex respiratory ailment in cattle, results from a combination of viral and bacterial factors, compounded by environmental stressors such as overcrowding, transportation, and adverse weather conditions. Its impact extends beyond mere health concerns, posing significant economic threats to the cattle industry. This study presents an extensive investigation into viral pathogens associated with BRDC in Serbian cattle, utilizing serum samples and nasal swabs. A cross-sectional study was conducted in 2024 across 65 randomly selected dairy farms in Serbia, excluding vaccinated farms. Farms were categorized by livestock count: small (≤50 animals), medium-sized (51-200 animals), and large (&gt;200 animals). Serum samples from adult cattle older than 24 months were tested for antibodies against BVDV, BHV-1, BRSV, and BPIV3. Nasal swab samples from animals with respiratory signs were tested using PCR for viral genome detection. Results showed seropositivity for all four viruses across all farms, with BPIV3 exhibiting universal seropositivity. Medium-sized and large farms demonstrated higher seropositivity for BRSV and BHV-1 compared to small farms (p&lt;.05). True seroprevalence estimates at the animal level were 84.29% for BRSV, 54.08% for BVDV, 90.61% for BHV-1, and 84.59% for BPIV3. PCR analysis of nasal swabs revealed positive detections for BRSV (20%), BHV-1 (1.7%), BVDV (8%), and BPIV3 (10.9%). Influenza D virus was not found in any of samples. This study provides critical insights into the prevalence and circulation of viral pathogens associated with BRDC in Serbian cattle, emphasizing the importance of surveillance and control measures to mitigate the impact of respiratory diseases in cattle populations.

Vaccination against HPV significantly reduced the incidence of HPV-related le-sions worldwide. Considering the increasingly younger age of patients in the gynaecological office and the resulting earlier sexual initiation and potentially contact with the HPV virus, doctors need to have tools to verify the diagnosis. Nowadays, women plan motherhood later, so it is necessary to consider whether sexual treatment in the form of, among others, LEEP-conization may increase the risk of premature birth or difficulty dilating the cervix during labour.For this reason, to avoid overtreatment of LSIL changes, methylation testing may be considered. In patients with histopathologically confirmed HSIL during biopsy and ultimately a lower diagnosis - i.e., LSIL or no signs of atypia - methylation turned out to be a useful tool. We performed Pap-smear, HPV genotyping, punch biopsy, LEEP-conization (if needed) and methylation test in 108 women admitted to the District Public Hospital in Poland. Women with a negative methylation test result were significantly more likely to be ultimately diagnosed with LSIL (p=0.013). This means that in 85.7% of patients with HSIL, major cervical surgery could have been avoided if methylation was negative. Methylation testing, as well as dual-staining and diagnostics detecting mRNA transcripts of highly oncogenic types of HPV, might be used in future in the diagnosis of pre-cancerous conditions, mainly of the cervix, and in HPV-dependent cervical cancer screening. The methylation test may also be used in the diagnosis and identification of lesions within the cervical canal, including those located deep within the frontal crypts, not visible even during a professional colposcopic evaluation of the cervix, as well as within the crypts of the palatine tonsils - in diagnostics of head and neck diseases.

Inter-industry has various capital structures to take advantage of growth opportunities due to agency differences and information asymmetric problems. This research aims to analyze: (1) the differences in leverage between industries; (2) the impact of shareholders-debtholders conflict and information asymmetric on growth opportunities. The findings show that there are differences in leverage between industries. However, they are relatively stable and not excessive for assets to prevent conflicts between managers and shareholders and debtholders. Equity is also not used to utilize growth because it is used to reduce the discretionary power (dilution) of shareholders. As a result, the use of opportunities and growth is limited.

This study analyzes how Perceived Credibility of a 360-degree feedback and Evaluation of Outcome impact on ratees’ attitudes towards behavioral change. The study adopts a qualitative-quantitative mixed method approach. Reasoned Action Theory was selected as the theoretical framework, and ten 360-degree assessment ratees were interviewed on their perceived credibility, evaluation of outcome and attitude toward behavioral change in the 360-degree assessment practices. The qualitative inputs were analyzed under the methodology of grounded theory. Additional 159 respondents participated in the quantitative research phase of the research to validate the findings in the qualitative research phase. It was validated that Perceived Credibility, Evaluation of Outcome has impact on Attitude Toward Behavioral Change in 360-degree assessment.

We demonstrate that the black hole evaporation can be modelled as a process where one symmetry of the system is spontaneously broken continuously. We then identify three free-parameters of the system. The sign of one of the free-parameters, governs whether the particles emitted by the black-hole are fermions or bosons. The present model explains why the Black Hole evaporation process is so universal. Interestingly, this universality emerges naturally inside certain modifications of gravity.

Non-woven jute (NWJ) produced from the carpet industry waste was oxidized by H2O2 or alkali treated by NaOH and compared with water-washed samples. Changes in the structure of the NWJ, tracked by XRD, showing that both chemical treatments disrupt hydrogen bond networks between cellulose Iβ chains of the NWЈ fibers. Thereafter, nano carbon nitride (nCN) was impregnated, using layer-by-layer technique, onto water washed jute sample (nCN-Jw), NaOH treated sample (nCN-Ja) and H2O2 treated sample (nCN-Jo). Analysis of the FTIR spectra of the impregnated samples, revealed that nCN anchors to the water washed NWЈ surface through hemicellulose and secondary hydroxyl groups of the cellulose. In the case of chemically treated samples nCN is preferentially bonded to the hydroxymethyl groups of cellulose. The stability and reusability of nCN-J samples were assessed by tracking the photocatalytic degradation of Acid Orange 7 (AO7) dye under simulated solar light irradiation. Results from up to ten consecutive photocatalytic cycles demonstrated varying degrees of effectiveness across different samples. nCN-Jo and nCN-Ja samples exhibited declining effectiveness over cycles, attributed to bond instability between CN and jute. In contrast, the nCN-Jw sample consistently maintained high degradation rates over ten cycles, with a dye removal percentage constantly above 90%.

The increase in the frequency of droughts in Africa has increased the interest in crops with drought escape mechanisms like grain Amaranth (Amaranthus hypochondriacus). However, the white-seeded grain Amaranth is easily cross-pollinated by its black seeded wild relatives such as smooth pigweed (Amaranthus hybridus). A study to investigate a minimum isolation distance enough to prevent seed contamination study was carried out at three sites namely Gweru and Bindura (2020/2021 rainy season) and Shamva (2021/2022 rainy season). Smooth pigweed was planted at distances of 3 to 150 m from pure grain Amaranth. Close monitoring of phenology and measurement of meteorological conditions such as temperature, wind speed and direction were done during the anthesis period. Cross-pollination in grain Amaranth was influenced by distance from the pollen source. A maximum of 12.9 % contamination rate was measured within the 10 m radius from the pollen donor plot and a minimum outcrossing rate of 0.45 % was recorded at the widest distance of 150 m. It can be concluded that complete removal of wild relatives of grain Amaranth is important within the field at as far as 150 m from the crop.

We propose two methods for obtaining estimations on the minimum distance and covering radius of orthogonal arrays. Both methods are based on knowledge about the (feasible) sets of distance distributions of orthogonal arrays with given length, cardinality, factors and strength. New bounds are presented either in analytic form and as products of an ongoing project for computation and investigation of the possible distance distributions of orthogonal arrays with parameters in doable ranges.

Modular steel buildings (MSBs) interlocking inter-module connections (IMCs) have piqued researchers’ interest. However, the existing literature has not yet studied the optimisation of its plate thicknesses. This paper, therefore, focuses on optimising interlocking (IMCs) plate thickness in (MSBs), leveraging previous experimental and numerical simulation methodologies. Various numerical models for four MSBs interlocking (IMCs) with plate thicknesses (4 mm, 6 mm, 10 mm, and 12 mm) were developed under compression loading conditions. The study’s novelty lay in its comprehensive parametric analysis, which evaluated the slip phenomenon in (MSBs) connections and introduced a slip prediction model validated against empirical data. Further innovative machine learning approach was utilised to predict slip values based on applied force through a random forest regression model trained using the 'Treebagger' function. Sensitivity analysis and comparisons with alternative methods were utilised to underscore the reliability and applicability of the findings. The results showed that 11.03 mm was the optimal plate thickness for interlocking (IMCs) in modular steel buildings, with up to 8.08% reduced material costs. Increasing plate thickness boosts its deformation resistance by up to 50.75%. ‘TreeBagger’ random forest for anomaly detection and machine learning improves slip prediction up to 7% at higher force levels.

Smartwatches are one of the most relevant fitness trends of the past two decades, and they collect increasing amounts of health and movement data. The accuracy of these data may be questionable and requires further investigation. Therefore, the aim of the present study is to validate smartwatches for use in triathlon training. Ten different smartwatches were tested for accuracy in measuring heart rate, distance (via global navigation satellite systems, GNSS), swim stroke rates and the number of swim laps in a 50 m Olympic-size pool. The optical heart rate measurement function of each smartwatch was compared to that of a chest strap. Thirty participants (15 females, 15 males) ran five 3-minute intervals on a motorised treadmill to evaluate the accuracy of the heart rate measurements. Moreover, for each smartwatch, running und cycling distance tracking was tested over six runs of 4,000 m on a 400 m tartan stadium track, six hilly outdoor runs over 3.4 km, and four repetitions of a 36.8 km road bike course, respectively. Three swimming protocols ranging from 200 m to 400 m were performed in triplicate in a 50 m Olympic-size pool, evaluating tracked distance and detected number of strokes. The mean absolute percentage errors (MAPEs) for the average heart rate measurements varied between 3.1% and 8.3%, with the coefficient of determination ranging from 0.22 to 0.79. MAPE results ranged from 0.8% to 12.1% for the 4,000 m run on the 400 m track, from 0.2% to 7.5% for the 3.4 km outdoor run, and from 0.0% to 4.2% for the 36.8 km bike ride. For the swimming tests, in contrast, the deviations from the true distance varied greatly, starting at a 0.0% MAPE for the 400 m freestyle and reaching 91.7% for the 200 m medley with style changes every 25 m. In summary, for some of the smartwatches, the measurement results deviated substantially from the true values. Measurements taken while road cycling over longer distances with only a few curves were in relative terms more accurate than for outdoor runs and even more accurate than for the 400 m track. In the swimming exercises, the accuracy of the measured distances was severely deteriorated by the medley changes among the majority of the smartwatches. Altogether, the results of this study should help in assessing the accuracy and thus suitability of smartwatches for general triathlon training.

Physical fitness is a fundamental indicator of health, leading to increased interest in fitness activ-ities under professional guidance. Fitness instructors play an essential role, requiring both tech-nical and interpersonal skills. Their image significantly influences participants perceptions and satisfaction. The objective of this study is to conduct a comparative analysis between Romania and Portugal regarding the quality of services offered within group fitness classes, focusing on instructors. It involved 133 group fitness instructors and 210 participants from Romania and Portugal. An adaptation of the Szumilewicz et al. (2008) questionnaire was used to assess the importance of the different attributes of fitness instructor image. Statistical analyses included de-scriptive statistics, t-tests, and effect size to compare perceptions between countries and groups. Romanian instructors and participants generally attributed higher importance to the fitness in-structor’s image compared to their Portuguese counterparts. Significant differences were found in attributes like physical fitness, technical execution and communication. Instructors tented to overestimate the importance of their image compared to participants. The fitness instructor image is crucial in determining participant satisfaction and perceptions of service quality. This study highlights the need for a balanced approach in instructor training, emphasizing both technical proficiency and interpersonal skills tailored to cultural contexts.

The carbohydrate antigen Sda is expressed on cells and secretions of the vast majority of Caucasians. The epitope is formed by a terminal GalNAc residue β4-linked to a α3-sialylated galactose. Different carbohydrate chains N- or O-linked to glycoproteins can be terminated by this epitope. The final step of Sda biosynthesis is catalyzed by the GalNAc transferase B4GALNT2. In this review, we will discuss the multifaceted aspects of B4GALNT2/Sda biology. In particular, we will see its implications in fertility and pregnancy, in susceptibility to infectious diseases, in chronic kidney diseases and in Duchene muscular dystrophy. In particular, we will show its regulation and prognostic implications in cancer.

We propose a quantum version of the well known minimum distance classification model called "Nearest Mean Classifier" (NMC). In this regard, we presented our first results in two previous works. In [34] a quantum counterpart of the NMC for two-dimensional problems was introduced, named "Quantum Nearest Mean Classifier" (QNMC), together with a possible generalization to arbitrary dimensions. In [33] we studied the n-dimensional problem into detail and we showed a new encoding for arbitrary n-feature vectors into density operators. In the present paper, another promising encoding of n-dimensional patterns into density operators is considered, suggested by recent debates on quantum machine learning. Further, we observe a significant property concerning the non-invariance by feature rescaling of our quantum classifier. This fact, which represents a meaningful difference between the NMC and the respective quantum version, allows to introduce a free parameter whose variation provides, in some cases, better classification results for the QNMC. The experimental section is devoted to: i) compare the NMC and QNMC performance on different datasets; ii) study the effects of the non-invariance under uniform rescaling for the QNMC.

The sustainability of ecotourism is the backbone of tourism development of a country. Ecotourism can contribute to both conservation and development in which involves dynamic relationship between tourism, biodiversity and communities, facilitate by great management. The purpose of this study is to analyze the dimensions of the socio-economic and environmental impacts of ecotourism in communities surrounding the Tasik Kenyir in Terengganu, Malaysia. This study provides a framework for the development and evaluation of ecotourism. The framework will determine if the relationships between indicators are positively correlated which will result in positive contribution to the other by using System Dynamic. Socio-cultural and economic data will be collected through interviews and group discussions in selected communities in Tasik Kenyir. Data on wildlife will be extracted from secondary data from Kenyir Research Institute, Universiti Malaysia Terengganu. The data analysis will explore the socio-cultural and economic differences between and within different communities due to ecotourism development, the contribution of ecotourism to conservation activities, local support for conservation and ecotourism as well as the influence of tourist activities on the distribution of wildlife species in Tasik Kenyir. This study aims to contribute toward understanding the natural resource community-ecotourism inter-relationship and help to bridge the knowledge gap that hinders biodiversity conservation initiatives. The findings will be used as a base for further development of ecotourism and will recommend alternative management options where necessary for the study area.

In response to the escalating climate-related catastrophes of recent decades and their significant impacts on global transportation systems, this study explores the concept of resilience bonds as a novel financial tool for enhancing supply chain sustainability. The research applies a case study from China to demonstrate the application of resilience bonds in funding infrastructure projects that aim to enhance the sustainability and resilience of supply chain networks. The study examines the market mechanisms and valuation models of resilience bonds, assessing their potential to mitigate risks associated with transportation failures and to reduce economic losses from unexpected disruptions. By applying empirical data, the research quantitatively evaluates the impact of resilience bonds on the sustainability and reliability of transportation systems. The findings show that resilience bonds can significantly contribute as a sustainable solution to enhance supply chain resilience and sustainability, offering insightful alternatives for financing resilient infrastructure projects. This study not only supports policymakers, investors, and business leaders in understanding innovative funding mechanisms but also makes a significant contribution to resilience financing for sustainable transportation within supply chains, especially in the context of global disruptions.

The ascidian fauna of the Bahamas remains grossly understudied. Here, we examined specimens of the genus Clavelina collected from four Bahamian islands using morphological observations and genetic barcoding. Only 3 species of Clavelina have been previously reported in the Caribbean: C. picta, C. oblonga, and C. puertosecensis. Here, we report C. picta (Verrill, 1900) and three new species in the Bahamas: C. rochae; C. pawliki; and C. erwinorum. C. picta and C. pawliki were found in the northernmost island surveyed, while C. rochae and C. erwinorum were particularly prevalent on the southeastern Bahamian islands. A complete review of genetic barcoding data and morphological characters of accepted Clavelina species was performed. The unique combination of in vivo coloration, morphological characteristics, haplotypes, and species distribution all supported the establishment of three new species, significantly adding to the diversity of the genus Clavelina in the Caribbean.

Despite all scientific efforts and many protracted and expensive clinical trials, no new drug has been approved by FDA for treatment of Alzheimer disease (AD) since 2003. Indeed, more than 200 investigational programs have failed or have been abandoned in the last decade. The most probable explanations for failures of disease-modifying treatments (DMTs) for AD may include late initiation of treatments during the course of AD development, inappropriate drug dosages, erroneous selection of treatment targets, and mainly an inadequate understanding of the complex pathophysiology of AD, which may necessitate combination treatments rather than monotherapy. Clinical trials’ methodological issues have also been criticized. Current drug-development research for AD is aimed to overcome these drawbacks. Preclinical and prodromal AD populations, as well as traditionally investigated populations representing all the clinical stages of AD, are included in recent trials. Systematic use of biomarkers in staging preclinical and prodromal AD and of a single primary outcome in trials of prodromal AD are regularly integrated. The application of amyloid, tau, and neurodegeneration biomarkers, including new biomarkers—such as Tau positron emission tomography, neurofilament light chain (blood and CSF biomarker of axonal degeneration) and neurogranin (CSF biomarker of synaptic functioning)—to clinical trials allows more precise staging of AD. Additionally, use of the Bayesian statistics, modifiable clinical trial designs, and clinical trial simulators enrich the trial methodology. Besides, combination therapy regimens are currently assessed in clinical trials. The abovementioned diagnostic and statistical advances, which have been recently integrated in clinical trials, are consequential to the recent failures of studies of disease-modifying treatments. Their experiential rather than theoretical origins may better equip potentially successful drug-development strategies.

The paper provides a methodological framework based on Multiple Criteria Decision Aid methods for determining relative regional vulnerabilities associated with forest fires that occurred during a given period. Different notions of ex-post vulnerability are defined based on the forest area burned and the frequency of forest fires, and their relation to the regions’ area and several socioeconomic characteristics. The methodology is applied to Greece and its administrative regions for the period 2000–2022. Linking the forest fire events with summer and spring temperatures and precipitation and using econometric count data analysis, the paper explores the climatic drivers of forest fire occurrence and provides projections of the expected change in the frequency of fires of specific size classes conditional on the evolution of mean seasonal regional temperature and precipitation according to IPCC scenarios. The results regarding relative regional vulnerabilities and expected forest fires could be useful in designing policies for preserving forests as natural capital and promoting sustainability.

The Science-Sustainability poses an interdisciplinary paradox. On the one hand, the science for sustainability has increased in OECD economies in and in China as well as in the US in particular; on the other hand; the sustainability situation has worsened (Co2 emission has risen). On the face value, the adverse correlation shows a paradox. However, without explicating the science-sustainability relationship, it leads to a premature conclusion. In this study, we have drawn on three concrete questions for concrete answers. First, whether and how interdisciplinary sciences—energy science and environmental science—contribute to the sustainability. Second, whether and how the Sino-US inter-institutional analysis varies in the science-sustainability paradox. The empirical analysis from a panel data in the interdisciplinary and inter-institutional context show mixed patterns in three ways. First, the increase in the environmental science shows an improvement in the sustainability; the energy science shows a decline in the sustainability. Second, the Chinese environmental science has a comparative advantage to American environment science for the sustainability development, and the Chinese energy science has a comparative disadvantage to the US in the sustainability development. Third, the environmental science mediates the energy science in the science-sustainability relationships. Standing alone, the increase in the energy science harms sustainability; mediated by environmental science, it benefits sustainability. The study explains the adverse role of energy science in Jevons Paradox. The study also offers some policy paths for further research how capitalisms differently innovate, form strategies, and implement the practice.

At the beginning of 2020, with the onset of the pandemic, the traditional learning environment for learners drastically changed globally. Since then, most students/teachers have started and practiced distance and virtual learning/teaching. Thus, a technological breakthrough in virtual learning has followed. In connection with this, many countries worldwide have commenced allocating additional financing and funds for educational institutions' technological improvement and development. The long-term stay in distance learning has revealed and highlighted new problems students face: their knowledge level has decreased, they lack socialization skills, and they face psychological and physical health problems. Due to this negative impact on students, a need to research and evaluate how much the EU countries allocated to solve the distance learning-caused problems and what programs or models they prepared has emerged and encouraged further studies. The research has found that many countries increased their allocations very minimally, e.g., 0.0.1%, but some increased their available budgets to 32%. Notably, most countries did not separate distance learning exit funding from distance learning preparation funding. Based on the problems the countries saw, only a few states identified withdrawal from distance learning as a problem. Considering this, we set ourselves the goal to evaluate exit models from distance learning and allocated funding amounts. The following objectives were planned to achieve the goal: · to evaluate the global practice of exit from distance learning; · to determine the scope of funding for pandemic management; · to evaluate the amounts of funding allocated to manage pandemic-caused consequences and the GDP ratio. Research methods: mathematical-statistical analysis, empirical analysis, and analysis of scientific literature.

Bonchev and Trinajstic defined two distance based entropy measures to measure the molecular branching of molecular graphs in 1977 [Information theory, distance matrix, and molecular branching, J. Chem. Phys., 38 (1977), 4517–4533]. In this paper we use these entropy measures which are based on distance matrices of graphs. The first one is based on distribution of distances in distance matrix and the second one is based on distribution of distances in upper triangular submatrix. We obtain the two entropy measures of paths, stars, complete graphs, cycles and complete bipartite graphs. Finally we obtain the minimal trees with respect to these entropy measures with fixed diameter.

Crigler-Najjar Syndrome (CNS) is a rare genetic disorder caused by mutations in the UGT1A1 gene, leading to impaired bilirubin conjugation and severe unconjugated hyperbilirubinemia. CNS presents in two forms: CNS type 1 (CNS1), the more severe form with a complete absence of UGT1A1 activity, and CNS type 2 (CNS2), with partial enzyme activity. CNS1 requires aggressive management, including phototherapy and plasmapheresis, but liver transplantation (LT) remains the only definitive cure. The timing of LT is critical, as it must be performed before the onset of irreversible brain damage (kernicterus), making early intervention essential. However, LT poses risks such as graft rejection and lifelong immunosuppression. CNS2 is milder, with patients re-sponding well to phenobarbital and having a lower risk of kernicterus. Recent advancements in gene therapy and autologous hepatocyte transplantation offer promising alternatives to LT. Gene therapy using adeno-associated virus (AAV) vectors has shown potential in preclinical studies, though challenges remain in pediatric applications due to liver growth and pre-existing immunity. Autologous hepatocyte transplantation avoids the risks of rejection but requires further research. These emerging therapies provide hope for more effective and less invasive treatment options, aiming to improve the quality of life for CNS patients and reduce reliance on lifelong interven-tions.

Rationale: The 14-3-3 protein family is known to interact with many proteins in non-cardiac cell types to regulate multiple signaling pathways, particularly those relating to energy and protein homeostasis; and the 14-3-3 network is a therapeutic target of critical metabolic and proteostatic signaling in cancer and neurological diseases. Although the heart is critically sensitive to nutrient and energy alterations, and multiple signaling pathways coordinate to maintain the cardiac cell homeostasis, neither the structure of cardiac 14-3-3 protein interactome, nor potential functional roles of 14-3-3 protein-protein interactions (PPIs) in heart has been explored. Objective: To establish the comprehensive landscape and characterize the functional role of cardiac 14-3-3 PPIs. Methods and Results: We evaluated both RNA expression and protein abundance of 14-3-3 isoforms in mouse heart, followed by co-immunoprecipitation of 14-3-3 proteins and mass spectrometry in left ventricle. We identified 52 proteins comprising the cardiac 14-3-3 interactome. Multiple bioinformatic analyses indicated that more than half of the proteins bound to 14-3-3 are related to mitochondria; and the deduced functions of the mitochondrial 14-3-3 network are to regulate cardiac ATP production via interactions with mitochondrial inner membrane proteins, especially those in mitochondrial complex I. Binding to ribosomal proteins, 14-3-3 proteins likely coordinate protein synthesis and protein quality control. Localizations of 14-3-3 proteins to mitochondria and ribosome were validated via immunofluorescence assays. The deduced function of cardiac 14-3-3 PPIs is to regulate cardiac metabolic homeostasis and proteostasis. Conclusions: Thus, the cardiac 14-3-3 interactome may be a potential therapeutic target in cardiovascular metabolic and proteostatic disease states, as it already is in cancer therapy.

A numerical application of linear-molecule symmetry properties, described by the D_∞h point group, is formulated in terms of lower-order symmetry groups D_nh with finite n. Character tables and irreducible representation transformation matrices are presented for D_nh groups with arbitrary n-values. These groups are subsequently used in the construction of symmetry-adapted ro-vibrational basis functions for solving the Schrödinger equations of linear molecules as part of the variational nuclear motion program TROVE. The TROVE symmetrisation procedure is based on a set of "reduced" vibrational eigenvalue problems with simplified Hamiltonians. The solutions of these eigenvalue problems have now been extended to include the classification of basis-set functions using ℓ, the eigenvalue (in units of ℏ) of the vibrational angular momentum operator ${\widehat{L}}_z$ . This facilitates the symmetry adaptation of the basis set functions in terms of the irreducible representations of D_nh. ¹²C₂H₂ is used as an example of a linear molecule of D_∞h point group symmetry to illustrate the symmetrisation procedure.

The aim of the study was to assess the impact of two lengths of Nordic walking (NW) training interventions combined with time restricted eating (TRE) on improving body composition parameters, lipid profile, and levels of selected adipokines in women with disrupted body mass. Overweight and obese women (n=55, age: 21-85) were recruited. Four groups were selected: 6 weeks (SG6, n=13) and 12 weeks intervention (SG12, n=13); and two control groups: CON6 (n=13); and CON12 (n=13). The training sessions took place 3 times a week (60 minutes each) and were conducted outdoor under the supervision of professional coach. The training intensity was determined individually. The extended NW program combined with TRE induced a significant weight reduction in SG12 by 1.96 kg (p=0.010) and fat tissue by 1.64 kg (p=0.05). The proposed interventions did not affect LBM, TBW [kg], VFA and lipid profile. The LDL/HDL ratio changed with small size effect. Leptin concentration differed between groups (p=0.006), but not over time. For resistin, the differentiating factor was time (p=0.019, with lower results observed after the intervention). The change in leptin concentration was negatively correlated with its baseline concentration. Extended to 12 weeks, this intervention allows for an improvement in body composition. Neither 6 nor 12 weeks of training and fasting affected the lipoprotein profile.

With the aim to produce new heterocycle molecules, the previously reported 2-(aminomethyl)-2-(4-chlorophenyl)-2,3-dihydroquinazolin-4(1H)-one was converted efficiently by reacting with N,N’-dithiocarbonyldiimidazole (DTCI), to produce the substituted imidazolidine-2-thione moiety inserted in a three fused ring scaffold of the title compound. The molecular composition was confirmed by a high-resolution MS experiment, and its structure was elucidated by 1H, 13CNMR, and IR analyses. The thioacetamide form of the product was supported by density functional theory (DFT)-NMR analysis where 13C chemical shifts of thioacetamide form and of its iminothiol tautomer were calculated in chloroform at the BP86 /Jgauss-TZP2 level of theory. The very good linear correlation between 13C chemical shifts by experiment and by calculation for the NHC=S form confirmed the structure.

In ocean engineering, accurate and efficient numerical simulations are crucial. Leveraging axisymmetry vastly enhances the efficiency of a simulation, reducing a three-dimensional scenario to a two-dimensional simulation. Exploiting axisymmetry enables a significant reduction in computational demand while maintaining simulation fidelity. In this paper, axisymmetry is leveraged to perform a large ensemble of simulations, in order to evaluate and maximise the accuracy and efficiency of a Computational Fluid Dynamics (CFD) simulation. In particular, the free decay motion and wave radiation from a heaving semi-submerged sphere are simulated, using the Reynolds Averaged Navier Stokes (RANS) solver, interFOAM, in the opensource finite volume CFD software OpenFOAM. Validated against highly accurate experimental data, extensive parametric studies are conducted, previously limited by computational constraints, which facilitate the refinement of simulation setups. More than 50 iterations of the same heaving sphere simulation are performed, informing efficient trade-offs between computational cost and accuracy across various simulation parameters and mesh configurations. Ultimately, by employing axisymmetry, this research contributes to the development of more accurate and efficient numerical modeling in ocean engineering.

Purpose: To determine whether 24-hour IOP monitoring can be a predictor for glaucoma progression and to analyze the inter-eye relationship of IOP, perfusion and progression parameters. Methods: We extracted data from manually drawn IOP curves with HIOP-Reader, a software suite we developed. The relationship between measured IOPs and mean ocular perfusion pressures (MOPP) to retinal nerve fiber layer (RNFL) thickness was analyzed. We determined the ROC curves for peak IOP (Tmax), average IOP (Tavg), IOP variation (IOPvar) and historical IOP cut-off levels to detect glaucoma progression (rate of RNFL loss). Bivariate analysis was conducted to check for various inter-eye relationships. Results: 217 eyes were included. The average IOP was 14.8±3.5 mmHg, with a 24-hour variation of 5.2±2.9 mmHg. 52% of eyes with RNFL data showed disease progression. There was no significant difference in Tmax, Tavg and IOPvar between progressors and non-progressors (all p>0.05). Except for Tavg and the temporal RNFL, there was no correlation between disease progression in any quadrant, Tmax, Tavg and IOPvar. 24-hour and outpatient IOP variables had poor sensitivities and specificities in detecting disease progression. The correlation of inter-eye parameters was moderate; correlation with disease progression was weak. Conclusion: In line with our previous study, IOP data obtained during a single visit (outpatient or inpatient monitoring) make for a poor diagnostic tool, no matter the method deployed. Glaucoma progression and perfusion pressure in left and right eyes correlated weakly to moderately with each other.

Finding an optimal combination of features and classifier is still an open problem in the development of automatic heartbeat classification systems, especially when applications that involve resource-constrained devices are considered. In this paper, a novel study of the selection of informative features and the use of a random forest classifier while following the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) and an inter-patient division of datasets is presented. Features were selected using a filter method based on the mutual information ranking criterion on the training set. Results showed that normalized R-R intervals and features relative to the width of the QRS complex are the most discriminative among those considered. The best results achieved on the MIT-BIH Arrhythmia Database were an overall accuracy of 96.14% and F1-scores of 97.97%, 73.06%, and 90.85% in the classification of normal beats, supraventricular ectopic beats, and ventricular ectopic beats respectively. In comparison with other state of the art approaches tested under similar constraints, this work represents one of the highest performances reported to date while relying on a very small feature vector.

Religious harmony performs a key role in preserving political and worldwide stability. Internationally it's far crucial to stay in a non-violent and harmonious environment, mainly in a society composed of believers of various races and religions. Relations among religions are normal. Failure to hold religious harmony can plunge society into battle and chaos. Therefore, the cause of this newsletter is to record in element the elements that affect the sustainable harmony among multi-religious societies. The record additionally emphasized that elements including acceptance, knowledge, cooperation, fairness, and justice are crucial elements in preserving religious harmony. Therefore, this paper pursuits to shape a solid idea of multi-sectarian harmony in society. It must additionally contribute to the improvement of a multi-religious society. Try to hold and construct a sturdy dating among them. Religious harmony is vital to make sure solidarity and team spirit amongst believers of more than one religion. A multi-religious society is a network with one-of-a-kind religions, including Islam, Buddhism, Hinduism, Christianity, etc. To stay in a non-violent and harmonious society, it's far very crucial to just accept believers from different religions in a selected network. From the angle of Islam, Islam is a faith that accepts the range of religions on this international however denies that every one religion is proper. So is the case in almost all religions. Religious pluralism may be described because of the range of religions on this international; however, withinside the idea of religious pluralism, it could be described because of the perception that every religion on this international is absolute truths. Pluralists trust that every one religion withinside the international is actual and have to be respected. It could be very crucial to recognize that everyone's proper statements are legitimate and deserve the same appreciation. This handiest method that fans of one religion have to be given different reality statements, including Muslims, Jews, Hindus, and others. However, from the angle of Islam, Islam accepts more than one religion however does now no longer understand that every one religion is proper. Recognizing the origins of various religions in a multi-religious society is the important thing to making sure harmony and solidarity withinside the international. Recognizing that religious range in a multi-religious society is a crucial component in preserving social harmony. For example, Muslims who cost the lifestyles of different religious groups (including Buddhists, Hindus, and Christians) can enhance inter-religious members of the society and deal with them kindly. Therefore, the popularity of the lifestyles of the religious range must be visible as a resounding component for a selected network. Everyone residing in a multi-religious society has to recognize and be given religious freedom. One must accept and appreciate the beliefs of different religions. Respect for the lifestyles of the religious range additionally contributes to political and countrywide stability. Respect also can enhance interreligious members of the society via way of means of acknowledging the lifestyles of various religious groups. Mutual appreciation, cooperation, tolerance, knowledge, and kindness are crucial elements in residing in a multi-ethnic and religious society. To hold a harmonious society, all multi-religious believers must have the proper and freedom to exercise their faith. It must now no longer be abandoned (Abdullah et al., 2016). Islam has usually emphasized the idea of justice (alAdl). All divine religions are of belief of religious harmony. By studying different religions, it is very clear that all religions are of the view to respect other religions and followers of different beliefs.

Pandemics, and other risk-related contexts, require dynamic changes in behavior as situations develop. Human behavior is influenced by both explicit (cognitive) and implicit (intuitive) factors. In this study, we used psychological distance as a lens to understand what influences our decision-making with regards to risk in the context of COVID-19. The study was based on the rationale that our relational needs are more concrete to us than the risk of the virus. First, we explored the impact of social-psychological distance on participants’ risk perceptions and behavioral willingness. As hypothesized, we found that close social relationships of agents promoted willingness to engage in risky behavior. In the second phase, we tested an intervention designed to increase the concreteness of information about virus transmission as a mechanism to mitigate the bias of social influence. We found that the concreteness intervention resulted in significantly reduced willingness to engage in risky behavior. As such, communications aimed at changing the behavior of citizens during times of increased risk or danger, should consider conceptually concrete messaging when communicating complex risk, and hence may provide a valuable tool in promoting health-related behavior.

FACTS devices provide significance to enhance the functioning of both static and dynamic power systems. The type, size, and location of FACTS devices determine how well they achieve the goals of enhancing voltage stability, loadability, and loss minimization simultaneously. The aim of the function in this study includes voltage stability, line loadings, and loss minimization, which are expected to obtain the most benefits from their installation and other weights allocated to them. It has been determined how installing TCSC, and SVC will improve loadability under situations of continuously increasing load. In this study, a novel optimization technique known as differential evolution invasive weed optimization (DEIWO) is utilized to locate the optimal location for FACTS devices within the IEEE 14 bus system by considering valve point effects. DEIWO was a recently developed, dominant, and simple metaheuristic algorithm for genuine parameter optimization. DEIWO was inspired by the natural process of weed colonization and dissemination, and it is highly effective at addressing broad multi-dimensional, linear and nonlinear optimization problems. The proposed method was implemented in the IEEE 14 bus testing system. Different methods from the research papers have been used to compare and analyze the results. The results demonstrate the DEIWO algorithm's capability and show how it may be used in practice to locate FACTS devices in deregulated electricity networks in the most advantageous place. A comparison of the proposed process to existing algorithms found in the literature and implemented using traditional techniques reveals that it performs better in terms of precision and convergence

Let G[F,Vk, Huv] be the graph with k pockets, where F is a simple graph of order n ≥ 1,Vk= {v1,v2,··· ,vk} is a subset of the vertex set of F and Hvis a simple graph of order m ≥ 2,v is a specified vertex of Hv. Also let G[F,Ek, Huv] be the graph with k edge pockets, where F is a simple graph of order n ≥ 2, Ek= {e1,e2,···ek} is a subset of the edge set of F and Huvis a simple graph of order m ≥ 3, uv is a specified edge of Huvsuch that Huv− u is isomorphic to Huv− v. In this paper, we derive closed-form formulas for resistance distance and Kirchhoff index of G[F,Vk, Hv] and G[F,Ek, Huv] in terms of the resistance distance and Kirchhoff index F, Hv and F, Huv, respectively.

A method for measuring the real part of the weak value of spin for non-zero rest mass atoms is presented using a variant on the original Stern-Gerlach apparatus. The experiment utilises helium in the metastable 2³S₁ state. A full simulation for observing the real part of the weak value using the impulsive approximation has been carried out and it predicts a displacement of the beam, Δ_w, that is within the resolution of our detector. It also indicates how this shift might be increased. The full analysis also indicated that there is a limit, L, to the applicability of the weak value approximation and has been evaluated for our apparatus. This experiment has the possibility to be expanded to utilise other nobal gas species such as neon and argon in the ³P₂ metastable state, but we shall restrict this paper to metastable helium only.

This work presents the study of a reproducible acoustic emission method based on the launching of a metallic sphere and low-cost piezoelectric diaphragm. For this purpose, tests were first conducted on a carbon fiber-reinforced polymer structure, and then on an aluminum structure for comparative analysis. The Pencil Lead Break (PLB) tests were also conducted for comparisons with the proposed method. Different launching heights and elastic deformations of the structures were investigated. The results show higher repeatability for the sphere impact method, as the PLB is more affected by human inaccuracy, and it was also effective in damage detection.

Background: Mental health has a special relevance in adolescence. Therefore, this study aimed to analyze the possible association between emotional distress and suicidal ideation among adolescents. Methods: The article presents the results of a survey of secondary school students (N=1419) in the city of Tarragona, Spain. The aim of the analysis was to verify the existence of an association between suicidal ideation and risk factors such as emotional distress and low self-esteem. All the data were also analysed to determine the existence of statistically significant differences between girls and boys. Bivariate analyses were performed with chi-square, point-biserial coefficient and non-parametric tests for independent groups. Logistic regression analysis was also performed to calculate the probability of suicidal ideation. Results: These analyses showed that both emotional distress and low self-esteem increase the likelihood of suicidal ideation among adolescents. This was more evident among boys with high levels of emotional distress. Conclusion: Schools should develop suicide ideation prevention programs for adolescents based on situations that cause emotional distress.

This study explores the dependence of drone acoustic detection systems performance on distance, using learning machines with different complexities, from simple linear discriminants, to deep neural networks, such as YAMNet, which exploits transfer learning. Other machine learning systems studied are the multilayer perceptron, support vector machines and random forest. These methods have been evaluated with a meticulously selected database that includes a wide variety of drone and interference sounds, previously processed by array signal processing and affected by ambient noise. Two strategies for training learning machines are considered. In the first one, they are trained with unattenuated signals, trying to preserve the information generated by the sound sources, but they are tested considering the attenuation with distance, achieving effective detection at distances up to 200 metres with some methods, especially linear discriminant. In all cases, the existence of interferences that hinder detection is considered. In the second strategy, the systems are trained and tested with attenuated signals as a function of distance. The effective detection range increases up to 300 metres for most methods and up to 500 metres for the YAMNet based detector. In addition, this second approach raises the possibility of developing specialised detectors for each distance range, thus significantly extending the effective detection range. These results underline the promising potential of acoustic detection of drones at various distances, encouraging further exploration in this research area.

This study aims to identify benefits and barriers to distance education, particularly from the perspective of teachers in Saudi Arabia. As the applied data collection tool, a questionnaire was distributed to the general education teachers in three districts. The sample size of the study was 1076 teachers. The results revealed that despite several benefits gained from distance learning, there are also some barriers. Teachers found that the most important advantage in distance learning is the acquisition of technical skills during the online teaching processes, they learn more and use digital education platforms, they have sufficient time to prepare the scientific content, they were able to provide adequate technical solutions for their courses, and they have the opportunity to use multiple media to deliver their courses. With the introduction of distance learning, teachers have explored new ways to deliver course contents to students. It has fostered better ways to provide more interactive real-time and on-demand teaching and learning using modern technology, thus, helping teachers become familiar with the use of electronic resources. It seems that teachers invest in technical methods to enhance students’ performance. Also, teachers reported some obstacles that they face during remote teaching. Most of these problems are connection problems applied with devices and the internet, lack of students’ motivation to learn in distance, problems associated with urban learners.

Human number understanding is thought to rely on the analogue number system (ANS), working according to Weber’s law. We propose an alternative account, suggesting that symbolic mathematical knowledge is based on a discrete semantic system (DSS), a representation that stores values in a semantic network, similar to the mental lexicon or to a conceptual network. Here, focusing on the phenomena of numerical distance and size effects in comparison tasks, first we discuss how a DSS model could explain these numerical effects. Second, we demonstrate that DSS model can give quantitatively as appropriate a description of the effects as the ANS model. Finally, we show that symbolic numerical size effect is mainly influenced by the frequency of the symbols, and not by the ratios of their values. This last result suggests that numerical distance and size effects cannot be caused by the ANS, while the DSS model might be the alternative approach that can explain the frequency-based size effect.

This study aims to unravel the paradox of perceptions and knowledge of the flood victims towards the causes of the disaster in both internal and external context. Internal context comprises of a comparison of perceptions and knowledge based on individual characteristics (age, gender, education and income). Whereas, the external context includes the factors of the awareness of the victims towards the amount of rainfall, the impact of land use changes as well as the negligence of the responsible parties. The main objective of this study is to determine the differences of perception and knowledge of December 2014 flood victims in Kelantan towards the factors that lead to the flood. This disaster had resulted in huge amount of money loss as well as traumatize the victims in which can be felt to this day. Since that incident, there were various points of view and different perceptions in finding the cause of the disaster occurred. Besides that, the study found that the level of perception and knowledge as to the cause of the disaster is different in the internal context (individual characteristics). This difference has a significant influence on the awareness of the causes of the floods that occurred in the external context. Significant relationships at the level of p <0.05 has existed between perception and knowledge of the causes of the disaster victims affected by environmental changes in the last 10 years. This indicates that although the victim is aware of the physical environment changes happening around them, but all that is seen as not a major contributing factor to the cause of the floods in Kelantan in 2014.

In this paper, we show that the entropy or information flow of a gravitational system constrains the dependence of gravitational force on radial distance r which for very large r is equivalent to gravity in two dimensions. This proposal solves the problem of flat rotation curves of galaxies at very large distances.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

Our aim was to compare the inter-rater agreement of Transvaginal ultrasonography (TVS) with Magnetic Resonance Imaging (MRI) regarding diagnosing adenomyosis and for various predefined imaging features of adenomyosis in the same set of women. MRIs were assessed by four experienced radiologists and 2D TVS videoclips and 3D ultrasound volumes were assessed by five experienced sonographers. Each rater subjectively evaluated the presence or absence of adenomyosis, assessed various imaging. Fleiss kappa (κ) was used to reflect inter-rater agreement for categorical data and the Intraclass correlation coefficient (ICC) to reflect the reliability of quantitative data. Agreement between raters for diagnosing adenomyosis was higher for TVS than for MRI (κ =0.42 vs.0.28). MRI had a higher inter-rater agreement in assessing wall asymmetry irregular Junctional Zone (JZ) presence of myometrial cysts, while TVU had a better agreement for globular shape. MRI showed a moderate-good reliability for measuring the JZ (ICC=0.57-0.82). For TVS, JZ was unmeasurable in &gt; 50% of cases and the remaining cases had a low reliability (ICC=-0.31-0.08). We found that Inter-rater agreement for diagnosing adenomyosis was higher for TVS than for MRI, despite that MRI showed a higher inter-rater agreement in most specific features. Measurements of JZ in the coronal plane with 3D TVS were unreliable and thus unlikely to be useful for diagnosing adenomyosis.

Pancreatic beta cells secrete insulin in response to stimulation with glucose and other nutrients, and impaired insulin secretion plays a central role in development of diabetes mellitus. Pharmacological management of diabetes includes various antidiabetic drugs, including incretins. The incretin hormones, glucagon-like peptide-1 and gastric inhibitory polypeptide, potentiate glucose-stimulated insulin secretion by binding to G protein-coupled receptors, resulting in stimulation of adenylate cyclase and production of the secondary messenger cAMP, which exerts its intracellular effects through activation of protein kinase A or the guanine nucleotide exchange protein 2A. The mo-lecular mechanisms behind these two downstream signaling arms are still not fully elucidated and involve many steps in the stimulus-secretion coupling cascade, ranging from the proximal regula-tion of ion channel activity, to the central Ca2+ signal, and the most distal exocytosis. In addition to modifying intracellular coupling, the effect of cAMP on insulin secretion could also be at least partly explained by the impact on intercellular coupling. In this review, we systematically describe the possible roles of cAMP at these intra- and inter-cellular signaling nodes, keeping in mind the rele-vance for the whole organism and translation to humans.

Recently, inter-vehicle communication, which helps to avoid collision accidents (by driving safety support system) and facilitate self-driving (by dissemination of road and traffic information), has attracted much attention. In this paper, in order to efficiently collect road / traffic information in the request / response manner, first a basic method, Content-centric network (CCN) for Vehicular network (CV), is proposed, which applies CCN cache function to inter-vehicle communication. Content naming and routing, which take vehicle mobility into account, are investigated. On this basis, the CV method is extended (named as ECV) to avoid the cache miss problem caused by vehicle movement, and is further enhanced (named as ECV+) to more efficiently exploit cache buffer in vehicles, caching content according to a probability decided by channel usage rate. Extensive evaluations on network simulator Scenargie, with realistic open street map, confirm that the CV method and its extensions (ECV, ECV+) effectively reduce the average number of hops of data packets (by up to 47%, 63%, 83% respectively) and greatly improve the content acquisition success rate (by up to 356%, 444%, 689%, respectively), compared to the method without cache mechanism.

The association between depressive symptomatology and cognitive decline has been examined using the Centre for Epidemiologic Studies-Depression Scale (CES-D), however, concerns have been raised about this self-report measure. Here, we examined how the CES-D total score from the 14- and 10-item versions compared to the 20-item version in predicting progression to cognitive decline from a cognitively unimpaired baseline. Data from 1,054 participants were analysed using ordinal logistic regression, alongside moderator and receiver-operating characteristics curve analyses. All baseline total scores significantly predicted progression to cognitive decline. The 14-item version was better than the 20-item version in predicting consensus diagnosis, as shown by their AICs, while also showing the highest accuracy when discriminating between participants by diagnosis at last visit. We did not find sex to moderate the relationship between CES-D score and cognitive decline. Current findings suggest the 10- and 14-item versions of the CES-D are comparable to the 20-item version, and that the 14-item version may be better at predicting longitudinal consensus diagnosis compared to the 20-item version.

Phoenixin-14 (PNX) - a bioactive peptide recently discovered in the rat brain, is highly conserved among many animal species: rodents (rat, mouse), pig, dog as well as humans. In rodents, PNX is expressed in areas responsible for the transmission of sensory information dorsal root ganglia (DRG) neurons, where it is present at relatively high levels and may preferentially suppress visceral pain. However, so far the presence of PNX has not been investigated in DRG in pigs, a species which, due to its anatomical and histological similarity to humans, is considered a better model for biomedical studies than rodents. The present study aimed to investigate the immunoreactivity of PNX in the DRG of the domestic pig. The collected spinal ganglia from the cervical (C), thoracic (Th), lumbar (L) and sacral (S) sections were transversely divided into serial sections of 10 μm thickness. DRG sections from each level of the spinal cord were double-labeled immunohistochemically using antibodies to PNX in a mixture with antibodies to: cocaine and amphetamine related transcript (CART), calretinin (CRT), calcytonin gene-related peptide (CGRP), galanin (GAL), nitric oxide synthase (nNOS), pituitary adenylate cyclase-acrivating polypeptide (PACAP), substance P (SP) or somatostatin (SOM), respectively. Immunohistochemical studies revealed PNX immunoreactivity in approximately 20% of nerve cells in all DRG examined, highlighting mainly the presence of the peptide in cells of small diameter (approximately 74% of all PNX-positive neurons found). Double labeling of DRG sections showed that PNX-immunopositive neurons stained also for CGRP (96.1%), SP (88.5%), nNOS (52.1%), GAL (20.7%), CRT (10.05%), PACAP (7.4%), CART (5.1%), or SOM (4.7%). Our research revealed for the first time the presence of the new peptide PNX in the sensory ganglia of the domestic pig, its co-localization with other important neurotransmitters involved in sensory transmission and its percentage distribution in ganglion domains. The exact function of PNX in DRG is not yet known, however, the high degree of co-localization of this peptide with the main nociceptive transmitters SP and CGRP may indicate its function in modulation of pain transmission.

Isolation of new plant-derived lactic acid bacteria (LAB) is highly prioritized in developing novel starter cultures for plant-based fermentation. This study explores the diversity of LAB in Danish flowers and their potential use for plant-based food fermentation. 46 flower samples under 34 genera were collected for LAB isolation. By introducing an enrichment step, in total of 61 LAB strains were isolated and identified by using MALDI-TOF and 16S rRNA sequencing. These strains represent 24 species across 9 genera, predominantly Leuconostoc mesenteroides, Fructobacillus fructosus, Apilactobacillus ozensis, and Apilactobacillus kunkeei. Phenotypic screening for exo-cellular polysaccharide production revealed that 40 strains exhibited sliminess or ropiness on sucrose containing agar plate. HPLC analysis confirmed that all isolates produced exo-cellular polysaccharides containing glucose, fructose or galactose as sugar monomer. Therefore, the strains were glucan, fructan and galactan producers. The suitability of these strains for plant-based fermentations was characterized by using almond, oat and soy milk. The results showed successful acidification in all three types of plant-based matrices but only observed texture development in soy by Leuconostoc, Weissella, Lactococcus, Apilactobacillus, and Fructobacillus. The findings highlight the potential of flower-derived LAB strains for texture development in soy-based dairy alternatives.

Background: In South Africa, a relatively small number of children and adolescents belong to sports clubs, and adolescent participation in organised sport has steadily declined, especially in recent years. Participation levels in organised sport has decreased during late childhood and, more increasingly, as adolescents grow older. Aim: Therefore, the aim of this study was to determine the participation of secondary school learners aged 14 to 16 years in organised sport. Methods: The study was a quantitative and descriptive, cross-sectional design. The study sample was 329 conveniently sampled secondary school learners, males, and females, aged 14 to 16 years from three state schools in Cape Town, Western Cape Province. A researcher-generated, self-administered questionnaire on organised sport participation was developed, piloted, and validated among 44 learners of similar background, who did not participate in the main study. The statistical software, SPSS version 27, was used to capture and analyse the research data. The level of statistical significance for the study was set at p < 0.05. Results: A total of 66.9% of participants in the study sample participated in organised sport. Low but significant correlations were reported between gender and participation in organised sport (r = 0.22; p < 0.001), gender and friend participation in organised sport (r = 0.24; p < 0.001), participants participation and sibling participation in organised sport (r = 0.23; p < 0.001), participants participation and friend participation in organised sport (r = 0.36; p < 0.001), and between participant participation and parental participation in organised sport (r = 0.17; p = 0.003). Conclusion: Adolescent participation in organised sport was lower in the present circumstances compared to participation in the past three years. Adolescent males had higher participation levels in organised sport compared to adolescent females. There is an urgent need for solutions to counteract the low levels of adolescent participation in organised sport, especially in adolescent females.

Aims: To report on the clinico-pathological features of a series of 12 intra-oral mucoepidermoid carcinomas showing exclusive intra-cystic growth. Methods and methods: All mucoepidermoid carcinomas diagnosed in the period 1990-2012 were retrieved, the original histological preparations were reviewed to confirm the diagnosis, and from selected cases, showing exclusive intra-cystic neoplastic component, additional sections were cut at 3 subsequent 200m intervals and stained with Hematoxylin-Eosin, PAS and Alcian Blue, to possibly identify tumor invasion of the adjacent tissues, which could have been overlooked in the original histological preparations. Also, pertinent findings collected from the clinical charts and follow-up data were analyzed. Results: We identified 14 intraoral mucoepidermoid carcinomas treated by conservative surgery and with a minimum follow up of 5 years. The neosplasm were located in the hard palate (9 cases), the soft palate (2), the cheeck (2) and the retromolar trigone (1). In all instances histological examination was revealed the presence of a single cystic space, containing clusters of columnar, intermediate, epidermoid, clear and mucous-producing cells, the latter exhibiting distinct intra-cytoplasmic mucin production, as confirmed by PAS and Alcian Blue stains. The cysts were entirely circumscribed by fibrous connective tissue and no solid areas or infiltrating tumour clusters were detected. Conservative surgical resection was performed in all cases and no recurrences or nodal metastases were observed during the follow up period. Conclusions: Mucoepidermoid carcinomas showing prominent (>20%) intra-cystic proliferation currently are considered low-grade tumours. In addition, we also unveil the possibility that mucoepidermoid carcinomas, at least in their early growth phase, may display an exclusive intra-cystic fashion and might be considered as in situ carcinomas, unable to infiltrate adjacent tissues or metastasize.

This paper explores the influence of GenAI chatbot (ChatGPT) and AI-based simulation (SimVenture) on the start-up intention through the mediating roles of entrepreneurship educational outcomes; entrepreneurship cognition, entrepreneurship competencies, and innovation spirit of students in higher education institutions in the Middle East. The proposed conceptual framework was empirically tested with partial least squares structural equation modeling using 166 survey responses from fourth-year undergraduate students enrolled in business administration programs in four higher education institutions in the Middle East region. The findings reveal that ChatGPT significantly promotes start-up intention, while ChatGPT has insignificant influence on entrepreneurship educational outcomes. Conversely, the SimVenture platform negatively influences start-up intention. Additionally, the SimVenture platform significantly improves entrepreneurship educational outcomes; thereby entrepreneurship educational outcomes directly influence start-up intention. Furthermore, the mediation paths show ChatGPT has an insignificant indirect influence on start-up intention through the mediating roles of entrepreneurship educational outcomes, while the SimVenture platform exhibits a notable indirect influence on startup intentions through improved entrepreneurship educational outcomes. These findings seek to provide insights for higher education institutions in the Middle East region in leveraging GenAI chatbot (ChatGPT) and AI-based simulation (SimVenture) to enhance entrepreneurial knowledge, awareness, competencies, and the spirit of innovation, which leads to promoting students’ intentions to start their businesses. Theoretically, the study contributes to filling the gap in the existing literature between AI tools and entrepreneurship education. This study is among the first to empirically explore the influences of GenAI chatbot (ChatGPT) and AI-based simulation (SimVenture) on entrepreneurship educational outcomes and the intention of students to embark on entrepreneurial ventures. It highlights the need for understanding AI technologies as distinct pedagogical tools.

For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are deployed around the world. Proliferation of artificial intelligence (AI) and machine learning (ML) technologies has gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference scale as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capture a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets.

Critical distance concerns precise digital levelling, which has inaccurate results at a certain sighting distance. The influence of critical distance on a measured height difference has been confirmed by calibrating certain digital levels and their appropriate code devices on a vertical comparator under laboratory conditions. The paper describes the research based on experimental measurements done to find the influence of critical distance on height differences obtained by precise digital levels of Leica NA3003 and DNA03. The processing of the measurement results consisted of de-fining a random error on a station by using parameter estimation of an error model to specify a partial error on a station dependent on sighting distance and then the processing phase continues with the finding the relation between sighting distance and dispersion of height differences ac-quired by digital levelling under terrain conditions. The theoretical part involves the development of levelling accuracy theories that vary over time by view on random and systematic error propagation. The numerical and graphical solution of the experimental measurements involves ordering the height differences into sighting groups according to the sighting distances and the computed standard deviations, which represent a measure of height differences dispersion. The suspicious sighting groups are highlighted in the table and compared with theoretical data pro-vided by authors of calibrating the same digital levels

In this brief paper a quantum algorithm to calculate the Hamming distance of two binary strings of equal length (or messages in theory information) is presented. The algorithm calculates the Hamming weight of two binary strings in one query of an oracle. To calculate the hamming distance of these two strings we only have to calculate the Hamming weight of the xor operation of both strings. To test the algorithms the quantum computer prototype that IBM has given open access to on the cloud has been used to test the results.

The article delves into the intricate phase transitions of 1-Octadecanol and n-Nonadecane within a binary system, unveiling dynamic structural changes under varying conditions. Through Fouri-er-transform infrared (FTIR) analysis, specific molecular vibrations were identified, shedding light on the molecular composition and interactions. The study highlights the challenges in detecting subtle phase transitions and emphasizes the individuality of molecular behaviours in closely related compounds. The findings underscore the complexity of phase transitions in binary systems and advocate for a nuanced approach to studying molecular structures and behaviours. Addi-tionally, the research contributes valuable insights into the molecular dynamics of Organic Polymer Functional Adsorption Materials, offering implications for synthetic waxy layers in plant epidermis.

The diverse physical appearances and wide density range of polymer bead foams offer immense potential across various applications and future advancements. The multi-scale and multi-level structural features of bead foams involve many fundamental scientific topics. This review presents a comprehensive overview of recent progress in bead foams preparation and molding techniques. Initially, a comparative analysis is conducted among bead foam characteristics of distinct polymers, based on their unique properties. Subsequently, a summary and comparison of molding processes employed for fabricating bead foam parts are provided. Beyond traditional methods like steam-chest molding (SCM) and adhesive-assisted molding (AAM), emerging techniques like in-mold foaming and molding (IMFM) and microwave selective sintering (MSS) are highlighted. Lastly, the bonding mechanisms behind the diverse molding methods are discussed.

This study analyses 749 drawings of five female Bornean orang-utans (Pongo pygmaeus) at Tama Zoological Park in Japan. We searched for differences between individuals but also tried to identify possible temporal changes among the drawings of one individual, Molly, who drew almost 1,300 drawings from 2006 to 2016. An analysis of the drawings was carried out after collecting quantitative and qualitative variables. Our findings reveal evidence of differences in the drawing style of the five individuals as well as creative changes in Molly’s drawing style throughout her lifetime. Individuals differed in terms of the colours used, the space they filled but also the shapes (fan patterns, circles or loops) they drew. Molly drew less and less as she grew older, and we found a significant difference between drawings produced in winter, when orang-utans were kept inside and had less activity, and those produced during other seasons. Our results suggest that the drawing behaviour of these five orang-utans is not random and that differences among individuals might reflect differences of styles, states of mind but also motivation to draw.

Fractional delay-Doppler (DD) channel estimation in Orthogonal Time Frequency Space (OTFS) systems poses a significant challenge considering the severe effects of Inter-Path Interference (IPI). To this end, several algorithms have been extensively explored in literature for accurate low-complexity channel estimation in both integer and fractional DDs scenarios. In this work, we develop a variant of the state-of-the-art Delay-Doppler Inter-Path Interference Cancellation (DDIPIC) algorithm which progressively cancels the IPI as estimates are obtained. The key advantage of the proposed approach is that it requires only a final refinement procedure reducing the complexity of the algorithm. Specifically, the time difference in latency between the proposed approach and the DDIPIC algorithm is almost proportional to the square of the number of estimated paths. Numerical results show that the proposed algorithm outperforms the other channel estimation schemes achieving lower Normalized Mean Square Error (NMSE) and Bit Error Rate (BER).

This study investigated the magnitude and direction of inter-limb asymmetry in 21 professional female sepak takraw players across several task-specific tests. Five inter-limb asymmetry assessments were employed: unilateral countermovement jump (Uni-CMJ), bilateral countermovement jump (Bi-CMJ), single leg hop (SLH), triple hop test (THOP), and isokinetic concentric peak torque of the knee flexors and extensors at 60 deg/sec-1, 120 deg/sec-1 and 180 deg/sec-1 angular velocities. A “true” inter-limb asymmetry was only observed for Uni-CMJ jump height (16.62%) and THOP distance (6.09%). Kappa coefficients demonstrated fair agreement in the direction of asymmetry between the Uni-CMJ and Bi-CMJ tests for jump height (Kappa = 26.67), but only slight agreement for peak force (Kappa = 0.11), propulsive impulse (Kappa = -0.12) and eccentric impulse (Kappa = -0.14), respectively. Fair agreement was observed between the SLH and THOP (Kappa = 0.32). Slight to moderate agreement was found for concentric peak torque across angular velocities for the knee extensors (Kappa = 0.08 to 0.48) while fair to nearly perfect agreement was noted for the knee flexors (Kappa = 0.31 to 1). The Uni-CMJ and THOP are most sensitive to detect between-limb asymmetries in female sepak takraw players. However, given the inconsistency in asymmetry direction across tests, monitoring asymmetry direction is crucial for conditioning.

With the rise of powerful computational technologies, healthcare systems are going through a paradigm shift to the era of healthcare 5.0, also known as" smart healthcare". This new-age healthcare system not only enhances the life of patients but also aims to reduce healthcare costs significantly. People's health data collected through various means, including various Internet of Things (IoT) devices, is too sensitive to be regulated by particular organisations owing to increasing privacy and security threats. The sheer size of data makes it difficult for centralised bodies to regulate, due to which third-party involvement is introduced, which again poses threats to the privacy of the data holder. Motivated by the abovementioned gaps, we propose a decentralised approach using Ethereum blockchain technology and Oasis protocol. The patient-driven solution approach effectively solves the privacy problem by providing complete control of data to the owner. Along with this, byproducts such as fine-grained access control and on-chain data processing capabilities are also obtained.

Clinical rehabilitation and sports performance analysis both require the objectification of movement. Kinovea^© is a free 2D motion analysis software that enables the establishment of kinematics parameters. This low-cost technology has been used in sports sciences, as well as clinical field and research work. Although it has been validated as a tool with which to assess time-related variables, this is not yet the case regarding angular and distance variables. The main objective of this study was to determine the validity and reliability of the Kinovea software in obtaining angular and distance data at different perspectives of 90°, 75°, 60° and 45°. For this purpose, a figure with 29 points was designed (in AutoCAD) and 24 frames analysed. Each frame was examined by three observers who each made two attempts. For each export data item, 20 angles and 20 distance variables were calculated, with intra- and inter-observer reliability also analysed. To evaluate Kinovea reliability and validity a multiple approach was applied involving the following analysis: -systematic error with a two-way ANOVA 2x4; -relative reliability with ICC and CV (95% confidence interval); -absolute reliability with Standard Error. The results thus obtained indicate that the Kinovea software is a valid and reliable tool that is able to measure accurately at distances up to 5 m from the object and at an angle range of 90°–45°. Nevertheless, for optimum results an angle of 90° is suggested.

Currently, Pythagorean fuzzy sets (PFSs) have been widely applied in various fields due to their substantial advantages in expressing and dealing with uncertainty. However, measuring the similarity and difference between PFSs effectively remains an unresolved issue. Inspired by Tanimoto similarity, we propose a novel set of similarity and distance measures for PFSs. We delve into the theoretical properties of the proposed measures and compare it with existing PFSs measures. Numerous numerical examples validate their rationality and effectiveness. Furthermore, our experimental findings suggest that in contrast to existing measures, the introduced measures successfully circumvent various counter-intuitive issues encountered by current measures, and yield more pronounced outcomes in the discrimination of different fuzzy sets. This enhances the uniqueness and superiority of our measures. Finally, we developed two decision models based on the proposed measures and validated their applicability in three applications.

Faba bean (Vicia faba L.) is a partially outcrossing species requiring an isolation distance to maintain genetic purity when more than one variety is grown in field conditions. This information is crucial for seed growers and faba bean breeders. A study was conducted at the University of Sydney’s Plant Breeding Institute, Narrabri over two years to examine the extent of natural outcrossing using a creamy white flower characteristic as a morphological marker, which is controlled by a single recessive gene. The white flowered genotype (IX225c) was grown in paired rows of 150 m length in four directions from a central 480 m2 plot of the normal flowered genotype PBA Warda. A beehive was placed in the central plot at flowering time and natural pollination was allowed. At maturity, seed samples were taken from the white flowered genotype at designated intervals along each axis and 100 seeds from each sample were grown in the glasshouse/birdcage to the 4-5 leaf stage and the proportion of plants displaying stipule spot pigmentation (normal flower colour and spotted stipule are linked) was used to determine the percentage of outcrossing. Maximum outcrossing of 2.28% occurred where both genotypes were grown side by side (0 m) and the degree of outcrossing decreased as the distance along each axis from the central plot increased. At 6 m distance the outcrossing was less than 1%, however on occasion it increased to 1% beyond a distance of 100 m, indicating the volatile and unpredictable nature of bee flights. Distance had the major effect on outcrossing, but direction and its interaction had no effect. The results suggest that to limit outcrossing to below 0.5%, a distance of more than 150 m between plots of different faba beans cultivars would be required. It also indicated that Australian faba bean genotypes are mostly self-fertile and a relatively narrow isolation distance will ensure self-fertilization in seed production and breeding programs.

Allama Iqbal Open University (AIOU) is the largest distance learning institute of Pakistan and providing education to 1.4 million students. This is fairly a large setup across the country where students are geographically distributed. Currently the system works on a manual approach which is not efficient. Allocation of tutors and study centers to students plays a key role in distance learning for a better learning environment. Assigning tutors and study centers to distance learning students is a challenging task when there is huge geographical spread. The utilization of geospatial technologies in open and distance learning can fix allocation problems. This research analyzes the real data of twin cities Islamabad and Rawalpindi. The results show that the geospatial technologies can be used for efficient and proper resource utilization and allocation, which in turn can save the time and money. The overall idea fits into improved distance learning framework and related analytics.

Multiple myeloma (MM) accounts for about 10 to 15% of all diagnosed hematologic malignancies and about 1-2% of all cancer cases. Approximately 80-90% of MM patients develop bone disease and the changes rarely regress. It is only possible to stop or slow their progression. A major role in bone destruction in MM is attributed to the Wnt signaling pathway, and its action can be modified by various types of interventions including training and diet. Therefore, the aim of this project was to evaluate the effects of a Nordic Walking (NW) training cycle and intermittent fasting (IF) on the levels of selected bone turnover markers associated with the Wnt pathway in patients with MM. Results from 35 patients divided into training (NW and IF NW) and non-training (IF and control) groups were included in the analysis. A 6-week training cycle involving 60-minute workouts 3 times a week was conducted. Body mass and composition and the levels of: vitamin D, calcium and phosphorus, beta2-microglobulin and albumin were examined before and after completion of training cycle. Markers of bone turnover were also determined: sclerostin (SOST), Dickkopf-related protein 1 (DKK-1), osteoprotegrin (OPG), osteopontin (OPN) and Tartrate-resistant acid phos-phatase 5b (TRACP 5b). There was no negative effect of IF or combined training and fasting on the nutritional status of the patients (the level of albumines was unchanged). Both training groups showed an increase in serum concentrations of the active metabolite of vitamin D (IF NW and NW: p=0.001 and p=0.022, re-spectively). The change in the concentration of this vitamin negatively correlated with the con-centration of TRACP 5b (r=-0.413, p=0.014). Evaluating the concentrations of markers related to bone turnover, a reduction in the concentrations of SOST (time: p=0.026, time vs. group: p=0.033) and TRACP 5b (time: p

This study presents an innovative analytical method for the detection and quantification of 14 β-agonists in pork using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The method involves the use of a solid phase microextraction (SPME) probe, consisting of a wooden tip coated with polyacrylonitrile (PAN) and molecularly imprinted polymer (MIP). This probe has been integrated into an automated extraction device, designed in our laboratory to extract the target analytes prior to LC-MS/MS analysis. Compared to the traditional solid-phase extraction (SPE) procedure, the proposed approach significantly reduces the costs related to adsorbent ma-terial and solvent consumption, and shortens purification time, thereby reducing both the tech-nical workload and associated expenses. The methodology has been carefully validated in ac-cordance with the National Standard of the People's Republic of China (GB/T 22286-2008), evalu-ating key parameters including the limit of detection (LOD), limit of quantification (LOQ), matrix effects, linearity, and both intra-day and inter-day precision. The average recovery rates for all β-agonists were observed to lie between 71.6% and 82.2%, with relative standard deviations (RSDs) consistently below 15% for both types of precision. The LOD and LOQ values were found to range from 0.09 to 0.39 μg/kg and 0.27 to 0.99 μg/kg, respectively. In terms of accurate detection of positive samples, the developed method demonstrated a closer approximation to the true value of the sample compared to the National Standard method GB/T 31658.22-2022. Overall, the proposed method exhibits substantial promise for the routine assessment of β-agonists in pork and presents a practical alternative for compliance monitoring in regulatory settings.

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

Long-distance Real-Time Kinematic (RTK) positioning is crucial for applications in remote areas, such as maritime environments. Achieving 2-3 cm accuracy with RTK requires successful ambiguity resolution, which involves two main two steps: identifying the best integer ambiguity candidate and confirming its validity. While previous research has largely concentrated on the first step, including the development of Cascading Ambiguity Resolution methods, and reducing tropospheric delay, studies on the validation of ambiguity for long-distance RTK are limited. This study conducts a thorough examination of ambiguity validation for long-distance RTK, focusing on two prevalent methods: the theoretical success rate and the R-ratio test. The results reveal several key insights. Firstly, the six commonly used bounds for the theoretical success rate are not accurate reflection of the actual success rate, making them unsuitable for long-distance RTK applications. Secondly, the R-ratio test proves to be dependable when the threshold is set above 1.7, assuming there is a minimum observation period of one minute and at least ten satellites are visible. However, the probability of successfully resolving ambiguities with the R-ratio test does not surpass 50%. Additionally, if ambiguity resolution is not achieved within 20 minutes, simply prolonging the observation time is generally unproductive. To improve the performance of ambiguity resolution in practical situations that require extended observation times, this research proposes a novel ambiguity validation method. This new approach is based the duration for which an integer ambiguity resolution candidate maintaining the best status. This method aims to provide a reliable means of validating ambiguities in case that the R-ratio test fails.

Accurately predicting the distance an object will travel to its destination is very important in various sports. Acceleration sensors as a means of real-time monitoring are increasingly gaining increasing attention in sports. Due to the low energy output and power density of Triboelectric Nanogenerators (TENGs), recent efforts have focused on developing various acceleration sensors. However, these sensors suffer from significant drawbacks of large size, complexity, high power input requirements, and high cost. Here, we described a portable and cost-effective real-time refreshable strategy design comprising a series of individually addressable and controllable units based on TENGs embedded in a flexible substrate. This results in a highly sensitive, low-cost, and self-powered acceleration sensor. Putting, which accounts for nearly half of the strokes played, is obviously an important component of the golf game. The developed acceleration sensor has an accuracy controlled within 5 %. The initial velocity and acceleration of the forward movement of a rolling golf ball after it is hit by a putter can be displayed, and the stopping distance quickly calculated and predicted in about 7 seconds. This research demonstrates the application of the portable TENG-based acceleration sensor, while paving the way for designing portable, cost-effective, scalable, and harmless ubiquitous self-powered acceleration sensors.

In this work, the conceptions of the electrodynamics of action-at-a-distance and of electromagnetic fields are reviewed and confronted, with the aim of clarifying whether it is possible to propose a Weberian theory of action-at-a-distance, which together with a renewed mechanics (both re-specting the existence of a limiting velocity c) could constitute a framework capable of yielding predictions equivalent to those of the conventional Maxwell-Lorentz and SRT framework. In this work, it is shown that, considering basic assumptions about the interaction process, understanding it as an action exchange process, it is possible to derive the expression of the electrodynamic Phipps’ potential energy between two interacting charges. It is also shown that, with the same assumptions and the additional hypothesis that the interaction is supported by a mediating agent (aether or field) which is perturbed by the transmission of action, this perturbation obeys the wave equation (one of the main drawbacks of Weberian electrodynamics). Another aim of the work is to confront the relational electrodynamic formulations (supported by Galilean transformations that assume a universal time) with the Maxwell-Lorentz formulation of electromagnetism (linked to Lorentz transformations that entail local times), in order to elucidate whether the space-time to which the SRT leads us is a mere mathematical artifice or an inescapable reality.

Vegetation identification using remote sensing is essential for understanding terrestrial ecosystems and monitoring climate change. This technology by providing high-resolution images of the Earth's surface from satellites, aircraft, and drones has revolutionized the way scientists study and un-derstand vegetation patterns, providing information on the distribution, structure, and health of plant communities in different regions and biomes. However, identifying vegetation in multi and hyperspectral images remains a difficult, time-consuming, and costly process. Traditional methods for vegetation mapping involve the in-terpretation of spectral indices such as the Normalized Difference Vegetation Index (NDVI), alt-hough they often suffer from limitations such as manual interpretation and limited spatial resolution of satellite images. Furthermore, machine learning techniques, artificial neural networks, decision trees, support vector machines, and random forests have shown promising results in identifying and classifying vegetation cover from hyperspectral images. However, due to the need for per-son-nel with specialized expertise, their application on a large scale is still challenging. On the other hand, correlation methods between curves have emerged as powerful and simplified tool for comparing spectral signatures of hyperspectral image pixels and efficiently classifying vegetation in a study area. Pearson's correlation coefficient and spectral angle mapper are among the most common correlation methods used in vegetation mapping. These correlation methods, among many others available, offer an easily implemented and computationally efficient approach, making them particularly useful for applications in developing countries or regions with limited resources. In this article, a comparative study of correlation/distance metrics was conducted for the detection of vegetation pixels in hyperspectral images. The study allowed for the comparison of five distance and/or correlation metrics: direct correlation, cosine similarity, normalized Euclidean distance, Bray-Curtis’s distance, and Pearson correlation. The two metrics that yielded the best accuracy results in vegetation pixel detection were direct correlation and Pearson correlation. Based on the selected methods, a vegetation detection algorithm was implemented and validated on a hyper-spectral image of the Manga neighborhood in Cartagena de Indias, Colombia. The spectral library was utilized for image processing, while the numpy and scipy libraries in the Python programming language were used for the mathematical calculation of correlations. Both the study's approach and the implemented algorithm aim to serve as a reference for conducting detection studies of various material types in hyperspectral images using open-access programming platforms.

Here we use the flat Friedmann-Lemaitre-Robertson-Walker metric describing a spatially homogeneous and isotropic universe to derive the cosmological redshift distance in a way which differs from that which can be found in the astrophysical literature. We use the co-moving coordinate r_e (the subscript e indicates emission) for the place of a galaxy which is emitting photons and r_a (the subscript a indicates absorption) for the place of an observer within a different galaxy on which the photons - which were traveling thru the universe - are absorbed. Therefore the real physical distance - the way of light - is calculated by D = a(t₀) r_a - a(t_e) r_e. Here means a(t₀) the today’s (t₀) scale parameter and a(t_e) the scale parameter at the time of emission (t_e) of the photons. Nobody can doubt this real travel way of light: The photons are emitted on the co-moving coordinate place r_e and are than traveling to the co-moving coordinate place r_a. During this traveling the time is moving from t_e to t₀ (t_e ≤ t₀) and therefore the scale parameter is changing in the meantime from a(t_e) to a(t₀). Using this right way of light we calculate some relevant classical cosmological equations (effects) and compare these theoretical results with some measurements of astrophysics. As one result we get e.g. the today’s Hubble parameter H_0a ≈ 62.34 km/(s Mpc). This value is smaller than the Hubble parameter H_0,Planck ≈ 67.66 km/(s Mpc) resulting from Planck 2018 data [12] which is discussed in the literature.

The k-means is one of the most popular and widely used clustering algorithm, however, it is limited to only numeric data. The k-prototypes algorithm is one of the famous algorithms for dealing with both numeric and categorical data. However, there have been no studies to accelerate k-prototypes algorithm. In this paper, we propose a new fast k-prototypes algorithm that gives the same answer as original k-prototypes. The proposed algorithm avoids distance computations using partial distance computation. Our k-prototypes algorithm finds minimum distance without distance computations of all attributes between an object and a cluster center, which allows it to reduce time complexity. A partial distance computation uses a fact that a value of the maximum difference between two categorical attributes is 1 during distance computations. If data objects have m categorical attributes, maximum difference of categorical attributes between an object and a cluster center is m. Our algorithm first computes distance with only numeric attributes. If a difference of the minimum distance and the second smallest with numeric attributes is higher than m, we can find minimum distance between an object and a cluster center without distance computations of categorical attributes. The experimental shows proposed k-prototypes algorithm improves computational performance than original k-prototypes algorithm in our dataset.

The Performance Assessment for California Teachers (PACT) is a high stakes summative assessment that was designed to measure pre-service teacher readiness. We examined the inter-rater reliability (IRR) of trained PACT evaluators who rated 19 candidates. As measured by Cohen’s weighted kappa, the overall IRR estimate was .17 (poor strength of agreement). IRR estimates ranged from -.29 (worse than expected by chance) to .54 (moderate strength of agreement); all were below the standard of .70 for consensus agreement. Follow up interviews of 10 evaluators revealed possible reasons we observed low IRR, such as departures from established PACT scoring protocol, and lack of, or inconsistent, use of a scoring aid document. Evaluators reported difficulties scoring the materials that candidates submitted, particularly the use of Academic Language. Cognitive Task Analysis (CTA) is suggested as a method to improve IRR in the PACT and other teacher performance assessments such as the edTPA.

To keep the global navigation satellite system functional during extreme conditions, it is a trend to employ autonomous navigation technology with inter-satellite link. As in the newly built BeiDou system (BDS-3) equipped with Ka-band inter-satellite links, every individual satellite has the ability of communicating and measuring distances among each other. The system also has less dependence on the ground stations and improved navigation performance. Because of the huge amount of measurement data, centralized data processing algorithm for orbit determination is suggested to be replaced by a distributed one in which each satellite in the constellation is required to finish a partial computation task. In current paper, the balanced extended Kalman filter algorithm for distributed orbit determination is proposed and compared with whole-constellation centralized extended Kalman filter, iterative cascade extended Kalman filter, and increasing measurement covariance extended Kalman filter. The proposed method demands a lower computation power however yields results with a relatively good accuracy.

We investigate the stationary and dynamic properties of the celebrated Nosé-Hoover dynamics of many-body interacting Hamiltonian systems, with an emphasis on the effect of inter-particle interactions. To this end, we consider a model system with both short- and long-range interactions. The Nosé-Hoover dynamics aims to generate the canonical equilibrium distribution of a system at a desired temperature by employing a set of time-reversible, deterministic equations of motion. A signature of canonical equilibrium is a single-particle momentum distribution that is Gaussian. We find that the equilibrium properties of the system within the Nosé-Hoover dynamics coincides with that within the canonical ensemble. Moreover, starting from out-of-equilibrium initial conditions, the average kinetic energy of the system relaxes to its target value over a size-independent timescale. However, quite surprisingly, our results indicate that under the same conditions and with only long-range interactions present in the system, the momentum distribution relaxes to its Gaussian form in equilibrium over a scale that diverges with the system size. On adding short-range interactions, the relaxation is found to occur over a timescale that has a much weaker dependence on system size. This system-size dependence of the timescale vanishes when only short-range interactions are present in the system. An implication of such an ultra-slow relaxation when only long-range interactions are present in the system is that macroscopic observables other than the average kinetic energy when estimated in the Nosé-Hoover dynamics may take an unusually long time to relax to its canonical equilibrium value. Our work underlines the crucial role that interactions play in deciding the equivalence between Nosé-Hoover and canonical equilibrium.

The aging mechanism of 10% and 30% nickel substituted manganese hexacyanoferrate cathode material in aqueous zinc-ion batteries has been explored through the advanced synchrotron-based two-dimensional x-ray fluorescence technique. Thanks to the two-dimension modality, not only the metal concentration dynamics throughout the entire electrodes was followed during the aging process, but their spatial distribution was also revealed, suggesting the route of the material transformation. The dissolution of Mn and Ni, and the penetration of Zn inside the framework, alongside the Mn aggregations outside the hexacyanoferrate framework were detected. Additionally, the possibility of conducting x-ray absorption spectroscopy measurements on the regions of interest made it possible to explore the chemical state of each metal, and furthermore, synchrotron-based powder x-ray diffraction demonstrated the gradual structural modification in 30% Ni-containing sample series, in terms of the different phase formation.

This paper addresses the study of synoptic-scale meteorological conditions that favor long-range pollen transport in southern South America combining airborne pollen counts, modeled three-dimensional backward trajectories, and synoptic and surface meteorological data. Alnus pollen transport trajectories indicate origins predominantly in Montane forests of the Yungas between 1500 and 2800 m altitude. The South American Low-Level Jet is the main meteorological feature that explains 64\% of the detected pollen arrival at the target site. Podocarpus and Nothofagus pollen instead are linked primarily to the widespread Subantartic forests in southern Patagonia. Their transport patterns are consistent with previous studies which show an association with synoptic patterns related to cold front passages carrying pollen in the free atmosphere (27% for Nothofagus and 25% for Podocarpus). These results show the significance of understanding long-distance pollen transport for disciplines such as climate change reconstruction and agriculture, emphasizing the need for further research to refine atmospheric circulation models and refine interpretations of past vegetation and climate dynamics.

Stingless bee beekeeping provides new opportunities to improve the incomes of many households in Malaysia through the sale of honey and other bee products. While Heterotrigona itama is one of the most commonly cultured species of stingless bees, its behavior is not very well understood. Hence, we conducted this study to investigate the behavior of H. itama in exploiting food sources by ascertaining the nectar sugar concentration preferred by the bee. We also aimed to determine the preferred distance of food source from the bee hive. Our results suggest that H. itama prefers high sugar concentrations of 35% and above, and they would fly up to 7 m from the hive to collect food. We discuss how nectar concentration and food distance influence the number of bees exploiting food sources and the overall foraging pattern of H. itama.

Abstract: Objective: The aim of this study is to evaluate the effectiveness of an affective-sexual education programme among 14-15 year-old adolescents and its impact on their perception of their health. Design: This is a quantitative, descriptive and prospective quasi-experimental study, employing various cross-sections throughout the school year. It constitutes an educational promotion intervention where a health blog serves as a supplementary component to the "Health and School" programme. This phase is assessed using specific tools designed to evaluate scales. The study aims to assess the programme's effectiveness in the areas of knowledge and attitudes related to sexuality, sexually transmitted diseases, and affectivity. Additionally, it aims to evaluate the intervention's impact on 292 secondary school students, divided into a control group (N=144) and an intervention group (N=148). Both groups belong to the same population and have undergone affective-sexual education interventions focusing on gender and sexual diversity perspectives. The assessment considers perceptions and behaviours related to knowledge about HIV and other STDs/STIs, attitudes towards HIV, health anxiety, and AIDS phobia. Cross-sectional comparisons between the control and intervention groups are conducted at three points during the school year: longitudinal study, baseline, at month 4, and at month 9 post-intervention. Methods: Median and interquartile range, Wilcoxon test for numerical variables and Fisher's test for categorical variables were calculated according to intervention group. Results: 53% of the students reported a very good perception of health, compared to 45% with a good perception. Boys reported a good perception of health (85%) and had perceptions of their physical appearance and health (p=0.023) and (p<0.001) in the intervention group. Differences were found between perceptions of physical appearance in the control group, which was close to normal (p=0.429). In terms of level of knowledge about HIV and other STDs/STIs, the most significant difference was found in knowledge about HIV transmission, which varied between groups. Conclusions: Early affective-sex education sessions have an impact on health perception and behaviours related to knowledge about HIV and other STDs/STIs, leading to reduced anxiety in adolescents. School nurses should focus on health promotion, prevention of sex-related diseases, healthy attitudes from a gender perspective and integrated affective relationships in all their dimensions.

The advancement of computational drug discovery and design necessitates continuous innovation to enhance the accuracy and scope of predictive models for early-stage drug research and development. This article introduces a novel workflow for in silico generation of structural and intermolecular binding affinity data with reasonable accuracy, combining two computational tools: Modeller and Prodigy. By leveraging synthetic structural and biophysical data, this approach addresses the limitations of existing experimental datasets, generating extensive, high-quantity binding affinity data with reasonable accuracy for biomolecular binding pairs, which broadens the horizon of computational biomolecule design and discovery by enabling extensive exploration of the sequence space of biomolecular binding pairs, and narrows the gap between experimental binding affinity data and its unexplored territories. Overall, this article presents a methodological advance to enhance the accuracy and scope of computational biomolecule discovery and design, paves the way for the development of preclinical candidates with improved efficacy and specificity, and holds transformative potential for further advancements in artificial intelligence-enabled biomolecule discovery and design in the future.

The potential effect of implementing environmental regulations on economic growth is a controversial issue for a long time. As portrayed by Porter hypothesis, environmental regulations may impact on economic growth by reconstructing the economic structure. A number of previous literature proved the connections between the above three parties. With the intention of exploring the nature of this mechanism, it is important to understand the internal inter-relation of the three parties, requiring data from a large economy experiencing the transition in economic structure. By constructing a panel containing data from 30 Chinese provinces over 10 years (2008-2019), this paper investigate the dynamic inter-relation of the three parties by introducing PVAR model with Granger Test. Results reveal that environmental regulations give a “U” shaped impact to economic growth. In return, economic growth promotes the development of economy with a weakening strength. In addition, results also support the hypothesis that economic structure is the intermediate of economic growth and environmental regulations. There is a rooftop for the effect of economic structure to environmental regulations. The rooftop may signify the best optimization of primary structure.

Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed 5 different machine-learning (ML) and deep-learning (DL) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML and DL algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are constructed based on different combinations of VIIRS pixel and predictor variables. The validation and comparison results based on the collocated CALIOP data indicates that the FFNN method based on all available predictor variables is the best performing one among all methods. It has an averaged dust detection accuracy of about 81 %, 89 % and 85 % over land, ocean and whole globe, respectively, compared with collocated CALIOP. When applied to off-track VIIRS pixels, the FFNN method retrieves geographical distributions of dust that are in good agreement with on-track results as well as CALIOP statistics. For further evaluation, we compared our results based on the ML and DL algorithms to NOAA’s Aerosol Detection Product (ADP) , which is a product that classifies dust, smoke and ash using physical-based methods. The comparison reveals both similarity and differences. Overall, this study demonstrates the great potential of ML and DL methods for dust detection and proves that these methods can be trained on the CALIOP track and then applied to the whole granule of VIIRS granule.

NASA’s ICESat-2space-borne photon-counting lidar mission is providing global elevation measurements that will provide significant benefits to a variety of bio-geoscience research applications. Given the novelty of elevation and the derived data products from the ICESat-2 mission, the research community needs software tools that can facilitate photon-level analyses to support product validation and development new analysis methods. Here, we describe PhotonLabeler, a free graphic user interface (GUI) for manual labeling and visualization of ICESat-2 Geolocated Photon data (ATL03). Developed in MATLAB, the GUI facilitates the reading and display of ATL03 Hierarchical Data Format (HDF) files, the manual labeling of individual photons into target classes of choice using a number of point selections tools and enables eventual saving of labeled data in ASCII format. Other capabilities include saving and loading of labeling sessions to manage labeling tasks over time. We expect labeled data generated using the application to serve two main purposes. First, serve as ground truth for validating various products from ICESat-2 mission, especially for study sites around the world that do not have existing reference datasets such as airborne lidar. Second, serve as training and validation data in the development of new algorithms for generating various ICESat-2 data products. We demonstrate the first use case through a validation case study for the land and vegetation product (ATL08), which provides canopy and terrain height estimates, over two sites. For the first site, located in northwestern Zambia, we used ICESat-2 ATL03 data acquired at night and for our second site in Texas, US, we used ATL03 data acquired during the day. The PhotonLabeler application is freely available as a compiled MATLAB binary to enable free access and utilization by interested researchers.

Microplastics (plastic particles &lt; 5 mm) are ubiquitous pollutants that pose the ability to carry microbiota, including pathogens. Microbial adhesion is usually a sign of pathogenicity, thus we investigated the adherent strains found on 4 mm nylon strips, which were ingested and excreted by wild fish specimens. Polymer analysis of the excreted samples shown no signs of degradation, nor have their controls, represented by the nylon strips submerged in the same water tanks. Both the ingested samples and controls presented pathogens in large numbers, revealing the fact that nylon microplastics can be physical carriers for pathogens in the aquatic environment.

Distance learning has assumed a relevant role in the Educational scenario. The use of Virtual Learning Environments contributes to obtain a substantial amount of educational data. In this sense, the analyzed data generate knowledge used by institutions to assist managers and professors in strategic planning and teaching. The discovery of students’ behaviors enables a wide variety of intelligent services for assisting in the learning process. This article presents a literature review in order to identify the intelligent services applied in distance learning. The research covers the period from January 2010 to May 2021. The initial search found 1,316 articles, among which 51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems or models, and 6% (3/51) use assessment tools. This review allowed to observe that the principal services offered are recommendation systems and learning systems. In these services, the analysis of student profiles stands out to identify patterns of behavior, detect low performance and identify probabilities of dropouts from courses.