This paper provides a synthesis of qualitative studies, examining youth empowerment projects and initiatives which have encouraged young people to have a voice in local, regional and national political debates. Specifically, the article examines the role of UK youth services in building the spirit of citizenship in young people against the challenging question of the changing behaviour pattern and profiles of young British electorates. To do this, the paper draws on four case studies to help rethink the critical moments for disadvantaged and vulnerable young people in their journeys towards citizenship. The article, presents the advantages and limitations of the youth sector to enrich and furnish the spirit of citizenship in today’s youth and argues for a more innovative role in the part played by the state in an era of austerity.

Mining and analysis of the Big Data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of Tweets related to Ebola, E-Coli, Dengue, Human papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both these viruses. While there have been a few works published in the last few months that focused on performing sentiment analysis of Tweets related to either COVID-19 or MPox, none of the prior works in this field thus far involved analysis of Tweets focusing on both COVID-19 and MPox at the same time. With an aim to address this research gap, a total of 61,862 Tweets that focused on Mpox and COVID-19 simultaneously, posted between May 7, 2022, to March 3, 2023, were studied to perform sentiment analysis and text analysis. The findings of this study are manifold. First, the results of sentiment analysis show that almost half the Tweets (the actual percentage is 46.88%) had a negative sentiment. It was followed by Tweets that had a positive sentiment (31.97%) and Tweets that had a neutral sentiment (21.14%). Second, this paper presents the top 50 hashtags that were used in these Tweets. Third, it presents the top 100 most frequently used words that are featured in these Tweets. The findings of text analysis show that some of the commonly used words involved directly referring to either or both viruses. In addition to this, the presence of words such as "Polio", "Biden", "Ukraine", "HIV", "climate", and "Ebola" in the list of the top 100 most frequent words indicate that topics of conversations on Twitter in the context of COVID-19 and MPox also included a high level of interest related to other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that involves a comparison of this work with 49 prior works in this field is presented to uphold the scientific contributions and relevance of the same.

Background: The resurgence of syphilis, primarily in MSM populations, has necessitated novel prophylactic strategies, such as the use of doxycycline post-exposure prophylaxis (doxy-PEP). However, the potential for increased doxycycline use to select for tetracycline resistance represents significant challenges in managing this sexually transmitted infection. This study aims to identify chromosomal mutations associated with tetracycline resistance in Spirochaetales to inform treatment strategies and surveillance measures.Methods: Whole genome sequences (WGS) from the Spirochaetales order, including 4,355 genomes, were analyzed for the presence of mutations in 16S rRNA and non-synonymous mutations in the rpsC and rpsJ genes. The study utilized WGS from GenBank® and sequence data from the PubMLST Treponema pallidum isolate collection. Genetic resistance to tetracycline was detected using a combination of BLASTN searches and gene-gene analysis. Results: A transition mutation TGA to TGG at positions 965-967 in the 16S rRNA gene was detected in 5.6% of Treponema spp. and 3.97% of Spirochaeta spp. genomes. The rpsJ gene exhibited a V57G amino acid substitution across a significant subset of Treponema spp. Notably, the V57K mutation was common in Spirochaeta spp. The rpsC gene had the H178Q mutation and was found to be present in the Spirochaetales bacterium.Conclusion: The identification of mutations associated with tetracycline resistance in Spirochaetales provides a foundation for the development of rapid molecular diagnostics and guides the clinical selection of antibiotics for syphilis treatment. This study underscores the complexity of antibiotic resistance mechanisms and the critical importance of surveillance in the era of antibiotic prophylaxis for STI management.

Ant rescue behaviour belongs to the most interesting subcategories of prosocial and altruistic behaviour encountered in the animal world. Several studies suggested that ants are able to identify what exactly restrains the movements of another individual and to direct their rescue behaviour precisely to that object. To shed more light on the question how precise is the identifi-cation of the source of restraint of another ant, we investigated rescue behaviour of workers of the red wood ant Formica polyctena using a new version of an artificial snare bioassay in which a nestmate victim was bearing on its body two wire loops, one placed on the petiole and acting as a snare, and an additional one on the leg. The tested ants did not direct preferentially their rescue behaviour to the snare. Moreover, the overall strategy adopted by the most active rescuers was not limited to precisely targeted rescue attempts directed to the snare, but consisted of attempts to find a solution to the victim’s problem through frequent trial-and-error switching between various subcategories of rescue behaviour. These findings highlight the importance of precise identifica-tion of cognitive processes and overall behavioural strategies for better understanding of causal factors underlying animal helping behaviour.

Sepsis-associated encephalopathy (SAE) is a common brain dysfunction following sepsis that often results in severe cognitive and neurological sequelae and increases the mortality rate in patients with sepsis. Microglia are resident macrophages in the brain that play essential roles in the pathological and physiological processes of SAE. Depending on the nature of the stimulus, microglia can adopt two polarization states (M1/M2), which correspond to altered microglial morphology, gene expression, and function. Therefore, we systematically described the pathogenesis, morphology, function, and phenotype of microglial activation in SAE. We demonstrated that microglia are closely related to occurrence and development of SAE and concomitant cognitive impairement. Finally, we outlined some potential therapeutic approaches that can prime microglia and neuroinflammation towards the beneficial restorative microglial phenotype in SAE.

Transcutaneous electrical stimulation of the spinal cord is used to restore locomotion and body weight support in patients with severe motor disorders. We studied effects of this non-invasive stimulation on postural control in healthy subjects. Stimulation at the L1-L2 vertebrae was performed to activate the extensor muscles of the lower limbs. Because postural regulation depends on the cognitive style, the effects of the stimulation were analyzed separately in field-dependent (FD) and field-independent (FI) participants. During the study young adults (N=16, half FD and half FI participants, all right dominant leg) stood on a force platform in a soundproof chamber with their eyes closed. Stimulation was applied in the midline between the L1-L2 vertebrae or over the left or right dorsal roots of the spinal cord; under control condition there was no stimulation. Stimulation destabilized posture in healthy subjects, whereas patients with movement disorders usually showed an improvement in postural control. In FD participants, left dorsal root and midline stimulation increased several postural parameters by up to 30%. Dorsal root stimulation on the side of the supporting leg reduced postural control, while stimulation on the side of the dominant leg did not. No significant changes were observed in the FI participants.

Background and Aims. Most research on Parkinson's disease (PD) focuses on describing symp-toms and movement characteristics. Studies rarely focus on the early detection of PD and the search for suitable markers of a prodromal stage. Early detection is important, so treatments that may potentially change the course of the disease can be attempted early on. While gait disturb-ances are less pronounced in the early stages of the disease, the prevalence, and severity increase with disease progression. Therefore, postural instability and gait difficulties could be identified as sensitive biomarkers. The aim was to evaluate the discriminatory power of the Trail-Walking Test (Schott, 2015) as a potential diagnostic instrument to improve the predictive power of the clinical evaluation concerning the severity of the disease and record the different aspects of walking. Methods. 20 older healthy (M = 72.4 years, SD = 5.53) adults and 46 older adults with PD and the motor phenotypes postural instability/gait difficulty (PIGD; M = 69.7 years, SD = 8.68) and tremor dominant (TD; M = 68.2 years, SD = 8.94) participated in the study. The participants performed a motor-cognitive dual task (DT) of increasing cognitive difficulty in which they had to walk a given path (condition 1), walk to numbers in ascending order (condition 2), and walk to numbers and letters alternately and in ascending order (condition 3). Results. With an increase in the cognitive load, the time to complete the tasks (seconds) become longer in all groups, F(1.23, 73.5) = 121, p < .001, ɳ2p = .670. PD-PIGD shows the longest times in all conditions of the TWT, F(2, 60) = 8.15, p < .001, ɳ2p = .214. Mutual interferences in the cognitive and motor domain can be observed. How-ever, clear group-specific patterns cannot be identified. A differentiation between the motor phenotypes of PD is especially feasible with the purely motor condition (TWT-M; AUC = . 685, p = 0.44). Conclusions. PD patients with PIGD must be identified by valid, well-evaluated clinical tests that allow a precise assessment of the disease's individual fall risk, the severity of the dis-ease, and the prognosis of progression. The TWT covers various aspects of mobility, examines the relationship between cognitive functions and walking, and enables differentiation of the motor phenotypes of PD.

This report is a narrative of a certified nurse working on a long-term rehabilitation ward for patients with dementia in Japan during the early phase of the COVID-19 pandemic. During this time visitation restrictions had been implemented to prevent the spread of COVID-19 causing psychological distress for patients and their families which nurses had to cope with . The nurse was interviewed twice September–October 2020. The recordings were transcribed verbatim and analysed thematically. Three themes were identified relating to changes in care in response to the pandemic which nurses had to adapt to: the risk of collapse of family members’ roles, anxiety caused by patients forgetting family members and family memories and increased disorientation. During the pandemic, nursing care needs to adapt, ensuring that family attachments and ties continue and minimizing the disruption caused by the pandemic, while ensuring that everyone remains Covid-safe.

The current study investigated whether a domain-specific intervention targeting maths and executive functions of primary school children with a Roma background would be effective in improving their scholastic performance and executive functioning. In total, 122 students attending Grade 4 of elementary school took part in the project. The study concerned a pretest-intervention-training experimental design with three conditions: the experimental condition, an active and a passive control group. The results suggested that both maths performance and executive functions improved over time, with no significant differences between the three conditions. An additional correlational analysis indicated that pretest performance was not related to posttest performance for the children in the experimental and active control group.

In open, sustainable policymaking, we are expecting to develop the valuable results that will bring us closer to a fairer and more balanced society. One way to involve the public in these processes is to engage them in online e-participation projects. Despite the hopes, empirical analyses show that many e-participation initiatives have failed to deliver expected benefits. Revealing what actually works in examined projects and what requires improvement would allow for better policy planning in the future. In this article, I made an attempt to identify and assess the cognitive processes enabling emergence of collective intelligence (CI) in a singular e-participation project. For this reason, I worked out and tested an evaluation technique, combining the MILCS framework for group cognition and the results of empirical research on CI. A case study method based on semi-structured interviews was selected to evaluate a sample participatory budgeting initiative, Civic Budget of the City of Kraków. Results reveal that most cognitive processes are working satisfactorily, but the real problem is using collective memory, which works only to a very limited extent. A guideline for future policymakers should be to develop a shared memory system, to which all community members should have access.

The next generation of wireless and mobile networks will have to handle a significant increase in traffic load compared to the actual one. This situation calls for novel ways to increase spectral efficiency. Therefore in this paper, we propose a wireless spectrum hypervisor architecture that abstracts a radio frequency (RF) front-end into a configurable number of virtual RF front-ends. The proposed architecture has the ability to enable flexible spectrum access in existing wireless and mobile networks, which is a challenging task due to the limited spectrum programmability, $i.e.$, the capability a system has to change the spectral properties of a given signal to fit an arbitrary frequency allocation. The main goal of the proposed approach is to improve spectral efficiency by efficiently using vacant gaps in congested spectrum-bandwidths or adopting network densification through infrastructure sharing. We demonstrate mathematically how our proposed approach works and present several simulation results proving its functionality and efficiency. Additionally, we designed and implemented an open-source and free proof of concept prototype of the proposed architecture, which can be used by researchers and developers to run experiments or extend the concept to other applications. We present several experimental results used to validate the proposed prototype. We demonstrate that the prototype can easily handle up to 12 concurrent physical layers.

SerIoT is a Research and Innovation Project funded by the European Commission to demonstrate secure networks for the Internet of Things (IoT). This paper presents an overview of the design for smart and secure network infrastructures for the IoT, based on Software Defined Networks (SDN), named the SerCPN system, whose main component is a smart SDN-Controller with online cognitive security surveillance and reporting, which establishes and dynamically modifies paths that enhance security for IoT devices and end users, and offers a high quality of service (QoS) with required security constraints. This online cognitive surveillance and path management is based on the Cognitive Packet Network (CPN) principle and uses Random Neural Networks (RNN) for decision making.

This study assessed the perception and use of digital applications for soil fertility management and conservation strategies among small-scale crop farmers in southwest Nigeria. A total of 376 farmers were randomly selected across the six southwest states. The data collected were analyzed using descriptive statistics and Logit regression. Majority of the farmers relied on perception and other non-conventional approaches such as the appearance of weeds and performance of crops in the previous season to assess soil fertility. Only 1.1% and 0.3% of the farmers assessed soil fertility through soil test and digital application respectively. Most farmers adopted bush fallowing and the use of inorganic fertilizer to improve soil fertility. Although, 4.8% of the farmers indicated that they had digital application on their mobile phones, only 2.9% reported to have used such. More than half (56.4%) of the famers stated that lack of awareness of the existence of digital application and internet enabled telephones were reasons they have not been able to use digital applications. Majority of the farmers (97.3%) indicated their willingness to embrace the use of new farm decision digital application which can provide more information especially on soil fertility if introduced. Logit model results revealed that literacy level, membership of farmers’ association and extension contact significantly increased the likelihood of willing to use soil digital application, but older farmers are less likely to use digital application.

This work presents an application called APPMAR 1.0 based on Python ® environment, built to perform the downloading, treatment and analysis of meteorological and marine information. This application is composed of two main modules: the first module allows the downloading of information from the database (NOAA - WW3); the second module uses the principles of statistical mathematics for the treatment of waves and wind. The importance of this simple application is based on the free and agile access to meteorological and marine information for a coastal project. The determination of representative conditions of sea states ultimately will govern the process of design of coastal and oceanic infrastructure. The analysis of historical time series of local waves and winds allows the evaluation of average regimes or operational design, the ultimate limit states or extreme design, and the storms or design by persistence. In spite that the former analysis is a common task for coastal engineers, the codes generated are seldom shared for public use. In summary, for operational purposes is useful to have a freeware that can assist in the data processing for decision making and forcing of the mathematical models that are part of the common practice of coastal, oceanic and offshore engineering. This application has been tested in the Caribbean area of Colombia where meteorological and marine information are scarce.

We introduce poly-symplectic extension of Souriau Lie groups Thermodynamics based on higher-order model of statistical physics introduced by R.S. Ingarden. This extended model could be used for small data analytics and Machine Learning on Lie groups. Souriau Geometric Theory of Heat is well adapted to describe density of probability (Maximum Entropy Gibbs density) of data living on groups or on homogeneous manifolds. For Small Data Analytics (Rarified Gases , sparse statistical survey,…), density of maximum entropy should consider Higher Order Moments constraints (Gibbs density is not only defined by first moment but fluctuations request 2nd order and higher moments) as introduced by R.S. Ingarden. We use Poly-sympletic model introduced by Christian Günther, replacing the symplectic form by a vector-valued form. The poly-symplectic approach generalizes the Noether theorem, the existence of momentum mappings, the Lie algebra structure of the space of currents, the (non-)equivariant cohomology and the classification of G-homogeneous systems. The formalism is covariant, i.e. no special coordinates or coordinate systems on the parameter space are used to construct the Hamiltonian equations. We underline the contextures of these models, and the process to build these generic structures.

In recent years, the production of renewable energy represents one of the main challenges of modern and future society. The new cultivation method called Agrivoltaic (hereafter AV), combines and associates agricultural production and renewable energy (photovoltaic) production in a collaborative synergy from which both benefit. In this study, the dry matter and essential oils yield of six medicinal species (Salvia officinalis L., Origanum vulgare L., Rosmarinus officinalis L., Lavendula angustifolia L., Thymus citriodorus L. and Mentha spicata L.) grown underneath a dynamic AV system in comparison to those in full sun (T), were evaluated. In particular, below the dynamic AV system, due to the rotation of the panels, two plot areas on the ground were distinguished: one always covered by the panels (UP), and another one covered alternatively (BP). During the aromatic crop cycles, the solar radiation, infrared leaf temperature and air temperatures in UP, BP and T plot, were measured. In addition, at harvest time, weed survey was carried out. The results showed that, in general, the solar radiation and infrared leaf temperature were lower in UP plot than in either the BP and T plots, while the air temperatures were similar in all compared plots. Also the weed cover percentage was lower in the UP plot than other ones. The essential oils yield in the sage, thyme, mint and rosemary plants were higher in both UP and BP plots than for T plot. Thus, shading and/or less weed infestation may have been the main factors favorably influencing the performance of medicinal crops under a dynamic AV system.

Musyakalah is one of the Arabic linguistic styles included under the category of majaz. This style is commonly used in Al-Quran. The Indonesian translation of Al-Quran is a case where many of the figures of speech are translated literally, thereby causing serious semantic problems. Thus, the research problem of this is formulated with the following questions: 1) How many musyakalah ayahs are there in Al-Quran?; 2) How are the musyakalah ayahs translated, literally (harfiyya) or interpretatively (tafsiriyya)?; 3) How many ayahs are translated literally and how many are translated interpretatively?; and 4) Which translated musyakalah ayahs have the potential to raise semantic and theological problems? The corpus in this research consists of all musyakalah ayahs in Al-Quran and their translation to Indonesian published by the Department of Religious Affairs of Indonesia. The research adopted a descriptive-semantic method. The findings of this research are: 1) There are only eleven ayahs in Al-Quran using musyakalah style, namely: Alhasyr ayah 19, Ali Imran ayah 54, Annaml ayah 50, Alanfal ayah 30, Asysyura ayah 40, Albaqarah ayah 15, Almaidah 116, Aljatsiah ayah 34, Attaubah ayah 79, Annisa ayah 142, and Albaqarah 194; 2) The musyakalah ayahs translated literally are: Aljatsiah 34, Almaidah 116, Asysura 40, Annaml 50, and Alhasyr 19, whereas the musyakalah ayahs translated interpretatively are Albaqarah 194, Annisa 142, Attaubah 79, Albaqarah 15, Alanfal 30, and Ali Imran 54; 3) Of the eleven musyakalah ayahs, only Alhasyr ayah 19 that is translated correctly and does not have the potential of creating misinterpretation. Meanwhile, the interpretation of the other four ayahs can potentially cause misinterpretation or are against the basic principles of Islam. The six remaining ayahs are translated interpretatively and thus do not have the potential to be misinterpreted; 4) The findings suggest that musyakalah ayahs are more appropriately translated interpretatively. Therefore, the following is recommended: 1) Considering the different characteristics of Arabic and Indonesian languages, studies on Al-Quran translation into Indonesian should continuously be carried out; 2) In order to avoid misinterpretation, it is better that the translation of musyakalah ayahs uses tafsiriyya (interpretative) approach; 3) The harfiyya (literal) and tafsiriyya (interpretative) approaches should be developed for other styles beside musyakalah..

Immunosenescence refers to age-related alterations of the immune system function affecting both the humoral and cellular arm of immunity. Understanding immunosenescence and its impact on vaccination of older adults is essential since primary vaccine responses in older individuals can fail to generate complete protection, especially vaccines targeting infections with increased incidence among the elderly, such as the respiratory syncytial virus. Here, we review the clinical trials of both candidate and approved vaccines against respiratory syncytial virus (RSV), which include adults aged ≥50 years, with an emphasis on the evaluation of immunogenicity parameters. Currently, there are 10 vaccine candidates and 2 vaccines approved for the prevention of RSV in the older adult population. The number of registered clinical trials for this age group amounts to 32. Our preliminary evaluation of published results and interim analyses of the RSV vaccine clinical trials indicates efficacy in older adult participants, demonstrating immunity levels that closely resemble those of younger adult participants.

To date, various chimeric lysins have been developed as efficacious antibiotics against multidrug-resistant bacteria, but direct comparisons of their antibacterial activities have been difficult due to the preparation of multiple recombinant chimeric lysins. Previously, we reported an E. coli cell-free expression method to screen better chimeric lysins against Staphylococcus aureus, but we still needed to increase the amounts of expressed proteins enough to be able to detect them non-isotopically for quantity comparisons. In this study, we improved the previous cell-free expression system by adding a previously reported artificial T7 terminator and reversing the different nucleotides between the T7 promoter and start codon to those of the T7 phage. The new method increased the expressed amount of chimeric lysins enough for us to detect them using Western blotting. Therefore, the qualitative comparison of activity between different chimeric lysins has become possible via the adjustment of the amount variables between samples without protein purification. We applied it to select more active chimeric lysins derived from our previously reported chimeric lysin (ALS2). Finally, we compared the antibacterial activities of our selected chimeric lysins with reported chimeric lysins (ClyC and ClyO) and lysostaphin, and determined the rank orders of antibacterial activities on different S. aureus strains in our experimental conditions.

The Earth’s synthetic gravitational and density models can be used to validate numerical procedures applied for a global (or large-scale regional) gravimetric forward and inverse modelling. Since the Earth’s lithospheric structure is better constrained by tomographic surveys than a deep mantle, most of existing 3-D density models describe only a lithospheric density structure, while 1-D density models are typically used to describe a deep mantle density structure below the lithosphere-asthenosphere boundary. The accuracy of currently available lithospheric density models is examined in this study. The error analysis is established to assess the accuracy of modelling the sub-lithospheric mantle geoid, while focusing on the largest errors (according to our estimates) that are attributed to lithospheric thickness and lithospheric mantle density uncertainties. Since a forward modelling of the sub-lithospheric mantle geoid comprises also numerical procedures of adding and subtracting gravitational contributions of similar density structures, the error propagation is derived for actual rather than random errors (that are described by the Gauss’ error propagation law). Possible systematic errors then either lessen or sum up after applying particular corrections to a geoidal geometry that are attributed to individual lithospheric density structures (such as sediments) or density interfaces (such as a Moho density contrast). The analysis indicates that errors in modelling of the sub-lithospheric mantle geoid attributed to lithospheric thickness and lithospheric mantle density uncertainties could reach several hundreds of meters, particularly at locations with the largest lithospheric thickness under cratonic formations. This numerical finding is important for a calibration and further development of synthetic density models of which mass equals the Earth’s total mass (excluding the atmosphere). Consequently, the (long-to-medium wavelength) gravitational field generated by a synthetic density model should closely agree with the Earth’s gravitational field.

In new Guideline for the Management of Patients with Valvular Heart Disease for tricuspid regurgitation (TR) is focused the importance of measurement of right-sided diastolic pressures and right ventriculography, pulmonary artery pressures, and pulmonary vascular resistance. Assessment of right ventricular (RV) dimensions and function is pivotal to select patients with severe tricuspid regurgitation who may benefit from tricuspid valve intervention. However, with the exception of tricuspid valve annulus diameter, there is no other cut-off value that defines severe RV dysfunction or dilation for this specific group of patients. It may be difficult, if not impossible, to establish a cut-off value of RV dysfunction since the RV may be exposed to pressure and volume overload, myocardial ischemia, intrinsic myocardial disease or pericardial constraint. Furthermore, the response of the RV to tricuspid valve intervention may vary according to each aetiology and the time course of the disease (acute or chronic). Comprehensive assessment of RV function should address in shape remodelling; functional remodelling; tissue remodelling. This assessment should be performed in optimal loading conditions and after euvolemia has been achieved. It’s now that there is no other cut-off value that defines severe RV dysfunction or dilation for patients with severe TR with the exception of tricuspid valve annulus diameter. It may be difficult, if not impossible, to establish a cut-off value of RV dysfunction since the RV may be exposed to pressure and volume overload, myocardial ischemia, intrinsic myocardial disease or pericardial constraint. In this review we analyze the relationship between right ventricular (RV) and TR in term of outcomes and mortality predictor in patients undergoing percutaneous or invasive treatment for severe TR.

Graphene quality for use in biosensors was assessed in fabricated chips by a set of methods that includes atomic force microscopy (AFM), Raman spectroscopy, and low-frequency noise. It is shown that local areas of residues on the graphene surface, which arisen due to the interaction of graphene with a photoresist at the initial stage of chips development, leads to a spread of chips resistance (R) within 1-10 kΩ and to an increase in the root mean square (RMS) roughness up to 10 times that can significantly impair reproducibility of graphene parameters in biosensors chips. It was observed that the control of the photoresist residues after photolithography (PLG) by AFM and subsequent additional cleaning allow reducing the spread of R values in chips to 1–1.6 kΩ and obtaining RMS roughness similar to the roughness in pristine graphene before PLG. Monitoring of the spectral density of low-frequency voltage fluctuation (SU), which provides integral information about the defect system and quality of the material, makes it possible to identify chips with low graphene quality and with inhomogeneously distributed compressive stresses areas by the type of frequency dependence SU (f).

The environmental safety of a car is currently one of the most important indicators of the vehicle competitiveness and quality in the consumer market. Currently, the assessment of the ecological properties of vehicles can be made based on various criteria. In the case of combustion-powered cars, the most attention is usually paid to the values characterizing their use, and in the environmental assessment, first of all to pollutant emissions and operational fuel consumption. The proposed article considers the possibility of using the life cycle assessment to analyze the ecological properties of a passenger car during its operation. A simplified LCA method of the vehicle was presented, which in strictly defined cases can be used for the analysis of environmental impacts and the assessment of the energy analysis related to its operation. For this purpose, a vehicle life cycle model was developed. Data on the operation of 33 passenger cars of different manufacturers with similar operational characteristics, coming from different production periods, were analyzed in detail. The obtained results were found to be highly sensitive to the assumptions made in the article.

Keenjhar Lake is the main source of drinking water for the metropolitan city of Karachi. The release of untreated wastewater from Kotri industrial area and other sources have made the lake water polluted. This study was subjected to determine the impacts of such pollutant sources on the water quality of Keenjhar Lake. The study involves the analysis of water quality parameters of Keenjhar Lake and its Feeding source (KB Feeder). The sampling sites were selected based on the sources of contamination. The water samples are tested for physical, chemical and microbiological parameters. The result of water analysis indicates the contamination level of the lake is quite alarming for the sites of Kotri effluent and WAPDA colony where Total Dissolved Solids (TDS), Chloride and other ionic metals were quite higher in concentration than other sites. These sites are also contaminated with Fluoride and Arsenic which are carcinogenic elements. The study reveals that the contamination level of feeding source is causing big non-reversible damage to the lake if continued to be uncontrolled. This contamination is mainly due to the release of toxic metals and ions in the KB feeder caused by human carelessness.

The move towards a new Circular Economy (CE) economic model has been advocated and supported in Portugal, however, there is limited research on this topic. To address this gap, a quantitative research based on an online survey was carried out among 99 Portuguese organizations, encompassing a wide range of sectors and sizes. The results show that CE is regarded as a strategic and relevant issue for profitability and value creation. Furthermore, the perception that it requires the adoption of new business models in addition to the classical “reduce, reuse, and recycle” approach is growing. Moreover, based on the hypotheses raised, results suggest that the level of CE adoption is positively impacted by the status of the EMS (Environmental Management System) certification and the willingness to improve the environmental performance and achieve a sustainable business model. However, CE activities are still relatively modest and a friendlier context (fiscal, legal, organizational, etc.) and the stronger support from supply chain agents and consumers are required. Future research should focus on how to design and shape the transition from a linear to a CE economy, and to ascertain if the positive attitude towards CE is materialized in changing the way business is done.

Mirror Therapy (MT) and Cognitive Therapeutic Exercise (CTE) are two neurorehabilitation techniques based on neuroplasticity and designed to enhance the functionality of the upper limb. Therefore, the aim is to assess the effectiveness of the combination of CTE and MT with task-oriented training (TOT) on fine and gross motor skills of the affected hand, as well as the functionality of the affected upper limb, autonomy, independence, and quality of life (QoL) in patients with acute stroke. This longitudinal and multicenter study involved a sample of 120 participants selected randomly and consecutively. Three groups were formed, including a control group (CG) and two treatment groups, one with MT and the other with CTE. The functionality of the upper limb, fine and gross motor skills, the quantity and quality of use of the affected upper limb, autonomy, and QoL were evaluated using various scales validated in the Spanish population at the baseline, after 20 treatment sessions, and at three months of follow-up. ANCOVA analysis was conducted, revealing statistically significant differences between assessments in the CG compared to the experimental groups (GEs), indicating a notable improvement in overall functionality, fine and gross motor skills, autonomy, and QoL in the latter. However, there were no significant differences between the GEs. Therefore, the application of either of these experimental combinations is crucial for the motor recovery, functionality, and improvement of the quality of life of individuals who have suffered a stroke.

The global antibiotic resistance crisis, driven by antibiotic overuse and misuse, is multifaceted. This study aimed to assess the microbiological and genetic characteristics of raw retail pork meat, including the isolation, antibiotic susceptibility testing (AST), whole genome sequencing (WGS) of selected indicator bacteria, antibiotic residue testing, and metagenomic sequencing. Samples were purchased from 10 randomly selected retail stores in Gauteng, South Africa. Samples were aseptically separated, with a portion sent to an external laboratory for isolating indicator bacteria and antibiotic residue testing. Identification of the isolated bacteria was reconfirmed using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). AST was performed using the Microscan Walkaway system. WGS and metagenomic sequencing were performed using the Illumina NextSeq 550 instrument. The isolated E. coli and enterococci exhibited little phenotypic resistance, with WGS revealing tetracycline resistance genes. Isolated bacteria and meat samples all harboured tetracycline resistance genes, and the antibiotic residue concentrations were within acceptable limits for human consumption. In the metagenomic context, most identified bacteria were of food/meat spoilage and environmental origin, with the resistome analysis primarily indicating beta-lactam, tetracycline and multidrug resistance genes. Further research is needed to understand the broader implications of these findings on environmental health and antibiotic resistance.

Introduction: Left ventricular dysfunction in an independent predictor of mortality in Chagas disease. Global longitudinal strain (GLS) is an emerging echocardiographic modality with possible incremental value in early detection of cardiac involvement. Objective: Compare left ventricular function with multiple echocardiographic parameters between patients with the indeterminate and chronic forms of Chagas and perform a literature review. Methods: In this observational study, 11 patients with a confirmed diagnosis of Chagas disease were evaluated. Individuals were distributed as follows: 5 with the indeterminate and 6 with the chronic forms. Conventional echocardiographic evaluation was followed by GLS measurement. Results: The mean left ventricle ejection fraction LVEF was 33 ± 2.88 and the mean GLS of the left ventricle was - 10.76 ± 2.20 in the chronic form as compared to 57.80 ± 6.49 and - 17.34 ± 3.82, respectively in the indeterminate group. Contractility was reduced in segments of the inferior, inferoseptal and anteroseptal walls in both groups. Conclusion: Analysis of longitudinal strain in the indeterminate form has potential incremental value to the conventional analysis of ejection fraction when compared to patients with chronic Chagas cardiomyopathy, especially with individual segmental analysis but the impact and clinical significance of these techniques is still unknown.

During the last two years the COVID-19 pandemic has affected the world population in several ways. An important increase in mental health problems is a consequence of this pandemic that is ubiquitous worldwide. In this work we study the effect of the pandemic on the mental health of a population of teenagers and youth based on the analysis of natural language processing, machine learning algorithms and expert knowledge. The data analysed was obtained from a chat helpline called Safe time from theIt Get’s Better Foundation in Chile. The data consists of 10,986 conversations gathered from 2018 until 2020 between volunteers from the foundation and users of the platform. We compared the conversationsbefore and during the pandemic in terms of their thematic content. Our analysis found: a significantdecrease in self-image appreciation during the pandemic; a significant decrease in the quality of personalrelationships during the pandemic, and a significant increase of performance appreciation.

Rabies persists as a longstanding issue in Zambia, despite being preventable. The current control measures, including dog vaccination, population control, and movement restriction, guided by ‘The Control of Dogs Act Chapter 247 of the Laws of Zambia’, have not yielded the desired impact in many areas of the country including Manyinga and Mwansabombwe districts. These two districts continue to report low dog vaccination rates, unrestricted dog movements, and escalating cases of animal and human rabies, along with dog bites. Aligned with global aspirations to achieve zero human rabies cases by 2030, this study scrutinizes the determinants and obstacles hampering the execution of rabies control initiatives in Manyinga and Mwansabombwe. Spanning approximately 11 months, this cross-sectional study gathered pre- and post-vaccination data from 301 households in Manyinga and 100 households in Mwansabombwe. Questionnaires probed knowledge, attitudes, and practices related to rabies prevention and control. A transect survey, key informant interviews, and assessment of rabies vaccination and dog bite records complemented the data collection. Findings revealed that 88% of respondents from both districts possessed knowledge about rabies, confirming affected species and transmission. Moreover, 76.8% in Manyinga and 88.6% in Mwansabombwe were acquainted with rabies prevention and control methods. Concerning dog owners, 89% were aware of rabies, 66% understood its prevention and control, and the majority identified bites as the primary mode of transmission. Vaccination coverage stood at 64% in Manyinga and 21% in Mwansabombwe. Notably, education and occupation exhibited a positive significant association with rabies knowledge. In terms of dog bite cases, Manyinga recorded 538 dog bite cases from 2017 to June 2022 while Mwansabombwe recorded 81 dog bite and 23 jackal bite cases from 2021 to June 2022. The study underscores critical knowledge gaps in rural areas and emphasizes the imperative for enhanced public education and awareness programs, improved rabies surveillance, free mass vaccination campaigns, and community engagement to augment vaccination coverage and knowledge about rabies.

Nanotechnology has become a popular topic in agricultural and animal research, generating sig-nificant global interest. Nanoparticles (NPs)-enabled agriculture and animal husbandry (AHs) products have three main principles: crop production, protection, and nutrient enrichment. Re-search on this topic has grown exponentially in recent years, with an increasing number of peer-reviewed literature and patents on NPs-enabled agriculture and AH products. This study examines and collects data on the various applications of NPs-enabled agricultural products for crop production, protection, and livestock improvement from the core nanotechnological database. The search returned several types of NPs and products used in agriculture and AH, countries that utilize these products at a commercial level, and analysis of these data by phyton, R-statistical software. Our findings show remarkable growth of nanotechnology in agriculture, AH, and re-search fields for future sustainability goals in the era of climate change

(1) Background: Substantial evidence that neural timing deficits are prevalent in developmental disorders, aging, and concussions resulting from a mild Traumatic Brain Injury (mTBI) is presented. We show that if timing deficits are remediated using low-level movement discrimination training, then high-level cognitive skills, including reading, attention, processing speed, and working memory improve substantially. (2) Methods: Two case studies are presented using MEG source imaging on an adult dyslexic, and a healthy older adult observed before and after training on movement discrimination two times/week for 8 weeks for adult dyslexic. (3) Results: We found improvements in reading, attention, processing speed, and working memory on neuropsychological tests. Substantial MEG signal increases in visual Motion Networks (V1, V3, MT, MST), Attention Networks (ACC, dlPFC, vlPFC and precuneous/ PCC areas) and Memory Networks (dlPFC). (4) Conclusions: Improving neural timing deficits before cognitive exercises to improve specific cognitive skills provides a rapid and effective method to improve cognitive skills. Improving the timing and sensitivity of low-level dorsal pathways, improving feedforward and feedback pathways, is essential to improve high-level cognitive skills. This adaptive training with substantial feedback shows cognitive transfer to tasks not trained on, significantly improving a person’s quality of life rapidly and effectively.

Background: Positive person-centered attributes of inpatient rehabilitation need to be identified from the patient perspective to be further developed and sustained. Purpose: To identify which attributes patients openly evoke as being great care experiences, using an open appreciative inquiry during the inpatient rehabilitation stay. Methods: Qualitative secondary analysis of appreciative patient comments during a bedside patient experience rounding, facilitated by a neutral party. Two independent analysts employed an inductive, summative form of content analysis. Results: Among 150 patients rounded, 122 provided categorizable appreciative accounts. Over two thirds of the patients (67.2%) focused on “staff attributes” in their great-experience accounts. Those attributes were mostly interpersonal such as being “attentive & caring - beyond clinical duty” and being “encouraging (but not too hard) & reassuring”. These interpersonal staff attributes were reported with words showing deep levels of personal significance or patient appreciation. Beyond staff attributes, the perceived quality of the “patient care” (31.1%) and opportunities for “leisure and social activities” (9.0%) were also frequently evoked. Amenities like food or costumer service were the least evoked, rarely so as an exclusive attribute (0.8% for each). Conclusions: The human(e) factor, especially the interpersonal qualities of staff, emerged as greatly appreciated from the patient experience perspective during inpatient rehabilitation. These experiences help identify which person-centered attributes of care might be further developed and sustained.

This comprehensive review digs into the complex landscape of the Metaverse, focusing on user perceptions and the critical roles of usability, social influence, and interoperability. The paper integrates various scholarly insights, underscoring the Metaverse's evolving significance in the digital realm and its implications across diverse sectors. Central to the findings is the importance of usability. The influence of social factors is also prominent, shaping user behavior and decision-making within these virtual spaces. Interoperability becomes a big problem, showing how important it is to have standard protocols and technologies so that experiences on different Metaverse platforms are smooth and seamless. The review further reveals insights into user experience and accessibility, emphasizing the need for inclusive, ergonomic, and user-friendly design to enhance engagement and reduce barriers. Moreover, the paper discusses the ethical and societal implications of the Metaverse, including issues like digital harassment, aggressive marketing, and privacy concerns, which call for responsible development and robust regulatory frameworks. Additionally, the review identifies gaps in current research, particularly in user experience in healthcare applications, the impact of the Metaverse on educational outcomes, and the need for more empirical studies on the long-term effects of Metaverse interactions on user behavior and psychology. This synthesis offers a detailed overview of current user perceptions and challenges within the Metaverse and sets the stage for future research directions, aiming to foster a secure, inclusive, and interoperable Metaverse environment that aligns with ethical standards and societal needs.

This study aimed to compare the levels of physical activity and quality of life reported by Chileans with and without osteoarthritis (OA). Educational level and monthly income data were collected from eighty-two participants (62 women and 20 men), of whom 41 had OA. Concerning the medical information of the persons suffering from OA, diseases, and date of diagnosis were indicated. The International Physical Activity Questionnaire-Short Form (IPAQ-SF) and its Elderly version (IPAQ-E) estimated physical activity levels. The Short Form 36 Questionnaire (SF-36) assessed health-related quality of life. The findings indicate that non-OA subjects scored higher on all dimensions of the SF-36, including physical function, physical role, bodily pain, general health, vitality, social function, emotional role, and mental health. Individuals with OA reported better physical function and role, general health, vitality, social function, and mental health when they walked more. However, no associations were found between any physical activity levels and the degree of pain in people with OA. Our findings underscore the critical role of investigating physical activity in OA patients to enhance their quality of life, particularly in light of the increased sedentary behavior observed post-pandemic.

Using assembly theory, we investigate the assembly pathways of binary strings of length N formed by joining bits present in the assembly pool and the strings that entered the pool as a result of previous joining operations. We show that the string assembly index is bounded from below by the shortest addition chain for N, and we conjecture about the form of the upper bound. We define the degree of causation for the minimum assembly index and show that for certain N it features regularities that can be used to determine a shortest addition chain. We show that a string with the smallest assembly index for N can be assembled by a binary program of length equal to this index if the length of this string is expressible as a product of Fibonacci numbers. We conjecture that there is no binary program that has a length shorter than the length of the string having the largest assembly index for N that could assemble this string. The results confirm that four Planck areas provide a minimum information capacity that corresponds to a minimum thermodynamic (Bekenstein-Hawking) entropy. Knowing that the problem of determining the assembly index is at least NP-complete, we conjecture that this problem is NP-complete, while the problem of creating the string so that it would have a predetermined largest assembly index is NP-hard. The proof of this conjecture would imply P ≠ NP, since every computable problem and every computable solution can be encoded as a finite binary string. Once the new information is assembled by a dissipative structure or by a human, it is subject to the 2^nd law of thermodynamics, and nature seeks to optimize its assembly pathway.

One of the challenges of deploying Wireless Sensor Networks (WSN) at large scale is to operate for prolonged periods of time relying on the limited amounts of energy stored in its nodes. Generally, data communication is the main factor of draining energy. Consequently, reducing the amount of transmitted data by the nodes is of great interest. At the same time, reducing data to be transmitted should not decrease the quality of the deduced information. Solving this problematic is what we call in this article "Ensuring the Quality of Information Service (QoIS)". This document provides a state-of-the-art for the QoIS regarding WSNs. At first, we give a quick overview of the Internet of Things (IoT) paradigm and the WSN technologies in order to understand how data is gathered and used in these networks. Also, we introduce a novel concept: QoIS, in contrast with classic QoS. Then, it is expected a summary of criteria that asses the QoIS in WSNs plus some techniques to improve it. In this article, we will focus on data management techniques. These techniques can improve raw data, information and/or knowledge quality in energy-constrained WSNs. Finally, we give some conclusions and a glimpse about our future work.

The quest to comprehend the nature of consciousness has spurred the development of many theories that seek to explain its underlying mechanisms and account for its neural correlates. In this paper, I compare my own conscious electromagnetic information field (cemi field) theory with integrated information theory and global workspace theory for their ability to ‘carve nature at its joints’ in the sense of predicting the entities, structures, states and dynamics that are conventionally recognized as being conscious or nonconscious. I demonstrate the cemi field theory shares features with both integrated information theory and global workspace theory but consistently outperforms both by correctly predicting the entities, structures, states and dynamics that support consciousness. I argue that the simplest solution to the question of why cemi field theory consistently outperforms rival theories of consciousness is that the brain’s EM field is consciousness.

There is evidence of constitutional rank in Chile, and not only a sectoral rank, to estimate that the regulation of digital media is in an initial and insufficient phase to properly protect information pluralism as political pluralism. The study aims to investigate the forms of concentration, such as the communicational flow of digital media, to determine the opportunities and information defects of these media due to regulatory deficiencies in this sector. Data collection will be carried out through a qualitative and quantitative methodology. The prospect of the imminent constituent process in Chile provides the opportunity to the evaluate the possibilities to propose changes not only at the legislative level but also at the constitutional level, which are likely to benefit freedom of expression with greater guarantees to such as the pluralism of digital media. The latter also means assessing the relevance of enshrining the right to communication in the future.

The previous two parts of the paper (correspondingly, https://philpapers.org/rec/PENFLT-2 and https://philpapers.org/rec/PENFLT-3) demonstrate that the interpretation of Fermat’s last theorem (FLT) in Hilbert arithmetic meant both in a narrow sense and in a wide sense can suggest a proof by induction in Part I and by means of the Kochen - Specker theorem in Part II. The same interpretation can serve also for a proof FLT based on Gleason’s theorem and partly similar to that in Part II. The concept of (probabilistic) measure of a subspace of Hilbert space and especially its uniqueness can be unambiguously linked to that of partial algebra or incommensurability, or interpreted as a relation of the two dual branches of Hilbert arithmetic in a wide sense. The investigation of the last relation allows for FLT and Gleason’s theorem to be equated in a sense, as two dual counterparts, and the former to be inferred from the latter, as well as vice versa under an additional condition relevant to the Gödel incompleteness of arithmetic to set theory. The qubit Hilbert space itself in turn can be interpreted by the unity of FLT and Gleason’s theorem. The proof of such a fundamental result in number theory as FLT by means of Hilbert arithmetic in a wide sense can be generalized to an idea about “quantum number theory”. It is able to research mathematically the origin of Peano arithmetic from Hilbert arithmetic by mediation of the “nonstandard bijection” and its two dual branches inherently linking it to information theory. Then, infinitesimal analysis and its revolutionary application to physics can be also re-realized in that wider context, for example, as an exploration of the way for physical quantity of time (respectively, for time derivative in any temporal process considered in physics) to appear at all. Finally, the result admits a philosophical reflection of how any hierarchy arises or changes itself only thanks to its dual and idempotent counterpart.

A newly-discovered physical mechanism involving electron tunneling in layers of the protein ferritin that are found in catecholaminergic neurons (catecholaminergic neuron electron transport or CNET), is hypothesized to support communication between neurons. Recent tests further confirm that these ferritin layers can also perform a switching function (in addition to providing an electron tunneling mechanism) that could be associated with action selection in those neurons, consistent with earlier predictions based on CNET. While further testing would be needed to confirm the hypothesis that CNET allows groups of neurons to communicate and act as a switch for selecting one of the neurons in the group to assist in reaching action potential, this paper explains how that hypothesized behavior would be consistent with Integrated Information Theory (IIT), one of a number of consciousness theories (CTs). While the sheer number of CTs suggest that any one of them is not sufficient to explain consciousness, this paper demonstrates that CNET can provide a physical substrate that is consistent with IIT and which can also be applied to other CTs, such as to conform them into a single explanation of consciousness.

Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. Based on the mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.

Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. Based on the mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.

The introduction of anti-amyloid monoclonal antibodies against Alzheimer’s disease (AD) is of high importance. However, even though treated patients show very little amyloid pathology, there is only a modest effect on the rate of cognitive decline. Although this effect possibly can increase over time, there is still a need for alternative treatments that will improve the cognitive function in patients with AD. Therefore, the purpose of this study was to characterize the triazinetrione ACD856, a novel pan-Trk positive allosteric modulator, in multiple models to address its neuroprotective and potential disease modifying effects. The pharmacological effect of ACD856 was tested in recombinant cell lines, primary cortical neurons or animals. We demonstrate that ACD856 enhanced NGF-induced neurite outgrowth, increased the levels of the pre-synaptic protein SNAP25 in PC12 cells and increased the degree of phosphorylated TrkB in SH-SY5Y cells. In primary cortical neurons, ACD856 led to increased levels of phospho-ERK1/2, showed neuroprotective effect against amyloid-beta or energy-deprivation induced neurotoxicity, and increased the levels of brain derived neurotrophic factor (BDNF). Consequently, administration of ACD856 resulted in a significant increase of BDNF in the brains of 21-months old mice. Furthermore, repeated administration of ACD856 resulted in a sustained anti-depressant effect which lasted up to seven days, suggesting effects that go beyond merely symptomatic effects. In conclusion, the results confirm ACD856 as a cognitive enhancer, but more importantly, they provide substantial in vitro and in vivo evidence of neuroprotective and long-term effects that contribute to neurotrophic support and increased neuroplasticity. Presumably, the described effects of ACD856 may improve cognition, increase resilience, and promote neurorestorative processes, thereby leading to a healthier brain in patients with AD.

The paper examines the impact of inward remittances flows on per capita GDP growth in Bangladesh during 1976-2012. We find that the growth effect of remittances is negative at first but becomes positive at a later stage, an evidence of a non-linear. Unproductive use of remittances was rampant in the beginning when they were received by migrant families but better social and economic investments led to more productive utilisation of remittances receipts at later periods. This was the possible mechanism behind the U-shaped relationship. Unlike what is suggested in the literature that the effect of remittances is more pronounced in a less financially developed economy, our evidence do not show that the effect of remittances on per capita GDP growth in Bangladesh is conditional on the level of financial development.

The high spatial complexities of soil temperature modelling over semiarid land have challenged the calibration—predication framework, whose composited objective lacks comprehensive evaluation. Therefore, this study, based on the Noah land surface model and its full parameter table, utilizes two global searching algorithms and eight kinds of objective with dimensional—varied metrics, combined with dense site soil moisture and temperature observations of central Tibet, to explore different metrics’ performances on the spatial heterogeneity and uncertainty of regional land surface parameters, calibration efficiency and effectiveness, as well as spatiotemporal complexities in surface forecasting. Results have shown that metrics’ diversity has shown greater influence on the calibration—predication framework than the global searching algorithms themselves. Besides being significantly better than other metrics, the enhanced multi objective metric (EMO) and the enhanced Kling-Gupta efficiency (EKGE) have their own advantages and disadvantages in simulations and parameters respectively. Especially, EMO that composited with four metrics as correlated coefficient, root mean square error, mean absolute error, and Nash–Sutcliffe efficiency, has shown relatively balanced performance in surface forecasting when compared to EKGE. In general, the calibration—predication framework that benefited from EMO could greatly reduce the spatial complexities in soil temperature modelling of the semiarid land.

In a software-defined radar (SDR) system, most of the signal processing usually implemented in hardware is implemented by software, thus allowing for higher flexibility and modularity compared to conventional radar systems. However, the majority of SDR demonstrators and proofs-of-concept reported in the open literature so far have been based on simple antenna systems. As a result, the full potentialities of an SDR approach have not been completely exploited yet. In this work, we propose a flexilbe antenna module to be integrated into an active electronically scanning array (AESA) with controlled sidelobe level over a wide angular range, exhibiting polarization reconfigurability with low cross-polarization level and high isolation. For this purpose, analytical and numerically efficient techniques for the synthesis of the aperture distribution and the correct evaluation of the radiating features (e.g., beamwidth, pointing angle, sidelobe levels, etc.) are presented in order to grant a real-time control of the digital beamforming network. A sub-array module demonstrator is fabricated and measured to corroborate the concept.

This study aimed to evaluate the impact of different closures used in second fermentation on the aromatic fraction of sparkling wine. Six types of closures (cork stoppers and screw cap) and 94 months of aging in bottle were investigated. Head-space-solid phase microextraction (HS-SPME) and thermal desorption (TD) procedures coupled to gas chromatography-mass spectrometry (GCMSMS) analysis was applied. The vectors containing the relative abundance of the volatile compounds are compositional vectors. The statistical analysis of compositional data requires specific techniques that differ from standard techniques.101 volatile compounds were identified. HS-SPME extracted highest percentage of ester, ketones and other compounds while TD was a useful tool for the obtention of alcohol, acid, ether and alkanes compounds. Esters was the most abundant family of compounds. Compositional data analysis applied to study the impact of different closures used in bottle-aging after second fermentation on the volatile composition of sparkling wine concluded that there are differences in the relative abundance of certain volatile compounds between cork stoppers and screw cap closures group. Overall, the most abundant part in screw cap closures was Ethyl hexanoate and, Ethyl octanoate in cork stoppers. Also, the proportion amount of Dimethylamine was higher in screw cap closures than cork stoppers group relative to the entire of the sample.

This study examines the potential of implementing a systematic approach to piloting desalination plants, with the objective of evaluating energy improvements in water desalination processes. The global scarcity of freshwater resources has significantly influenced the development of desalination technologies, with reverse osmosis emerging as one of the most prevalent and highly regarded methods. Pilot tests with the latest generation of reverse osmosis membranes in large desalination plants are becoming increasingly common. This is due to the fact that small-scale experiments are being carried out on the operation of new generation membranes with better salt rejection and lower energy consumption. The objective of this approach is to achieve the optimal water quality standard at the lowest possible cost. In this manner, pilot tests are being conducted prior to the determination of whether to alter the reverse osmosis membranes, as this represents a substantial investment. The objective of these tests is to minimise the risk of making an erroneous decision and to ensure optimal results in terms of energy consumption, operating costs and reduction of environmental impact, while complying with the requisite water quality standards. In this case, the Pareto analysis is employed to identify the two or three most significant causes whose treatment affects more than 80% of the potential energy savings to be implemented. This article introduces a novel approach to studying a total of 180 desalination plants at the territorial level in the Canaries.

Methylmercury (MeHg) intoxication is a global health concern. Although MeHg neurotoxicity is well-recognized, less is known about MeHg's effects on metabolism. In this study, we evaluated MeHg intoxication (20mg/L in drinking water for 20 days) effects on liver and epididymal white fat (EWF) in young ApoE knockout (ko) and wild-type mice. The former is well-known to show spontaneous dyslipidemia. Hg levels in hair, liver, and EWF were measured to assess the degree of MeHg exposure. We also evaluated body weight gain, plasma triglycerides, total cholesterol levels, and liver injury by assessing steatosis score, AST, ALT, SOD, and TBAR levels. To evaluate EWF, we analyzed mean wet weight and adipocyte diameter, plasma leptin levels and metabolomics. Hg levels were markedly higher among intoxicated mice. ApoE ko mice showed higher Hg in the hair but lower levels in the liver and EWF than wild-type controls. Among wild-type mice, MeHg compromised weight gain and increased liver TBAR levels compared to non-challenged controls. Among non-intoxicated mice, ApoE deficiency significantly increased triglyceride, total cholesterol, and liver transaminases with reduced EWF wet weight, hepatic SOD, and plasma leptin levels. MeHg accumulation together with ApoE deficiency elevated total cholesterol, triglycerides, hepatic transaminases, and TBARS. We found distinct EWF metabolite activity in different scenarios of ApoE deficiency and MeHg intoxication, highlighting increased cardiovascular risk especially when both challenges occur. Further studies are required to elucidate the underlying mechanisms involved in these effects and the target nutritional interventions in most need to ameliorate them.

Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models, Patch-generating Land Use Simulation (PLUS) model, and Geodetector within a unified framework to evaluate the dynamics of land use, water yield, and their relationships with various factors (meteorological, social, economic, etc.). To forecast the Land Use/Cover Change (LUCC) pattern of the Yellow River Basin by 2030, three scenarios were considered: economic development priority (Scenario1), ecological development priority(Scenario2), and cropland development priority(Scenario3). Climate change scenarios were constructed using CMIP6 data, representing low stress (SSP119), medium stress (SSP245), and high stress (SSP585) conditions. The results show the following: (1) From 2000 to 2020, cropland was predominant in the Yellow River Basin, Henan Province, with significant land conversion to impervious land (construction land) and forest land; (2) Water yield changes during this period were primarily influenced by meteorological factors, with land use changes having negligible impact; (3) By 2030, the water yield of Scenario1 is highest among different land use scenarios, marginally surpassing ecological development priority by 0.5%; (4) Climate scenarios reveal significant disparities, with SSP126 yielding 67% higher water production than SSP245, driven predominantly by precipitation; (5) Geodetector analysis identifies precipitation as the most influential single factor, with significant interactions among meteorological and socio-economic factors. These findings offer valuable insights for policymakers and researchers in formulating land use and water resource management strategies.

Accurately assessing forest structure and maintaining up-to-date information about forest structure is crucial for various forest planning efforts, including the development of reliable forest plans and assessments of the sustainable management of natural resources. Field measurements traditionally applied to acquire forest inventory information (e.g., basal area, tree volume, and aboveground biomass) are labor-intensive and time-consuming. To address this limitation, remote sensing tech-nology has been widely applied in modeling efforts to help estimate forest inventory information. Among various remotely sensed data, LiDAR can potentially help describe forest structure. This study was conducted to estimate and map forest inventory information across the Talladega Na-tional Forest by employing ALS-derived data and aerial photography. The quality of predictive models was evaluated to determine whether additional remotely sensed data can help improve forest structure estimates. Additionally, the quality of general predictive models was compared to that of species-group models. This study confirms that quality level 2 LiDAR data was sufficient for developing adequate predictive models (R2adj. ranging between 0.71 and 0.82) when compared to the predictive models based on LiDAR and aerial imagery. Additionally, this study suggests that species-group predictive models were of higher quality than general predictive models. Lastly, landscape-level maps were created from the predictive models, and these may be helpful to plan-ners, forest managers, and landowners in their management efforts.

In an era dominated by rapid digitalization of sensed data, the secure exchange of sensitive information poses a critical challenge across various sectors. Established techniques, particularly in emerging technologies like the Internet of Things (IoT), grapple with inherent risks in ensuring the confidentiality and integrity of transmitted data. Blockchain technology, known for its decentralized and tamper-resistant characteristics, stands as a reliable solution for secure data exchange. However, the persistent challenge lies in protecting sensitive information amidst evolving digital landscapes. This paper explores the potential of Non-Fungible Tokens (NFTs), a manifestation of blockchain technology, as dynamic carriers for diverse forms of sensitive information among businesses beyond their conventional association with art and collectibles. Our solution leverages the immutable NFT data to serve as a secure data pointer, while the mutable NFT data holds sensitive information protected by steganography. This dual approach ensures both data integrity and authorized access, even in the face of evolving digital threats. A performance analysis validates the efficacy of the approach, highlighting its reliability, robustness, and resistance to potential attacks on hidden information, ensuring secure data transmission across industries.

The Topographic Ruggedness Index (TRI) is one of the essential measurement in topographic analysis, as well as in explaining biodiversity and geo-diversity. This study demonstrate how to utilize Geographic Information Systems (GIS) to generate TRI for Nyanga District using Digital Elevation Model (DEM). Though the TRI strongly depends on a local scale slope derived from an average adjacent neighbor slope algorithm, and selection of different lag distances in the computation of spatial variability, the measurement demonstrated that areas of higher altitude have higher ruggedness index. The TRI model was generated by computing focal statistics to determine focal sum of DEM and of DEM ². The final TRI was computed using Math Algebra (Raster Calculator) to generate layers of different ruggedness suitable for topographic and biodiversity analysis. The algorithm presented could also be used for smaller areas with high quality data and corrected DEMs. Despite widespread adoption, ruggedness metrics require thorough testing using both artificial landscapes and real world applications.

The calibration of length is related to the national economy and people's livelihood. The traditional calibration of length value was sent the uncalibrated instrument to the laboratory and the intrinsic error of the tested instrument was obtained in the laboratory. However, calibrating the instrument in the site produced additional error. In this article, a remote calibration method of length measurement based on optical fiber information transmission following with a proof of principle system was studied to reduce the additional error. The 1.0-level displacement guide rail was calibrated according to this method. The results showed that the relative error of guide rail displacement under different lengths was within 0.6%. The expanded uncertainties of 10 μm, 100 μm and 1 mm were analyzed respectively. The calibration results meet the actual standards of the guide rail. This optical method is a good candidate for remote calibration.

It is difficult to obtain necessary information accurately from Social Networking Service (SNS) while raising children, and it is thought that there is a certain demand for the development of a system that presents appropriate information to users according to the child's developmental stage. There are still few examples of research on knowledge extraction that focuses on childcare. This research aims to develop a system that extracts and presents useful knowledge for people who are actually raising children, using texts about childcare posted on Twitter. In many systems numbers in text data are just strings like words and are normalized to zero or simply ignored. In this paper, we created a set of tweet texts and a set of profiles created according to the developmental stages of infants from "0-year-old child" to "6-year-old child". For each set, we used ML algorithms such as NB (Naive Bayes), LR (Logistic Regression), ANN (Approximate Nearest Neighbor algorithms search), XGboost, RF (random forest), decision trees, and SVM (Support Vector Machine) to compare with BERT, a neural language model, to construct a classification model that predicts numbers from "0" to "6" from sentences. The accuracy rate predicted by the BERT (Bidirectional Encoder Representations from Transformers) classifier was slightly higher than that of the NB, LR, ANN, XGboost, RF, decision trees, and SVM classifiers, indicating that the BERT classification method was better.

The ability to handle spatiotemporal information makes contribution for improving the prediction performance of machine RUL. However, most existing models for spatiotemporal information processing are not only complex in structure but also lack adaptive feature extraction capabilities. Therefore, a lightweight operator with adaptive spatiotemporal information extraction ability named Involution GRU (Inv-GRU) is proposed for aero-engine RUL prediction. Involution, the adaptive feature extraction operator, is replaced by the information connection in the gated recurrent unit for obtaining the adaptively spatiotemporal information extraction ability and reducing the parameters. Thus, Inv-GRU can well extract the degradation information the of aero-engine. Then for RUL prediction task, the Inv-GRU based deep learning (DL) framework is firstly constructed, where features extracted by Inv-GRU and several human-made features are separately processed to generate the health indicators (HIs) from multi-raw data of aero-engines. Finally, fully connection layers are adopted are adopted to reduce dimension and regress RUL based on the generated HIs. By applying the Inv-GRU based DL framework to the Commercial Modular Aero Propulsion System Simulation (C-MAPSS) datasets, successful predictions of aero-engines RUL have been achieved. Comparative analysis reveals that the proposed model exhibits superior overall prediction performance compared to recent public methods.

It is important that both static and dynamic information is efficiently used to create a suitable tourism plan. However, collecting, accumulating and managing dynamic information can cost tourists time, money and energy. The present study developed an original tourism support system was designed and developed with the purpose of reducing the burden of tourists who are unfamiliar with the tourist destinations in particular. An original tourism planning support system and web-geographic information systems (Web-GIS) were integrated into a single system, and two original functions were implemented. The system was operated targeting Osaka City, Japan for a period of one month. The information concerning 529 sightseeing spots was collected from tourism-related web media and then saved to the database of the system beforehand. During the operation period, a cumulative total of 160 users accessed the system. Based on the questionnaire survey results, most of the main functions were highly rated regardless of users’ travel experiences to the operation target area. Additionally, based on the access log analysis results, it was clear that the use of the system was in line with the purpose of the present study, as the number of visits to the page for the main functions were high.

It is difficult to obtain necessary information accurately from Social Networking Service (SNS) while raising children, and it is thought that there is a certain demand for the development of a system that presents appropriate information to users according to the child's developmental stage. There are still few examples of research on knowledge extraction that focuses on childcare. This research aims to develop a system that extracts and presents useful knowledge for people who are actually raising children, using texts about childcare posted on Twitter. In many systems numbers in text data are just strings like words and are normalized to zero or simply ignored. In this paper, we created a set of tweet texts and a set of profiles created according to the developmental stages of infants from "0-year-old child" to "6-year-old child". For each set, we used Support Vector Machine (SVM), and Bidirectional Encoder Representations from Transformers (BERT), a neural language model, to construct a classification model that predicts numbers from "0" to "6" from sentences. The accuracy rate predicted by the BERT classifier was slightly higher than that of the SVM classifier, indicating that the BERT classification method was better.

Rooftop solar (RTS) projects have the potential to contribute to climate change mitigation and sustainable development objectives. In India, grid-connected RTS capacity has been targeted at 40 GW by the end of the year 2022 however, the installed capacity is limited to 8.2 GW only. As a result, the country missed its 2022 target of installing 100 GW of solar power capacity, largely due to inadequate uptake of RTS. The prevailing practices of RTS implementation is observed to be complex, repetitive, tedious, and time-consuming for potential consumers and there is a lack of coordination among implementing agencies. Therefore, even with vast potential, supportive measures, dedicated policies, and regulations the penetration of RTS systems is far below their respective potential. The introduction of information technology (IT) intervention in the RTS sector would integrate key stakeholders at a single platform so as to reduce the administrative burden of implementing agencies, optimization of processing time, handle multiple consumers simultaneously, and connect consumers with authentic vendors, etc. This would help them to understand and perform their shared roles and responsibilities and enhance the RTS implementation across the state. In this study, the scope for IT interventions in the existing RTS implementation process of the Odisha Renewable Energy Development Agency (OREDA) has been assessed and a simplistic approach has been developed for the execution of the RTS projects in relatively lesser time with the least human interface as well as resources. This approach has been successfully demonstrated with OREDA and a cumulative capacity of around 5 MWp (individual project of 1-2 kWp) residential grid-connected RTS projects implemented through the web platform. Currently, other state nodal agencies are also adopting the model for RTS execution and consumer proliferation through IT interventions.

Information cascades (tree-like structures formed by posts, comments, likes, replies, etc.) constitute the spine of the public online information environment, reflecting its various trends, evolving with it and, importantly, affecting its development. While users participate in online discussions, they display their views and thus contribute to the growth of cascades. At the same time, users’ opinions are influenced by cascades’ elements. The current paper aims to advance our knowledge regarding this social phenomenon by developing an agent-based model in which agents participate in a discussion around a post on the Internet. Agents display their opinions by writing comments on the post and liking them (i.e., leaving positive assessments). The result of these processes is dual: on the one hand, agents develop an information cascade; on the other hand, they update their views. Our purpose is to understand how agents’ activity, openness to influence, and cognitive constraints (that condition the amount of information individuals are able to proceed with) affect opinion dynamics. We also control for social contagions (when people’ perception of a message may depend not only on the message’s opinion, but also on how other individuals perceive this object, with more positive evaluations increasing the probability of adoption) and ranking algorithms that steer the order in which agents learn new messages. Among other things, we demonstrated that replies to disagreeable opinions are extremely effective for promoting your own position. In contrast, likes have a tiny effect on this issue.

To increase the network computer and mobile telephone capacity one needs a laser to carry information instead of electrons. Since the laser is very fast compared to electrons, one expects information to be transmitted very fast through the network (internet). This requires searching for chips that act as capacitors, inductors, or evens as resistors this work shows that the laser traveling beam diminished as the frequency reciprocal thus acts as a capacitor or diminished as frequency thus acts as an inductor and sometimes diminished with the concentration of carriers thus act as a resistor for magnetic materials with strength that cancels the friction force when the laser frequency is equal nearly to the atoms natural frequency the material act as an inductor. Then frictional force is dominant with high mobility dielectric, the material acts as a capacitor. However, it acts as a conductor for negligible friction and natural frequency.

Utilization of Bidirectional Reflectance Distribution Function (BRDF) model parameters obtained from the multi-angular remote sensing is one of the approaches for the retrieval of vegetation structural information. In this research, the potential of multi-angular vegetation indices, formulated by the combination of multi-spectral reflectance from different view angles, for the retrieval of forest above ground biomass was assessed. This research was implemented in the New England region with the availability of a high quality forest inventory database. The multi-angular vegetation indices were generated by the simulation of the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo Model Parameters Product (MCD43A1 Version 6) based BRDF parameters. The effects of seasonal (spring, summer, autumn, and winter) composites of the multi-angular vegetation indices on above ground biomass, angular relationship of the spectral reflectance with above ground biomass, and the interrelationships between the multi-angular vegetation indices were analyzed. Among the existing multi-angular vegetation indices, only the Nadir BRDF-adjusted NDVI ( and Hot-spot incorporated NDVI ( showed significant relationship (more than 50%) with the above ground biomass. This research proposed two more sensitive vegetation structural indices, Fore-scattering Back-scattering NDVI and Vegetation Structure Index (VSI). The Fore-scattering Back-scattering NDVI showed higher sensitivity (R² = 0.62, RMSE = 52.46) towards the above ground biomass than existing multi-angular vegetation indices. Furthermore, the VSI performed in the most efficient way explaining 64% variation of the above ground biomass, suggesting that the right choice of the spectral channel and observation geometry should be considered for improving the estimates of the above ground biomass. In addition, the right choice of seasonal data (summer) was found to be important for estimating the forest biomass while other seasonal data were either insensitive or pointless. The promising results shown by the VSI suggest that it could be an appropriate candidate for monitoring vegetation structure from the multi-angular satellite remote sensing.

The importance of ICTs for dissemination of information to farmers has been verified by extension practitioners, information and communication scholars, policy makers and development agencies. Information related to new seed varieties, production technologies, livestock breeds, vaccines, including weed and pest control, as well as relevant market information is constantly required by smallholder farmers. The identification of specific attributes among smallholder farmers which contributes to their adoption of a proposed ICT-based information source provides an important tool for developing interventions which address the information needs of farmers. Using a literature survey methodology, pertinent studies related to adoption of ICTs, farmers’ information source usage and relevant frameworks were identified, including applicable theories and models in technology adoption and information behaviour. In the proposed framework, the socio-economic characteristics of smallholder farmers were posited as key variables influencing smallholder farmers, within a farming system, to adopt ICT-based information sources. The framework contributes to discern the prospects of adopting ICT-based information sources by individual farmers within a farming system, and may also envisage other related welfare outcomes and market participation pathways among smallholder farmers. The review also addresses the paucity of conceptual discourse, while contributing to a growing pool of research on ICT in African agriculture.

There has been rampant fold-ups, merger and acquisitions occurring in the Ghanaian banking industry. Then, the questions arise: Is the Ghanaian Financial System in Crisis? This study was conducted to find answers to these problems unsolved with prior literature. A sample of seventy customers of the Royal Bank, 8 employees of the Royal Bank and 2 managers of the Royal Bank were selected for a case-survey. The study also monitored the Trend of the Ghanaian Financial System through the reading and monitoring of daily news on the Financial System and reports of banks. The data from the field and the secondary data from news and reports were analysed symmetrically. The study drew on Minsky’s Financial Crisis Theory to explain the phenomenon in the Ghanaian economy and to draw predictions of what would happen in other developing economies. The study found out that: (1) The Ghanaian financial system is fragile and it holds true for most developing economies; (2) The financial system suffers greatly when the confidence level of customers falls significantly; (3) Management information systems raises the confidence level of customers (borrowers and lenders) such that there is a greater fall and impact in times of instability in the economy; (4) The higher the level of MIS adoption in an unstable economy, the more fragile the Financial System becomes and (5) A higher adoption of Management Information Systems in a Fragile Financial System indirectly contributes to Financial Crisis of the Financial System.

Perception and motor interaction with physical surroundings can be analyzed by the changes in probability laws governing two possible outcomes of neuronal activity, namely the presence or absence of spikes (binary states). Perception and motor interaction with physical environment are accounted partly by the reduction in entropy within the probability distributions of binary states of neurons in distributed neural circuits, given the knowledge about the characteristics of stimuli in physical surroundings. This reduction in the total entropy of multiple pairs of circuits in networks, by an amount equal to the increase of mutual information among them, occurs as sensory information is processed successively from lower to higher cortical areas or between different areas at the same hierarchical level but belonging to different networks. The increase in mutual information is partly accounted by temporal coupling as well as synaptic connections as proposed by Bahmer and Gupta [1]. We propose that robust increases in mutual information, measuring the association between the characteristics of sensory inputs and neural circuits connectivity patterns, are partly responsible for perception and successful motor interactions with physical surroundings. It is also argued that perception from a sensory input is the result of networking of many circuits to a common circuit that primarily processes the given sensory input.

The commitment of the EU to Corporate Social Responsibility (CSR) is projected in the EU law about annual reporting by businesses. Since EU member states further develop this framework by their own domestic laws, annual reporting with CSR information is not unified and just partially mandatory in the EU. Do all European businesses report CSR information and what public declaration to society do they provide with it? The main dual purpose of this paper is identifying the parameters of this annual reporting duty and studying the CSR information provided by the ten largest Czech companies in their annual statements for 2013-2017. Based on legislative research and the teleological interpretation, the current EU legislative framework with Czech particularities is presented and, via a case study exploring 50 annual reports, the data about the type, extent and depth of the CSR is dynamically and comparatively assessed. It appears that, at a minimum, large Czech businesses satisfy their legal duty and e-report on CSR in a similar extent, but in dramatically different quality. Employee matters and adherence to international standards are used as a public declaration to society more than the data on environmental protection, while social matters and R&D are played down.

Disaster Risk Reduction (DRR) is a high priority on the agenda of main stakeholders involved in sustainable development and Earth Observation (EO) can provide useful, timely and economical information in this context. This short communication outlines the European Space Agency’s (ESA) specific initiative to promote the use and uptake of satellite data in the global development community: ‘Earth Observation for Sustainable Development (EO4SD)’. One activity area under EO4SD is devoted to Disaster Risk Reduction: EO4SD DRR. Within this project, a team of European companies and institutions are tasked to develop EO services for supporting the implementation of DRR in International Financial Institutions’ (IFI) projects. Integration of satellite-borne data and ancillary data to generate insight and actionable information is thereby considered a key factor for improved decision making. To understand and fully account for the essential user requirements (IFI & Client States), engagement with technical leaders is crucial. Fit-for-purpose use of data and comprehensive capacity building eventually ensure scalability and long-term transferability. Future perspectives of EO4SD and DRR regarding mainstreaming are also highlighted.

A probabilistic description is essential for understanding the dynamics of stochastic systems far from equilibrium, given uncertainty inherent in the systems. To compare different Probability Density Functions (PDFs), it is extremely useful to quantify the difference among different PDFs by assigning an appropriate metric to probability such that the distance increases with the difference between the two PDFs. This metric structure then provides a key link between stochastic systems and information geometry. For a non-equilibrium process, we define an infinitesimal distance at any time by comparing two PDFs at times infinitesimally apart and sum these distances in time. The total distance along the trajectory of the system quantifies the total number of different states that the system undergoes in time, and is called the information length. By using this concept, we investigate the information geometry of non-equilibrium processes involved in disorder-order transitions between the critical and subcritical states in a bistable system. Specifically, we compute time-dependent PDFs, information length, the rate of change in information length, entropy change and Fisher information in disorder-to-order and order-to-disorder transitions, and discuss similarities and disparities between the two transitions. In particular, we show that the total information length in order-to-disorder transition is much larger than that in disorder-to-order transition, and elucidate the link to the drastically different evolution of entropy in both transitions. We also provide the comparison of the results with those in the case of the transition between the subcritical and supercritical states and discuss implications for fitness.

For the first time in the world, we provided a phenomenologically verifiable proof for the validation of origin of universe and perceivable reality from the unperceivable information based on Ayurveda. We also provided the axiomatic definitions for the terminologies used to deliver the theoretical framework for the same based on Bharatheeya Knowledge Systems. The presented manuscript provides a corelation between quantum information transport and emergence of gunam “Laghu” and gunam “Guru”. We identified that this point of emergence can be considered as the origin point in the continuous process of generation of Perceivable Reality “Vyaktham” from Unperceivable Information “Avyaktham”. In this context we can consider the unperceivable information in “Avyaktham” as dark matter. So, we must develop methods like gunascopy or informationscopy to understand the types of information imbibed in the quantum matter of a dravyam to understand its original physico-chemical-biological and information state of the constituent matter. This is possible through the assessment of Dravya Guna aspects and panchabhootha tatwas of the dravyas. For the first time in the world, we have developed the method to derive all the gunas from “guru” and “Laghu” and calculated the mass of the finitie components of Nava Dravyas including Mann, Aatma, Kaal, Disha, Aakash, Vaayu, Agni, Jal and Prithvi from the quantum information emergence equation. We strongly believe that this work will open up new avenues to explore the frontiers of reality and universe through natural cognition. The solutions to the Yang-Mill conjecture is achieved by our team using the Quantum Information Emergence and Transport Equations. We proved that the information bounded in the quantum states of the constituent quantum matter of quantum particles will provide them positive masses, even though they travel at the speed of light. Based on the phenomenologically verifiable axioms proposed in this work, we proved that the space on the topologically invarient 2D Poincare Sphere which is equivalent to a 2D space without boundaries can facilitate the never-ending loop of continuous quantum information transport between “Avyaktham” and “Vyaktham”. So the information availabe in the constituent quantum states of “Avyaktham” is equal to the postiive mass of quantum systems travelling at the speed of light. In such condition all the mass of the quantum particles will be converted in to information bounded in the corresponding quantum states of the quantum matter. This complete conversion of mass of the quantumparticles into information will happen only at maximum entropy. Thus at maximum entropy the information which is self information entangled with its own quantum states in “Avyaktham” will have a positive mass equal to the mass gap proposed in the Yang-Mill conjecture.

In this paper, the effect of multi-pass friction stir processing on the mechanical properties of AZ91 alloy has been studied. For the numerical investigation, this process has been simulated with the three-dimensional numerical modeling based on the ABAQUS/Explicit. This simulation involves the Johnson-Cook models for defining the material behavior during this intense plastic deformation and investigating the fracture criterion. The tool plunging and stirring phases in the two-pass process has been simulated. To prevent too much distortion of elements during modeling, the Arbitrary Lagrangian-Eulerian technique for automatically re-meshing of distorted elements has been used. The model was calibrated using the experimental results from the previous works. This model can predict the transient temperature distribution and residual stress field during FSP on AZ91. The results show that the maximum temperature in the advancing-side region is more than that in the retreating-side region. In addition, numerical results show that at the end position of the process, the tool during the lift-up leaves the keyhole region in the compressive stress state. The experimental results investigated the effect of multi-pass FSP on the microstructure, microhardness, tensile and creep properties of AZ91 magnesium alloy. The FSP is performed by applying 50% overlapping. The tensile, and creep tests are conducted at several temperatures from 25 to 210 °C. The optical microscopy and scanning electron micrograph (SEM) were used to study the microstructure of the samples after performing multi-pass friction stir processing. The experimental results indicate that on average, the tensile strength, microhardness, and creep strength of the processed samples increased by about 29, 23, and 38 %, respectively compared to the unprocessed ones.

The present study aimed at investigating the influence of the concentration of sodium silicate and sodium hexametaphosphate on the dispersion of an aqueous kaolinitic clay slurry regarding further use for the tape casting process. The zeta potential of the kaolinitic clay slurry matched the requirements for tape casting. The addition of magnesite in the kaolinitic slurries tended to increase the zeta potential towards the required limit values. Despite, the further addition of surfactants allowed improving the zeta potential in agreement with the tape casting conditions. Accordingly, the rheological behavior, under continuous and oscillatory flow conditions, of various mixtures of magnesite and a kaolinitic clay was studied. Regarding the pH and the zeta potential measurements, the E–F attraction prevailed at low pH value, and F–F or E–E attraction was predominant at high pH value. All slurries exhibited a shear thinning behavior, which was well-correlated by Herschel–Bulkley model. It appeared that the best stability for the kaolinitic clay slurries was obtained while using 0.4 mass% and 1.2 mass% of sodium hexametaphosphate and sodium silicate respectively. An increase in the magnesite concentration above 6 mass% led to a complex behavior with low cohesion energy due to the occurrence of soluble complexes.

Automatic recognition methods for non-stationary EEG data collected from EEG sensors play an essential role in neurological detection. The integrative approaches proposed in this study consists of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-level classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets were adopted to decompose the EEG data into five time-frequency sub-bands, whose statistical features were computed and used as classification features. The grid search optimizer was used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a support vector machine and a random forest classifier constructed according to previous descriptions. Multiple-index were used to evaluate the Symlet wavelet transform-gradient boosting machine-grid search optimizer classification scheme, which provided better classification accuracy and detection effectiveness than has recently reported in other work on three-level classification of EEG data.

This study investigated how positive (e.g., incentives) and negative (e.g., restrictions) reinforcement used in vaccination promotional messages and trust in government impact parental vaccine perceptions and decisions on vaccinating their children during a large-scale infectious disease crisis. Two hundred and eighty-six parents of unvaccinated teens participated in a 2 (reinforcement: positive vs. negative) × 2 (trust in government: high vs. low) between-subjects experimental survey. Positive reinforcement (e.g., gift cards for the vaccinated) was more effective for parents with low trust in the government to improve their attitudes and intentions toward child vaccination, while parents with high trust in the government were unaffected by reinforcement types. To the best of our knowledge, this study is among the first to examine the effects of both positive and negative reinforcement on vaccination perceptions and behavioral intentions through a controlled experiment and is the first study to do so in a large-scale infectious disease crisis setting. This research is expected to help public health professionals better prepare for and manage virus-induced public health crises in the future.

Background: Despite the tremendous effort in hamstring injury prevention, injury rates in soccer remain elevated. Hypothesis: Hamstring and hip/pelvic isometric strength imbalances may increase the likelihood of hamstring strain injury (HSI) occurrence amongst soccer players.Study design: Case-control study.Methods: In 120 male soccer players (mean age: 20.0 +/- 6.96 years) pre-season maximum isometric strength of the hip adductors, abductors, flexors, and knee flexors were measured, and strength ratios were calculated. Previous injury and anthropometric data were registered. Injury data were collected following the FIFA/UEFA consensus. A multivariate and Machine learning-based Logistic Regression model was employed to estimate the likelihood of players sustaining a hamstring injury.Results: 21 players sustained 32 hamstring injuries. Players with higher abduction strength in the dominant leg had an 18.5% lower risk of sustaining a hamstring injury (OR = 0.818, CI 95% = [(0.695-0.964]). No other significant contributor variables were found to be significant predictors of hamstring injury in the player group (p> 0.05).Conclusion: Isometric strength of the abductor muscles in the dominant limb had a significant association with hamstring injury.Clinical relevance: Our findings indicate the utilization of hip strength measurements as an integral component of the pre-season screening assessment, thereby affirming prior research on the correlation between lumbo-pelvic stability and incidence of hamstring injuries. Consequently, clinicians should contemplate incorporating hip strengthening exercises into regular training sessions, rehabilitation programs, and injury prevention protocols especially indicated when lower hip strength levels are observed.

Random variations in microelectronic circuit structures represent the source of entropy for physical unclonable functions (PUFs). In this paper, we investigate delay variations that occur through the routing network and switch matrices of a field programmable gate array (FPGA). The delay variations are isolated from other components of the programmable logic, e.g., Look-up tables (LUTs), flip-flops (FFs), etc. using a feature of Xilinx FPGAs called dynamic partial reconfiguration (DPR). A set of partial designs are created that fix the placement of a time-to-digital converter (TDC) and supporting infrastructure to enable the path delays through the target interconnect and switch matrices to be extracted by subtracting out common-mode delay components. Delay variations are analyzed in the different levels of routing resources available within FPGAs, i.e., local routing and across chip routing. Data is collected from a set of Xilinx Zynq 7010 devices, and a statistical analysis of within-die variations in delay through a set of the randomly-generated and hand-crafted interconnects is presented.

Dietary habits, including meal frequency, meal timing, and skipping meals, have been extensively studied due to their association with the development of non-communicable diseases (NCDs). This study describes dietary habits, meal timing, meal frequency, skipping meals, and late-night eating in Kuwaiti adults. The Kuwait National Nutrition Surveillance System data was utilized to reach the objectives of this study. The findings reveal that approximately 50% of the adults in Kuwait eat after 10 p.m., 29% skip breakfast, and 9.8% skip dinner. Furthermore, adults in Kuwait consume 4.4 meals per day on average. Women skip breakfast more often and have extended night fasting than men (p&lt;0.001). Married adults skip breakfast and dinner less than unmarried adults (p&lt;0.001). In conclusion, this descriptive study provides valuable insights into the dietary habits of Kuwaiti adults, emphasizing the importance of further investigating the association between meal timing, meal frequency, and the prevalence of NCDs in Kuwait.

INTRODUCTION : In this scoping review, we delve into the transformative potential of artificial intelligence (AI) in addressing challenges inherent in whole genome sequencing (WGS) analysis, with a specific focus on its implications in surgical oncology. METHODS: Scoping review of whole genomic sequencing and artificial intelligence.DISCUSSION : Unveiling the limitations of existing sequencing technologies, the review illuminates how AI-powered methods emerge as innovative solutions to surmount these obstacles. The evolution of DNA sequencing technologies, progressing from Sanger sequencing to next-generation sequencing, sets the backdrop for AI's emergence as a potent ally in processing and analyzing the voluminous genomic data generated by these technologies. Particularly, deep learning methods play a pivotal role in extracting knowledge and discerning patterns from the vast landscape of genomic information. In the context of oncology, AI-powered methods exhibit considerable potential across diverse facets of WGS analysis, including variant calling, structural variation identification, and pharmacogenomic analysis. CONCLUSIONS : This review underscores the significance of multimodal approaches in diagnoses and therapies, highlighting the imperative for ongoing research and development in AI-powered WGS techniques. Integrating AI into the analytical framework empowers scientists and clinicians to unravel the intricate interplay of genomics within the realm of multi-omics research, paving the way for more personalized and targeted treatments in surgical oncology and perhaps beyond.

Thromboprophylaxis of hospitalized patients at risk of venous thromboembolism (VTE) presents challenges owing to patient heterogeneity and lack of adoption of evidence-based methods. Intuitive practices for thromboprophylaxis have resulted in many patients being inappropriately prophylaxed. Multiple interventions for thromboprophylaxis have been tested, including use of multifaceted approaches such as national VTE prevention programs with audits, use of passive alerts (either human or electronic), and more recently use of active clinical decision support (CDS) tools incorporated into electronic health records (EHRs). Multifaceted health-system interventions have shown mixed results in their ability to increase appropriate thromboprophylaxis and reduce VTE, unless mandated through a national VTE prevention program, though the latter approach is potentially costly, effort- and time-dependent. Studies utilizing passive human or electronic alerts have also shown mixed results in increasing appropriate thromboprophylaxis and reducing VTE. The use of active CDS tools holds the greatest promise, especially with further refinement and widespread implementation within various EHRs and clinical workflows. Recent-ly a universal cloud-based and EHR-agnostic CDS VTE tool revealed high adoption and effectiveness in increasing appropriate thromboprophylaxis and reducing major thromboembolism. This narrative review summarizes the literature on system-wide thromboprophylaxis interventions in hospitalized patients, with a focus on CDS tools.

Intelligent systems are increasingly present in various areas of society, gaining special relevance in healthcare environments. In this work, grounded on previous developments proposed by the authors, the evolution and improvement of a novel intelligent system focused on the diagnosis of obstructive sleep apnea (OSA) is addressed. To do this, starting from two packages of information of a heterogeneous nature, and deploying a set of Machine Learning classification algorithms, focused each of them on different levels of the apnea-hypopnea index (AHI), as well as a set of cascaded expert systems, it is possible to obtain a series of risk metric pairs (Statistical Risk, Symbolic Risk) for each AHI level, understood as a warning forecast associated with the early detection of the pathology. The risk indicators of each pair are subsequently aggregated by means of a symbolic inference system, whose knowledge base was determined using the automatic rule generation approach proposed by Wang and Mendel. At the output of each inference system, a risk indicator is obtained for each AHI level, the Apnea Risk, whose interpretation makes it possible to discriminate between patients who could suffer OSA and those who do not, as well as to generate the appropriate recommendations. For the initial tests of the system, the design and development of a specific software artifact was carried out using data from 4,400 patients from the Álvaro Cunqueiro Hospital in Vigo, obtaining when applied to the test set AUC values within the range 0.74–0.88, which invites optimism and points to the benefits expected from the architecture of the proposed intelligent system.

Big Data classification has recently received a great deal of attention due to the main properties of Big Data, which are volume, variety, and velocity. The furthest-pair-based binary search tree (FPBST) shows a great potential for Big Data classification. This work attempts to improve the performance the FPBST in terms of computation time, space consumed and accuracy. The major enhancement of the FPBST includes converting the resultant BST to a decision tree, in order to remove the need for the slow K-nearest neighbors (KNN), and to obtain a smaller tree, which is useful for memory usage, speeding both training and testing phases and increasing the classification accuracy. The proposed decision trees are based on calculating the probabilities of each class at each node using various methods; these probabilities are then used by the testing phase to classify an unseen example. The experimental results on some (small, intermediate and big) machine learning datasets show the efficiency of the proposed methods, in terms of space, speed and accuracy compared to the FPBST, which shows great potential for further enhancements of the proposed methods to be used in practice.

Given the increasing public interest in how ingredients are processed and the growing demand for organic food products, it is critical to understand consumers’ expectations about the process-related quality of organic products. Consumers perceive organic food to be nutritious, healthy and either natural or less processed, as they are afraid of the loss of nutrients and other natural properties of the food products. However, emerging food processing technologies might generate healthy and safe food options with nutritional quality properties. Simplified communication schemes might help to overcome this barrier. The main objective of this paper is to propose a working definition of "careful processing" for organic products and test its consistency while being used in scoring different processing methods by consumers. Results show that the proposed definition allows to consistently rate alternative processing methods. Consumers tend to score novel processing methods such as pulsed electric fields and microwave as less careful, supporting the idea that organic consumers want the least man-made interference with their food products. Results show that a simple but effective definition of careful processing may help consumers to distinguish further organic food products from conventional ones, no matter which communication scheme is used.

Anchovies are amid the largest fish catch worldwide. The anchovy fillet industry generates a huge amount of biowaste (e.g. fish heads, bones, tails) that can be used for the extraction of several potentially valuable bioproducts including omega-3 lipids. Following the extraction of valued fish oil rich in omega-3, vitamin D₃ and zeaxanthin from anchovy fillet leftovers using biobased limonene in a fully circular process, the solid residue was used as starting substrate for the production of biogas by anaerobic digestion. The results first reported in this study demonstrate good potential energy recovery of the anchovy sludge of about 280 mL_CH4∙g_VS^-1. Due to unbalanced C/N ratio typical of marine biowaste, co-digestion with a carbon rich substrate is recommended.

Mercury is a toxic metal, thus, it is an element which has more and more restrictions in its uses, but despite the above, the removal of this metal, from whatever the form in which it is encountered (zero valent metal, inorganic or organic compounds), and from different sources, is of a widespread interest. In the case of Hg(II), or Hg²⁺, the investigations about the treatment of Hg(II)-bearing liquid effluents (real or in most cases synthetic solutions) appear not to end, and from the various separation technologies, adsorption is the most popular among researchers. In this topic, and in the 2019 year, more than 100 publications had been devote to this field: Hg(II)-removal-adsorption. This work examined all of them.

Extracts from parts of coniferous trees have received increased interest due to their valuable bioactive compounds and properties, useful for plenty of experimental and consolidated applications, in fields comprising nutraceutics, cosmetics, pharmacology, food preservation, and stimulation of plant growth. However, the variability of the bioactive properties, the complexity of the extraction methods, and the use of potentially harmful synthetic chemicals, still represent an obstacle to the spreading of such valuable natural compounds. Hydrodynamic cavitation is emerging as a promising innovative technique for the extraction of precious food components and by-products from waste raw material of the agro-food production chain, which can improve processing efficiency, reduce resource consumption, and produce healthy, high-quality products. In this study, a process based on controlled hydrodynamic cavitation was applied for the first time to the production of aqueous solutions of silver fir (Abies Alba Mill.) needles with enhanced antioxidant activity. The observed levels of the in vitro antioxidant activity, comparable or higher than those found for reference substances, pure extracts, and other water extracts and beverages, highlight the very good potential of the HC process for the creation of solvent-free, aqueous solutions endowed with bioactive compounds extracted from silver fir needles.

Flow field based methods are becoming increasingly popular for the analysis of interfacial shear rheology data. Such methods take properly into account the subphase drag by solving the Navier-Stokes equations for the bulk phases flows, together with the Boussinesq-Scriven boundary condition at the fluid-fluid interface, and the probe equation of motion. Such methods have been successfully implemented at the double wall-ring (DWR), the magnetic rod (MR), and the bicone interfacial shear rheometers. However, a study of the errors introduced directly by the numerical processing is still lacking. Here we report on a study of the errors introduced exclusively by the numerical procedure corresponding to the bicone geometry at an air-water interface. In our study we directly input a preset the value of the complex interfacial viscosity and we numerically obtain the corresponding flow field and the complex amplitude ratio for the probe motion. Then we use the standard iterative procedure to obtain the calculated complex viscosity value. A detailed comparison of the set and calculated complex viscosity values is made upon changing different parameters such as real and imaginary parts of the complex interfacial viscosity and frequency. The observed discrepancies yield a detailed landscape of the numerically introduced errors.

This paper presents an approach to the Kirchhoff-Love plate theory (KLPT) using the finite difference method (FDM). The KLPT covers the case of small deflections, and shear deformations are not considered. The FDM is an approximate method for solving problems described with differential equations. The FDM does not involve solving differential equations; equations are formulated with values at selected points of the structure. Generally in the case of KLPT, the finite difference approximations are derived based on the fourth-order polynomial hypothesis (FOPH) and second-order polynomial hypothesis (SOPH) for the deflection surface. The FOPH is made for the fourth and third derivative of the deflection surface while the SOPH is made for its second and first derivative; this leads to a 13-point stencil for the governing equation. In addition, the boundary conditions and not the governing equations are applied at the plate edges. In this paper, the FOPH was made for all of the derivatives of the deflection surface; this led to a 25-point stencil for the governing equation. Furthermore, additional nodes were introduced at plate edges and at positions of discontinuity (continuous supports/hinges, incorporated beams, stiffeners, brutal change of stiffness, etc.), the number of additional nodes corresponding to the number of boundary conditions at the node of interest. The introduction of additional nodes allowed us to apply the governing equations at the plate edges and to satisfy the boundary and continuity conditions. First-order analysis, second-order analysis, buckling analysis, and vibration analysis of plates were conducted with this model. Moreover, plates of varying thickness and plates with stiffeners were analyzed. Finally, a direct time integration method (DTIM) was presented. The FDM-based DTIM enabled the analysis of forced vibration of structures, with damping taken into account. In first-order, second-order, buckling, and vibration analyses of rectangular plates, the results obtained in this paper were in good agreement with those of well-established methods, and the accuracy was increased through a grid refinement.

The Drinking Water Directive (EU) 2020/2184 includes the parameter microcystin-LR, a cyanotoxin, which drinking water producers need to analyze if the water source has potential for cyanobacterial blooms. In light of the increasing occurrences of cyanobacterial blooms worldwide and given that more than 50 percent of the drinking water in Sweden is produced from surface water, both fresh and brackish, the need for improved knowledge about cyanotoxin occurrence and cyanobacterial diversity has increased. In this study a total of 98 cyanobacterial blooms were sampled in 2016-2017 and identified regarding their toxin production and taxonomical compositions. The surface water samples from freshwater lakes throughout Sweden including brackish water from 8 east coast locations along the Baltic Sea were analyzed for their toxin content with LC-MS/MS and taxonomic composition with 16S rRNA amplicon sequencing. Both the extracellular and the total toxin content was analyzed. Microcystin´s prevalence was highest with presence in 82% of blooms, of which as free toxins in 39% of blooms. Saxitoxins were found in 36% of blooms in which the congener decarbamoylsaxitoxin (dcSTX) was detected for the first time in Swedish surface waters at four sampling sites. Anatoxins were most rarely detected, followed by cylindrospermopsin, which were found in 6% and 10% of samples, respectively. As expected, nodularin was detected in samples collected from the Baltic Sea only. The cyanobacterial operational taxonomic unit (OTU) with the highest abundance and prevalence could be annotated to Aphanizomenon NIES-81 and the second most profuse cyanobacterial taxon to Microcystis PCC-7914. In addition, two correlations were found, one between Aphanizomenon NIES-81 and saxitoxins and another between Microcystis PCC-7914 and microcystins. This study is of value to drinking water management and scientists involved in recognizing and controlling toxic cyanobacteria blooms.

The aim of this study was to evaluate the challenges basic school tutors face when integrating Information and Communication Technology (ICT) into their instructing and learning activities and to recommend possible strategies for addressing those challenges. This study employed an interpretive paradigm and a qualitative method approach to analyze the challenges basic school teachers face when integrating ICT into teaching in an emerging country, particularly Ghana. The participants of this study were chosen using purposive and snowball sampling techniques. A total of twenty (20) respondents were chosen for this study. The study found that teachers, despite their challenges, had a strong desire to integrate ICT into their instruction and learning activities. These challenges are the limited number of ICT tools and the absence of fully-subscribed software package that runs on them, the lack of electrical outlets in some classrooms, poor internet connectivity and power fluctuation. These teachers also had to deal with challenges like Trojan horses and spam despite the slow internet connectivity. The study suggested that basic school management should procure modern ICT tools and fully-subscribed software package to expand the school's ICT infrastructure giving teachers the choice to choose the ICT tools they will integrate into their teaching and learning activities for the successful integration of ICT into instruction and learning activities. Again, school management should ensure teachers are given basic training regarding ICT and its incorporation into tuition and learning activities to acquire the necessary competencies, understanding, and attitudes that will enhance teaching and learning activities.

Spatial modeling in Geographic Information Systems (GIS) always involves choices. The existence of constraints, either of a financial nature or related to the specifics of the software itself, to the algorithms, the uncertainty and even the reliability of the data, the purposes and the applications of the studies, make this a kind of guiding compass for GIS analysts. Building on a previous exercise of data acquisition (check-ins) based on two Digital Social Networks (DSN — Facebook and Foursquare) and on the awareness of the use of Volunteered Geographic Information (VGI) generated by tourists sharing their topophilic ties through DSN, the present analysis aims to evaluate the contribution of modern techniques of spatial analysis applied to tourism in the “Alta and University of Coimbra” area. Concepts and procedural tasks related to density determination, cluster analysis and identification of patterns associated with regionalized variables have thus been implemented with the purpose of evaluating and comparing the results obtained through the application of two techniques of spatial analysis, Kernel Density Estimation (KDE) and Optimized Hot-Spot Analysis (OHSA) & Inverse Distance Weighting (IDW) Interpolation.

Cloud computing has revolutionized how industries store, process, and access data. However, the increasing adoption of cloud technology has also raised concerns regarding data security. Machine learning (ML) is a promising technique to enhance cloud computing security. This paper focuses on utilizing ML techniques (Support Vector Machine, XGBoost, and Artificial Neural Networks) to progress cloud computing security in the industry. The selection of 11 important features for the ML study satisfies the study's objectives. This study focused on identifying gaps in utilizing ML techniques in cloud cyber security. Moreover. this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The efficiency of the employed models is assessed by applying validation matrices of Precision, Accuracy, Recall values, F1 score, R.O.C. curves, and Confusion matrix. The results demonstrated that the X.G.B. model outperformed in terms of all the matrices with an Accuracy of 97.50 %, 97.60 % Precision, 97.60 % Recall values, and 97.50 % F1 score. This research highlights the potential of ML algorithms in enhancing cloud computing security for industries. It emphasizes the need for continued research and development to create more advanced and efficient security solutions for cloud computing.

Medical waste management is crucial issues for keeping our life safe and safety. Without effective measures for waste management, we cannot keep ourselves safe from different types of contagious diseases; viruses, bacteria. Knowledge sharing assists healthcare staff in identifying and mitigating hazards on appropriate handling, segregation and disposal medical waste. The aim of the study is to identify a multifaceted analysis of knowledge sharing, training, communication and attitudes for effective medical waste management. This study selected 153 medical workers randomly, including nurses, lab technologists, and cleaners, from hospitals, diagnostic centers, and clinics in the northern part of Bangladesh. The study design of this research is mixed-methods. This research adopts a survey and interview as a research strategy. The samples are collected randomly. SPSS software packages, MS Excel Office, and PLS-SEM are used to analyze data. Most of the respondents always share their knowledge face-to-face according to the findings. The result also shows that there are positive relationship between sources of knowledge (p: 0.046), awareness and knowledge (p: 0.049), attitudes of waste management (p: 0.025) and effective medical waste management. However, there are no relationships between communication and collaboration (p: 0.943), the practice of waste management (p: 0.155), supervision and staff monitoring (p: 0.527), training and education (p: 0.656) and effective medical waste management. Its focus on the northern part of Bangladesh introduces regional specificity, potentially limiting the applicability of results to other regions. The knowledge-sharing practices for waste management among medical staff are examined within this research, along with the relationship between attitude, awareness, and knowledge, staff supervision and monitoring, practice, training, and education and effective medical waste management that have not been studied before home and abroad. This analysis highlights the critical need for an integrated approach to medical waste management, emphasizing the importance of continuous education, open communication, and cultivating a culture that prioritizes environmental sustainability. The insights from this study offer practical recommendations for healthcare institutions to improve their medical waste management practices, ultimately leading to enhanced health outcomes and better environmental protection.

Despite the lack of evidence that suggests hemodiafiltration (HDF) offers a better survival outcome than standard hemodialysis (HD), the number of patients initiating HDF in Japan continues to rise. This study examined the temporal change in the number of HDF incidents, evaluated factors associated with all-cause mortality, and compared the mortality risk and survival time of patient on HDF with patients receiving standard HD in three sets of 2-year cohorts. The primary analyses included the insurance claims data of 460 HDF patients and propensity score-matched 903 standard HD patients who initiated dialysis therapy between April 1, 2012 and March 31, 2018. Patient follow-up was censored at the time of death or the end of the 2-year study period. The influence of comorbidities on all-cause mortality was investigated, and the survival outcomes between HDF and standard HD patient groups throughout cohorts were compared. The number of HDF patients was increasing throughout cohorts, but the proportions of mortality cases across cohorts slowly decreased. Dementia status increases the mortality risk of patients regardless of dialysis modality. The mortality risk among HDF patients with dementia was more significant than HD patients. Adjusting for all covariates, we observed that HDF patients had a reduced mortality risk and longer survival time than patients on standard HD. This study supports the notion that HDF reduces all-cause mortality compared with standard HD in an incident dialysis population in Fukuoka Prefecture, Japan.

Introduction: Healthcare workers (HCW) are at higher risk of SARS-CoV-2 infection. Viral surveillance for early detection of COVID-19 is a critical strategy to understand the infection dynamics in this population and to prevent transmission. The study examines SARS-CoV-2 infection and reinfection among HCW vaccinated against COVID-19 who are employed at a primary health care unit serving a disenfranchised community of Brazil. Methods: The study was conducted in Cidade Estrutural, Federal District of Brazil, between February and October 2021. Participants were interviewed and provided samples. A prospective open cohort study was used to analyze the frequency of SARS-CoV-2 infection and reinfection. Nasopharyngeal and peripheral blood samples were collected from workers presenting with flu-like symptoms and subjected to RT-qPCR and serological testing (IgM and IgG chemiluminescence). The frequencies of infection and reinfection (RT-qPCR positive results 90 days after the infection) were calculated along with their respective confidence intervals (95%CI). Results: Of the 128 workers, 61 (47.65%; CI: 39.19-56.25) reported probable SARS-CoV-2 infection before vaccination. Of these, 50 (39.06%; CI: 31.04-47.71) had SARS-CoV-2 infection after vaccination, confirmed by molecular tests. Reinfection was identified in seven workers (14.00%; CI: 6.95-26.18), based on the 90-day interval between results. The serological data from the 128 workers during the cohort indicated that 68 had IgG antibodies (53.12%; CI: 44.5-61.5) and 46 had IgM antibodies (35.93%; CI: 28.14-44.54) against SARS-CoV-2. SARS-CoV-2 infection was common in community health workers (CHW, 56%), registered nurses (50%), and licensed practice nurses (33%). Following the COVID-19 vaccination, the percentage of infections among CHW decreased from 47.83% to 4.35%. Conclusion: These results demonstrate that (i) approximately 40% of the workers were infected with SARS-CoV-2 in 2021 and (ii) reinfections confirmed by RT-qPCR occurred in 14% of the HCW after vaccination. The results provide valuable insights into the circulation of SARS-CoV-2 among HCW in a primary care unit serving a minoritized community.

Obstructive Sleep Apnea (OSA) is nowadays one of the respiratory pathologies with a higher in-cidence globally in developed countries. This situation led to an increase in the demand for medical appointments and diagnostic studies related to that condition, especially those based on poly-somnographies and cardiorespiratory polygraphies. These studies are limited in resources, causing long waiting lists with the subsequent impact on the patients’ health. Furthermore, it is necessary to mention that OSA’s symptomatology is not very specific, and it is typically present in the general population (excessive sleepiness, snore, etc.). In this regard, this paper proposes a novel intelligent clinical decision support system for the diagnosis of OSA which could be used to help medical teams, both in primary care settings and in units specialized in respiratory pathologies. The aim of the proposed system is to help discriminate the patients suspected of suffering from the pathology from those who are not. To this end, two types of information sets of heterogeneous nature are consid-ered. The first one encompasses objective data, related to the patient's health profile with infor-mation usually available in electronic health records. The second type comprises subjective data, referred to the symptomatology reported by the patient in a previous interview. To process the first group of information, a Machine Learning classification algorithm is used, Bagged Trees in this case. For processing the second information set, related with the symptomatology of the patient, a col-lection of expert systems based on fuzzy inferential systems arranged in cascade are employed. As a result, the system is able to determine two risk indicators related to the patient's risk of suffering from OSA: the Statistical Risk and the Symbolic Risk respectively. Subsequently, by interpreting both risk indicators mentioned it will be possible to determine the severity of the patients’ health, proposing a preliminary evaluation on their condition. For the initial tests of the system, a software artifact has been built using a dataset with 4,978 selected patients, suspected of suffering from OSA, from the Álvaro Cunqueiro Hospital in Vigo. The results obtained are promising, demonstrating the potential usefulness of this type of tools in medical diagnosis. Once the system has been validated with new data from clinical environments, it is considered as possible to obtain a relevant improvement in the quality of the healthcare services, and a reduction in the associated costs.

Background: The Fat-Augmented Latissimus Dorsi (FALD) flap is an autologous flap that combines Latissimus Dorsi (LD) flap with immediate autologous fat transfer (AFT) in order to improve breast reconstruction (BR) volume. In recent years, our team has described the ergonomic FALD flap, an evolution of this technique which helps to achieve a complete BR in a single surgical step. In this long-term study, we analyze morphological variations of the breast after ergonomic FALD flap reconstruction compared to the traditional transverse FALD flap technique. Methods: Between December 2020 and September 2022 we prospectively enroll patients undergoing BR using FALD flap into 2 groups: Group A included ergonomic FALD flap, while Group B included traditional FALD flap. The primary endpoint of the study was to compare the two groups in terms of breast projection (BP), breast width (BW) and breast height (BH), while the second endpoint concerned the analysis of the aesthetic outcomes. Results: 32 FALD flaps (23 patients) were performed for the Group-A and 31 FALD flaps (25 patients) for Group-B. There were no significant differences regarding demographic variables. After propensity score weighting analysis, Group-A showed a statistically significant better breast projection (p<0.0001) compared to Group-B. (6.05 vs 5.60). Final aesthetic analyses showed to be superior in Group-A concerning breast shape (p=0.001) and global score evaluation (p=0.004). Conclusion: The ergonomic FALD flap offers better long-term aesthetic outcomes for autologous BR with higher breast projection compared to the traditional transverse FALD flap.

Background : Drug treatments for gait disorders in post-stroke patients aim to reduce muscular hyperactivity. Analysis of muscle activity is essential to help clinicians understand these disorders. The aim of this study was to quantify changes in muscle synergies before (PRE) and after (POST) rectus femoris nerve block. Methods : Gait analysis of 8 post-stroke patients before and immediately after nerve block. Muscle synergies were quantified from electromyographic signals. We have selected: the account for variance indicating the robustness of the synergies, the recruitment selectivity index indicating the degree of multiplexing of the synergies, and the recruitment consistency index indicating the variability of the synergies. Results : A decrease in VAF is observed, suggesting a reduction in gait pattern robustness. We also note that spatial and temporal primitives are slightly different (IRC, IRS) in PRE vs. POST, and that their activation is more nested in POST than in PRE. Conclusions : The motor nerve block has not created new muscle synergies of the paretic limb during gait, but indicates that there is flexibility in motor organization. This method of quanti-fication may enable clinicians to assess the motor adaptation potential of their post-stroke patient.

The aim of the present study was to evaluate the effects on the presence and distribution of oxyntopeptic cells (OPs) and enteroendocrine cells (EECs) expressing GHR, NPY and SOM in the gastric mucosa of European seabass and seabream of dietary levels of bioactive peptides (BPs) obtained from salmon processing by-products (in silico approach). In this study, 27 seabass and 27 seabreams were divided into three experimental groups: a control group (CTR) fed a control diet and two groups fed different levels of BP to replace fishmeal: 5% BP (BP5%) and 10% BP (BP10%). The stomach of each fish was sampled and processed for immunohistochemistry. Some SOM, NPY and GHR-IR cells exhibited alternating “open type” and “closed type” EECs morphologies. In seabream gastric mucosa, the BP10% group (16.8 ± 7.5) showed a significant change in NPY-IR cell number compared to CTR (CTR 8.5 ± 4.8) and BP5% (BP10% vs. CTR P ≤ 0.01; BP10% vs. BP5% P ≤ 0.05). In seabream, SOM-IR cells in the BP 10% diet (16.8 ± 3.5) differed from those in CTR (12.5 ± 5) (CTR vs. BP 10% P ≤ 0.05) and BP 5% (12.9 ± 2.5) (BP 5% vs. BP 10% P ≤ 0.01). In seabass, EEC SOM-IR cells increased at 10 % BP (5.3 ± 0.7) compared to 5 % BP (4.4 ± 0.8) (5 % BP vs. 10 % BP P ≤ 0.05). The results obtained suggest that salmon BPs have a promising impact as circulating ingredients for seabass and seabream.

Gastrodia elata Bl., one of the well-known edible plants in China, belongs to the family of Orchidaceae. In recent years, the consumption rate of this plant as food supplements or health products is gradually rising in China. However, the traditional industrialization processing procedures (TP) for this plant was found to be too complicated and not friendly to the environment with time and energy consuming. Hence, we investigated whether the processing procedures could be simplified without affecting its overall chemical profile and anti-oxidation activity. In this experiment, a new processing procedure (NP) was designed for processing this plant, and the chemical compositions of the extract for Gastrodia elata produced by two different processing procedures were identified by UPLC-Q-TOF/MS. It was found that phenols and parisins were the most abundant chemical components for this extract. Among all the identified components, the contents of gastrodin and its citric acid derivatives were obviously higher than that of other components. Therefore, the contents of six gastrodin derivatives and ethanol soluble extract rate for Gastrodia elata produced by two different processing procedures, traditional and newly designed processing procedures, were further comparatively investigated for the validation of the applicability. The DPPH radical scavenging activities of the extract from Gastrodia elata produced by two different processing procedures were also investigated. The results demonstrated that the contents of gastrodin and p-hydroxybenzyl alcohol in all samples were slightly decreased in NP samples compared with that of TP samples (except batches of S3 and S6 samples), but no significant difference was observed between these two groups (p = 0.8273 for gastrodin and p= 0.2320 for p-hydroxybenzyl alcohol). Additionally, the total contents of parisins were also in a decreased trend in NP samples but with no significant difference when comparing with that in TP samples (p = 0.1721). The results for ethanol soluble extract rate also had the same situation (p = 0.1094). Taken together, it was suggested that the chemical profiles and contents of gastrodin derivatives and ethanol soluble extract rate for NP samples was not significant different to that of TP samples. The DPPH radical scavenging activity of the extract of Gastrodia elata produced with NP method was not significant different from that of TP samples. As a result, the industrial processing procedures for the preservation of this material plant could be simplified to a more time and energy saving procedure without changing the quality of the products, which is also consistent with the concept of green and sustainable industrial production.

Since the exploitation of mineral resources results in the release of radionuclides, and consuming radionuclides affects public health in the short and long term. A case study of the environmental radiation impact from coal mining and germanium processing was carried out in southwest China. The coal mines contain germanium and uranium and have been exploited for more than 40 years. The farmlands around the site of coal mining and germanium processing have been contaminated by the solid waste and mine water in some extend since then. Samples of crops have been collected from contaminated farmlands in research area. The research area covers a radius of 5 km, in which there are 2 coal mines located. 210Pb and 210Po have been analyzed as the key radionuclides during monitoring program. The average activity concentrations of 210Pb and 210Po in the crops were 1.38 and 1.32 Bq/kg in cereals, 4.07 and 2.19 Bq/kg in leafy vegetables and 1.63 and 1.32 Bq/kg in root vegetables. The annual effective doses due to the ingestion of 210Pb and 210Po in consumed crops have been estimated for adult residents living in research area. The average annual effective dose was 0.336mSv/a, while the minimum was 0.171 mSv/a and the maximum was 0.948 mSv/a. The results show that crops grown on contaminated farmland contained an enhanced level of radioactivity concentration. Ingestion doses of local residents in research area were significantly higher than the China average level of 0.112 mSv/a, and the world average level of 0.042 mSv/a through 210Pb and 210Po in crops intake respectively.