Consumption of ultra-processed foods is associated with abdominal obesity, but this association has not yet been investigated in individuals on hemodialysis. This cross-sectional study, developed with 1014 individuals, aimed to analyze the prevalence of abdominal obesity and its association with the consumption of ultra-processed foods in individuals on hemodialysis. Consumption of ultra-processed foods was measured using a questionnaire, whose list of foods was based on the Surveillance System of Risk and Protective Factors for Chronic Non-Communicable Diseases through Telephone Enquiry. Waist circumference was used to determine the presence of abdominal obesity. The difference in proportions between the independent variables and the outcome was verified using Pearson's chi-square test. Hierarchical logistic regression was used to determine the association between the outcome and the independent variables. The prevalence of abdominal obesity was 77.22%. In the final model, higher consumption of ultra-processed foods, be male and an adult, have between 8 and 11 years of schooling and practice physical activity below the recommended level increased the chances of having abdominal obesity. The research provides a basis for the creation and improvement of public policies that promote improvements in these health determinants.

The range of rating instruments in depression measurement and the depth of their analytical relevance constitutes a major development in this psychiatric and psychotherapeutic field of mental health. Though the competition is acute amongst these various instruments, the results for the public have been outstandingly positive. A depression rating scale is essentially a psychiatric measuring instrument utilized in the identification and ranking of depression severity within the patient. The scale provides the practitioner, psychiatrist or psychotherapist, with sufficient information to assess the severity of the depression plotted on the scale. Not used as a “diagnostic tool” itself, nevertheless, the depression rating scale does function as an effective device for designating and assigning a behavioral score which may, then, be used in establishing the severity of depression of value in the designation of a diagnosis and treatment formula. In this paper, we will take a close look at the leading depression rating scales and briefly summarize their scope of assessment value in rating depression.

Capsinoids (CAP) are potential antioxidant agents capable of reducing oxidative damage and the resulting complications triggered by obesity. Thus, the aim of this study was to investigate the effects of CAP on adiposity and biomarkers of cardiac oxidative stress in rats with obesity induced by a high-fat diet. Male Wistar rats were exposed to a high-fat diet for 27 consecutive weeks. After characterization of obesity (week 19), some of the obese (Ob) animals began to receive capsinoids (10 mg/kg/day) by orogastric gavage. Adiposity and comorbidities were assessed. In the heart, remodeling, injury and biomarkers of oxidative stress were determinated. The treatment did not reduce the adiposity induced d by obesity, but they were efficient in reducing cholesterol levels. CAP treatment did not cause a difference in heart and LV mass, despite having reduced troponin I concentrations. Furthermore, capisinoids did not reduce the increase in advanced oxidation of proteins products (AOPP) and carbonylated proteins (CBO) caused by obesity in cardiac tissue. Besides that, it was observed that ObCap rats presented high levels of malondialdehyde and greater activity of antioxidant enzymes compared to Ob. In conclusion, treatment with capsinoids did not lead to significant positive effects in reducing obesity-induced oxidative stress.

Background: To compare photodynamic therapy 41 and the use of probiotics in reducing halitosis assessed through gas chromatography and microbiome analysis. Methods: Participants aged from 18 to 25 years, showing sulfide (SH2) ≥ 112 ppb on gas chromatography, were selected. They were divided into 4 treatment groups: Group 1 - Tongue Scraping; Group 2- Antimicrobial Photodynamic Therapy (aPDT); Group 3- Probiotics; Group 4- Antimicrobial Photodynamic Therapy (aPDT) and Probiotics. The halimetry process was performed before, immediately after the treatments, 7 days, 14 days and 30 days after the initial collection. The collections for later microbiological analysis were made along with the halimetry, for the microbiome analysis. Results: Treatment with aPDT or probiotics under these experimental conditions was not able to change the bacteria present in the biofilm of the tongue. Conclusions: More research is needed to know the behavior of the oral microbiome in the presence of halitosis and the effectiveness of new treatments.

Populational aging is marked by chronic non-communicable diseases, such as metabolic syndrome (MetS). IL-10 and IL-1β are pleiotropic cytokines with multiple biological effects linked to metabolic disorders. This cross-sectional study assessed 193 participants' IL-10 and IL-1β serum levels regarding their role in developing MetS, clinical characteristics, and their IL1B rs1143627 and IL10 rs1800890 variants' genotype frequencies in a population over 60. IL-10 levels correlated weakly with HDL levels and fat mass and inversely with triglycerides, glucose, glycated hemoglobin, and estimated average blood glucose levels. IL-10 levels were also indirectly influenced by the patient's T2DM duration, lean mass amount, and bone mineral content. Participants with altered HDL, elevated serum glucose, raised HbA1c levels, or those over 80 had reduced serum IL-10 levels compared to those with normal levels or other age groups, respectively. Women also had higher serum IL-10 levels than men. Dissimilarity, IL-1β levels correlated directly only with the number of total leukocytes and segmented neutrophils, showing only significant variations with self-reported alcohol consumption. Our study also found that those with the IL10 AA genotype (lower IL-10 levels) had a significantly higher risk of developing MetS. These findings may help direct future research and more targeted therapeutic approaches in older adults.

Water footprint has been widely used to illustrate the consumption of water in many situations; for instance, in products, processes or regions of interest. In this work, we analyzed – using a sensitivity analysis approach – the effect of some variables in the calculation of the water footprint in the viticulture in Brazilian Serra Gaúcha (the major producing region of Brazilian Wine).The classical Penman-Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering fraction in the vineyard, maximum temperatures for some months, altitudes and latitudes of the site). The sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to the evapotranspiration is more sensitive to the values of mulch covering fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

The aim of this manuscript is to synthesize and characterize novel silylated Sm3+ and Eu3+ complexes and incorporate them into mesoporous silica nanoparticles (MSNs) and dense silica nanoparticles (DSNs). Sm3+ and Eu3+ ion complexes were chosen for their strong UV-visible emission, deep-red luminescence, and effective interaction with bidentate organic ligands, such as 2-thenoyltrifluoroacetone (tta) and 1,10-phenanthroline (phen). Silylated ligands were employed as coupling agents to form covalent bonds between the complexes and an oxide matrix via a conventional sol-gel process. This approach aims to enhance the thermal stability and photophysical properties of the resulting hybrid material compared to free complexes. The new silylated-bidentate ligand complexes, using tta and phen as organic ligands and 3-chloropropyltriethoxysilane as the silylating agent, were characterized by FTIR, luminescence spectroscopy, and luminescence decay. After incorporation into silica nanoparticles, the hybrid materials were characterized by SEM, TEM, DLS, zeta potential, SAXS, porosity, surface area measurements, luminescence spectroscopy, and MTT assay. The results confirm the successful synthesis of new luminescent silylated complexes with high luminescence intensity and their incorporation into silica nanoparticles.

Latin America and the Caribbean offer the most favorable natural and social conditions for the emergence of cattle ranching that regenerate and maintains the ecosystem services on which life on Earth depends, that is energy efficient and that contributes to meeting fundamental human food needs. Unlike other major global producers, cattle farming in Latin America and the Caribbean is fundamentally based on pasture. Therein lies both its strength (due to the regenerative potential of well-managed pastures) and its vulnerability (due to the pervasive link between deforestation and pasture formation, especially in the Amazon, where 40% of the Brazilian herd is concentrated). The diversification of pastures (today marked by impressive monotony, with brachiaria accounting for 85% of the species in Brazil), the introduction of legumes and tree plantations within them, and the proper management of herds, open the way to the possibility of drastically reducing the area currently occupied by the activity (especially in the Amazon) while maintaining the supply of its products. Agricultural research has disseminated forms of moderate intensification of cattle ranching, which represent a low opportunity cost of animal feed and, at the same time, regeneration of ecosystem services that have so far been destroyed by the prevailing methods of farming.

Tryptophan, an essential amino acid in mammals obtained from the diet, is influenced by maternal diet, which impacts early life and development of offspring. This is studied under the Developmental Origins of Diseases and Health (DOHaD) concept, so factors from conception to early childhood affect health and disease susceptibility. Tryptophan is metabolized mainly through two pathways: serotonin (5-HT) and kynurenine. The kynurenine pathway, active in the brain, gut, liver, and placenta, breaks down over 95% of tryptophan and plays roles in inflammation, neurotransmission, immune responses, and immune modulation during pregnancy. The serotonin pathway uses up to 5% of Tryptophan, mainly in the gut, adipose tissues, pancreatic cells, and central nervous system, and regulates responses to environmental changes, including sleep, cognition, and feeding behavior. Key enzymes in these pathways include Trp-2,3-dioxygenase (TDO), indoleamine-2,3-dioxygenase (IDO) type1 (IDO1), and type 2 (IDO2) (kynurenine pathway), and tryptophan hydroxylase type 1 (TPH1) and type 2 (TPH2), serotonin pathway. The fetus-placental unit manages tryptophan metabolism. Serotonin and kynurenine are crucial for placental health and fetal development. Serotonin adjusts placental blood volume and aids neurodevelopment. Kynurenine metabolites protect the fetus from maternal immunity and offer initial neuroprotection. At birth, infants switch from placental nutrients to breast milk, rich in tryptophan and protective bioactive molecules. Tryptophan, solely from breast milk, is crucial for infants. Its levels are high in newborns (first three weeks, 2-4 times higher than in adults), gradually declining to adult levels by the fourth week. The highlights the importance of tryptophan for the serotonin and kynurenine pathways in fetuses, newborns and babies applied to the DOHaD concept.

This study investigated the effects of fish oil (FO) treatment, particularly enriched with eicosapentaenoic acid (EPA), on obesity induced by a high-fat diet (HFD) in mice. The investigation focused on elucidating the impact of FO on epigenetic modifications in white adipose tissue (WAT) and the involvement of adipose-derived stem cells (ASCs). C57BL/6j mice were submitted to a control diet or a HFD for 16 weeks, treated (or not) with FO for the last 8 weeks. WAT was removed for RNA and protein extraction, while ASCs were isolated, cultured, and treated with Leptin. All samples were analyzed using functional genomics tools, including PCR-array, RT-PCR and Western Blot assays. Mice receiving HFD displayed increased body mass, fat accumulation, and altered gene expression associated with WAT inflammation and dysfunction. FO supplementation attenuated these effects, a potential protective role against HFD-induced obesity. Analysis of H3K27 revealed HFD-induced changes in histone, which were partially reversed by FO treatment. The study further explored leptin signaling in ASCs, suggesting a potential mechanism for ASC dysfunction in the leptin-rich environment of obese WAT. Overall, FO supplementation demonstrated efficacy in mitigating HFD-induced obesity, influencing epigenetic and molecular pathways, and shedding light on the role of ASCs and leptin signaling in WAT dysfunction associated with obesity.

This paper presents an 8x8 channel optoelectronic readout array (ORA) realized in a 180-nm CMOS process for the applications of short-range light detection and ranging (LiDAR) sensors. We propose several circuit techniques in this work including an amplitude-to-voltage (A2V) converter that reduces the notorious walk-errors by intensity compensation, and a time-to-voltage (T2V) converter that acquires the linear slope of the output signals by exploiting a charging circuit, thus extending the input dynamic range significantly from 5 uApp to 1.1 mApp, i.e. 46.8 dB. These results correspond to the maximum detection range of 8.2 meters via the action of the A2V converter and the minimum detection range of 56 centimeters with the aid of the proposed T2V converter. Optical measurements utilizing an 850-nm laser diode confirm that the proposed 8 x 8 ORA with 64 on-chip avalanche photodiodes (APDs) can successfully recover the narrow 5-ns light pulses even at the shortest distance of 56 centimeters. Hence, this work provides a potential CMOS solution for low-cost, low-power short-range LiDAR sensors.

Brain aging causes a wide variety of changes at molecular and cellular levels, leading to the decline of cognitive functions and increased vulnerability to neurodegenerative disorders. The research aimed at understanding the aging of the brain has made much progress in recent decades. Technological innovations such as single-cell RNA-sequencing (scRNA-seq), proteomic analyses, and spatial transcriptomic analyses have facilitated the research on the dynamic changes occurring within neurons, glia, and other cells along with their impacts on intercellular communication during aging. In this review, we introduce recent trends of how neurons and glia change during aging and discuss the impact of the brain microenvironment such as the blood-brain barrier (BBB).

Sporotrichosis’ clinical manifestations, lesions in the cutaneous or subcutaneous tissues, it is caused by dimorphic fungi of the Sporothrix complex, is a cosmopolitan mycosis that affects humans and animals. This study aimed to identify prevalent species in Minas Gerais, Brazil, and characterize virulence profiles and antifungal susceptibility associated with Sporothrix isolates. Isolates were identified using macro and micro-morphological traits as well molecular biology techniques. Antifungal susceptibility was determined using the Minimal Inhibitory Concentration test, while, virulence factors such as growth at different temperatures, biofilm formation, melanin production, and urease activity were assessed. Our findings revealed S. brasiliensis as a predominant species isolated. Urease activity was a crucial factor associated with growth at 50°C in S. brasiliensis isolates, highlighting their high thermotolerance. Furthermore, strains isolated from humans produced a more robust biofilm compare to those from felines. Strains of S. brasiliensis isolated from Felis catus exhibited faster melanin production, decreased susceptible to antifungal agents tested, and superior growth capacity at 50°C to strains isolated from humans. Therefore, our study highlights the prevalence of S. brasiliensis causing sporotrichosis in both human and felines. However, it underscores the circulation of different strains, with varying levels of virulence and susceptibility to antifungal agents.

Pregnant women (PW) are at a higher risk of diseases and hospitalization from viral respiratory infections, specially influenza, due to cardiopulmonary and immunological changes. This study assessed the impact of viral respiratory infections on PW hospitalized with severe acute respiratory infection (SARI). It´s a cross-sectional study with 42 PW and 85 non-pregnant women (NPW) admitted with SARI to two tertiary hospital between January 2015 and December 2019. The rate of virus prevalence, SARI hospitalization, length of hospital stay, oxygen supplementation, intensive care unit (ICU) admission, and death were comparable between PW and NPW. Multivariate analysis showed that PW had a higher rate of viral SARI hospitalizations (OR = 2.37; 95% CI =1.02 - 5.48) compared to NPW, with influenza virus being the most prevalent (aOR =7.58; 95% CI =1.53 - 37.66). Length of hospital stay (aOR = 0.83; 95% CI = 0.73 – 0.95) and admission to the ICU (aOR=0.028; 95% CI=0.004 - 0.25) were lower in PW compared to hospitalized NPW. Respiratory viruses had a greater impact on the frequency of SARI in the group of PW. Although PW had a higher rate of influenza SARI, they had a better outcome than NPW due to the treatment they received.

The Internet of Things (IoT) is a concept based on the integration of various devices and methods of communication to exchange information in order to contribute to the improvement of everyday life, business and environment. This paper focuses on the establishment of a modern Internet of Things platform to ensure secure collection, processing and storage of data, under unexpected events. This platform establishes reliable connectivity which enhance system uptime; the key to success when implementing an IoT strategy. For this purpose, we proposed a hybrid approach combining different connectivity technologies to ensure the availability and smooth operation of the system and the prevention of data loss in unforeseen cases.

In this paper, a new approach to retrieve semantic images based on shape and geometric features of image in conjunction with multi-class support vector machine is proposed. Zernike moment as shape feature is to verify the invariance of objects for silhouette image. In addition, a set of geometrical features is to explore the objects shape using two features of rectangularity and circularity. Then the extracted features are normalized and employed for multi-class support vector machine either for learning or retrieving processes. The retrieving process relies on three main tasks which namely Query Engine, Matching Module and Ontology Manger, respectively. Query Engine is to build the input text or image query using SPARQL language. The matching module extracts the shape and geometric features of image’s objects and employ them to Ontology Manger which in turn inserts them in ontology knowledge base. Benchmark mammals have been conducted to empirically conclude the outcome of proposed approach. Our experiment on text and image retrieval yields efficient results to problematic phenomena than previously reported.

Background: The cumulative exposure of UV radiation can result in dangerous consequences such as skin burns, photoaging, and skin cancer, hence the importance of using photoprotective formulations. Currently, the formulations seek to be more efficient, innovative and reach a wider audience. Many sunscreens are vegan, and have antioxidant substances to ensure additional photochemoprotective action. To evaluate biological, functional, and mechanical characteristics of them on the skin, biometric and rheologic methods can be used. Objective: Evaluate biophysical, rheological and sensorial parameters of Face Care Facial Moisturizing Cream® (P1) and a vegan formulation (P2) by in vitro and in vivo tests. Methods: A UV scan experiment was performed covering the range of 250 to 400 nm. Sun Protection Factor (SPF) was evaluated by Mansur method. Rheological profiles were obtained using a MARS II (Haake®) controlled shear stress and gradient rheometer. Biophysical parameters were analyzed: skin sebum content, hydration level, transepidermal water loss, erythema and melanin level, skin color, and skin pH. The acceptance profile of the formulations by the volunteers was determined using a 9-point hedonic scale and a 5-point purchase intention test. Results: The formulations effectively absorbed light covering the UV radiation spectrum from UVA to UVB. The SPF of P1 was 25.21 and of P2 was 12.10 by in vitro tests. They had also pseudoplastic and thixotropic behavior, it can contribute to better spreadability and to form a protective film. Biometric tests showed an increase in hydration and skin sebum, decreased erythema, and maintenance of skin pH after application of both formulations. The products also had similarly good acceptance and purchase intention by the volunteers. Conclusion: The comparation of a commercialized product and a vegan test version showed very similar rheological and great acceptance profiles. Therefore, the vegan formulation is a good alternative to reach a different market.

This report describes the pathological findings of acute gastric dilatation syndrome in New World nonhuman primates that died suddenly. The animals were kept in a Zoo and were received proper nutritional and sanitary management. The animals were submitted for necropsy, and macroscopic findings included gastric and duodenal dilation with marked mesenteric vascular congestion. Gastric and intestinal contents were sent for microbiological examination, and bacterial colonies were isolated and identified as Escherichia coli, Streptococcus sp., Salmonella sp., Staphylococcus sp., and Cronobacter sakazakii. The antibiotic sensitivity test revealed that these bacteria had different re-sistance patterns to several antibiotics. Cronobacter sakazakii was susceptible to Ampicillin and Norfloxacin and resistant to Penicillin and Clindamycin. Cronobacter sakazakii is an opportunistic pathogen in animals, frequently reported in cases of human’s enterocolitis. Acute gastric dilata-tion, previously described in Old World nonhuman primates, is a cause of sudden death in Neo-tropical primates and should be considered as a differential diagnosis in cases of sudden mortality in these species. Although its pathogenicity in primates has not been elucidated, the isolation of Cronobacter sakazakii from samples suggests its presence in the environment, emphasizing the need for reinforced hygiene measures, especially of food supplied for these animals.

An outbreak of esophagitis and ingluvitis caused by Salmonella Typhimurium in passerines seized from illegal wildlife trafficking is described. This illegal activity causes stress and leads to lowered immunity in the birds. Additionally, inadequate hygiene conditions predispose the birds to diseases such as salmonellosis. Few studies report the occurrence of Salmonella-induced lesions in the esophagus and crop of wild birds; thus, this study aimed to describe the disease in these species, investigate its presence in the environment, and determine the antimicrobial resistance profile. Three birds of the genus Sporophila were necropsied. In the esophagus and crop, yellowish plaques corresponding to necrosis with bacterial aggregates were observed. Salmonella spp. was isolated from these lesions, with genus confirmation via MALDI-TOF. Environmental samples were collected from the enclosures and cages where the animals were quarantined, and the same bacterium was isolated. In two birds, the serotype S. Typhimurium was identified. Antibiograms performed on the strains from the birds and the environment revealed resistance to antibiotics in the classes of Penicillins, Sulfonamides, Aminoglycosides, Monobactams, Tetracyclines, and first and third-generation Cephalosporins. To the authors' knowledge, this is the first report of this agent causing death in Sporophila due to esophagitis and ingluvitis. It is also the first report of salmonellosis in three species of passerines in Brazil. The study underscores the importance of understanding the pathogens circulating in wild animals, especially within the context of One Health.

Abstract: Falls are among the top 10 causes of years lived with disability in people aged 75 and over. Preventive programs like case management (CM) are crucial. Objectives: To evaluate the effects of a multifactorial fall prevention program, based on CM, on physical performance, presence of pain and risk of falls and fractures in elderly people who have suffered falls. Methods: This is a randomized, single-blind clinical trial with parallel groups: intervention group (IG) and control group (CG) composed of 55 older people with a history of falls living in the community. All participants underwent an initial assessment via video call (containing anamnesis, Timed Up and Go test, Falls Risk Score, Short Physical Performance Battery and Clinical Frax). The IG underwent CM, the physical exercise protocol and the cognitive stimulation protocol. The CG was monitored through telephone calls and received general health and falls guidance. Results: No significant results were found in physical capacity, presence of pain, risk of falls and fractures between the intervention and control groups and between assessments. Conclusion: This program was not effective in improving functional performance, but it was important for characterizing pain and the probability of fracture in the next 10 years in this population.

Uterine leiomyosarcoma (LMS) is a rare and aggressive form of uterine cancer, with a poor prognosis and high number of uterine cancer-related deaths. Chimeric Antigen Receptor (CAR) T-cell therapy is an immunotherapy approach to antigen specific of cancer cell, thereby enhancing the immune system's ability to target and destroy cancer cells. This study aims to investigate the potential of targeting differentially expressed genes (DEGs) as a novel therapeutic strategy using CAR T-cell technology in LMS. Genome datasets were obtained from NCBI-GEO database, Of the five datasets, genes that experienced significant overexpression in at least three datasets were categorized as DEGs, and DEGs were evaluated for protein localization, Gene Ontology, bioactivity, interaction, and overexpression. The results identified 25 DEGs, with ten of them expressed on the plasma membrane; four of them—HMMR, KIF20A, PRC1, and ZWINT—were part of the main protein network derived from the 25 DEGs. The dominant bioactivity of these PMGs involves influencing the cell cycle, particularly processes related to chromosome segregation. In conclusion, HMMR, KIF20A, PRC1, and ZWINT are high-potential DEGs as candidates for tumor-associated antigen CAR T-cell therapy. Further research, both in vitro and in vivo, is needed to enhance the validity of these findings.

The LTTD process for highly concentrated crude oil-contaminated soil was studied to evaluate the remediation efficiency. In addition, the environmental impact difference between HTTD and LTTD was studied. OpenLCA software obtained from PE International was used to perform the analyses. The total ADP environmental impact of the HTTD process was 1.63E-04 MJ, whereas the total GWP impact was 4.14E+02 kg CO2-eq. In contrast, the LTTD process exhibits total ADP and GWP environmental impacts of 1.29E-04 MJ and 2.78E+02 kg CO2 eq, respectively. Analysis of the overall environmental impact reduction revealed that replacing the HTTD process with the LTTD process resulted in a total ADP reduction of 3.40E-05 MJ (20.5%) and a GWP reduction of 1.36E+02 kg CO2 -eq (32.9%). It is inferred that the LTTD treatment of contaminated soil can significantly reduce the environmental impact compared with conventional incineration and HTTD processes. Additionally, the LTTD-treated soil with coke or carbonized residue could act like biochar as a soil amendment. Approximately 50% of organic hydrocarbon contaminants are sequestered into coke or carbonized residues on the soil surface. With additional analyses of various oil-contaminated soils, the potential utility of LTTD-treated soils is expected to be maximized.

The use of C-type natriuretic peptide (CNP) in the interaction with the oocyte and in the temporary postponement of spontaneous meiosis resumption has already been well described. However, its action in pre-implantation developmental stage embryos remains to be understood. Thus, our study aimed to detect the presence of the canonical CNP receptor (natriuretic peptide receptor, NPR2) in germinal vesicle (GV), metaphase II (MII), presumptive zygotes (PZ), morula (MO), and blastocyst (BL) stage embryos and, later, to observe possible modulations on the embryos when co-cultured with CNP. In the Experiment I, we detected and quantified the NPR2 on the above-mentioned embryo stages. Following, Experiment II, intended to test different concentrations (100, 200, or 400 nM of CNP) at different times of inclusion in the in vitro culture (IVC; inclusion from the beginning, i.e. day 1, or from day 5). In Experiment III, it was used 400 nM of CNP on day 1 (D1) in the IVC, which did no demonstrate to be embryotoxic, and it showed potentially promising results in blastocyst production rate, when compared to the control. Thus, we analyzed the embryonic development rates of bovine embryos (D7) and hatching kinetics (D7, D8, and D9). Subsequently, morula and blastocyst were collected and evaluated for transcript abundance to competence and quality (apoptosis, oxidative stress, proliferation and differentiation) and lipid metabolism. Differences with probabilities less than P < 0.05, and/or Foldchange (FC) ˃1.5 were considered significant. We demonstrate the presence of NPR2 until the blastocyst development stage when there was a significant decrease in membrane receptors. There was no statistical difference in production rate after co-culture with 400 nM CNP. However, when we evaluated the abundance of morula transcripts, there was an upregulated transcription in ADCY6 (P=0.057), and downregulated transcripts, in BMP15 (P= 0.013), ACAT1 (P= 0.040), CASP3 (P=0.082). In addition, a total of 12 transcriptions in morula presented variation FC ˃1.5. In blastocysts, the treatment with CNP induced upregulation in BID, CASP3, SOX2, HSPA5 transcripts, and downregulation in BDNF, NLRP5, ELOVL1, ELOVL4, IGFBP4, and FDX1 transcripts (FC >1.5). Thus, our study identified and quantified the presence of NPR2 in bovine pre-implantation embryos. Furthermore, 400 nM of CNP in IVC, a concentration not previously described in the literature, modulated some transcripts related to embryonic metabolism and it was not embryotoxic morphologically.

Goat milk is a nutritious food, with high levels of vitamins and minerals. In this context, there is a growing need to improve preservation techniques. Thus, the present work was developed with the aim of drying goat milk using a spray dryer. Chemical, physical, and physicochemical analyses of powdered goat milk and reconstituted goat milk were performed, as well as the characterization of raw goat milk. The statistical model used was the full factorial design 2³ + 3, observing the Pareto Diagram and Analysis of Variance to study the effect of atomization frequency (Hz), inlet temperature (°C), and peristaltic pump rotation (rpm) on the responses for the powdered and reconstituted product. The planning was executed with eleven trials, including nine different trials and three central points. For the analysis of the planning at a 95% confidence level, the Statistica 8.0 software was used. The results showed that atomization frequency and inlet temperature had the greatest effect on the powdered product and the reconstituted food, respectively. Therefore, the importance of drying this food is evident as it offers several advantages for the food industry and consumers.

2019-n-CoV is the seventh coronavirus known to infect humans and cause serious illness, and due to its lethality there was a worldwide mobilization for the development of new drugs and new vaccines for Covid-19. Therefore, the present work aims to carry out a virtual screening of terpenes with potential anti SARS-CoV-2 activity. To perform the ligand-based virtual, a model was constructed regarding the compounds which already showed inhibitory activity of the SARS-CoV-2. The test series corresponded to terpenes isolated in the Fabaceae Family. The compounds that were most likely to activity were subjected to Molecular Docking and the best poses obtained were subjected to Molecular Dynamics (MD) simulations carried. The molecules under study were submitted to in vitro evaluation to assess inhibitory activity of compounds against SARS-CoV-2 in a phenotypic screening model of the virus. The compounds selected by in silico screening corresponded to triterpenoids from the lupane series. In the in vitro test, both demonstrated activity against the SARS-CoV-2 virus. The virtual screening performed managed to identify the compounds with the highest probability of activity, as well as the most stable compounds, and the method used was validated by the in vitro study carried out.

Abstract: (1) Background: After the COVID-19 pandemic, there is increasing concern regarding the immunity of population to SARS-CoV-2 variants, particularly the Omi-cron variant and its sub-lineages. (2) Methods: The study involved analyzing the im-mune response and symptomatology of 27 vaccinated individuals who were subse-quently infected by Omicron sub-lineages. Blood samples were collected for serological analysis, including the detection of IgG antibodies reactive to the Nucleocapsid (N) and Spike (S) antigens of SARS-CoV-2. Additionally, participants were interviewed to assess the intensity of symptoms during the infection. (3) Results: Despite the high levels of anti-Spike IgG observed after vaccination, all participants were infected by Omicron sub-lineages. The most common symptoms reported by participants were fever or chills, sore throat, and cough. Surprisingly, the levels of anti-Spike IgG found prior to infection did not correlate with symptom severity post-infection. However, it was observed that high post-infection anti-Nucleocapsid IgG levels correlates with mild symptoms during the course of the disease, suggesting a potential role for anti-N antibodies in disease resolution. (4) Conclusions: The high levels of IgG anti-Spike resulting from vaccination may not provide complete protection against infection by the Omicron variant. Addi-tionally, our data suggest that anti-Nucleocapsid IgG titers are negatively correlated with the intensity of the symptoms during mild infections. These findings underscore the importance of ongoing surveillance of viral variants and continuous adaptation of vaccination strategies to ensure efficacy against SARS-CoV-2 variants.

Background: Here, we investigated the inflammaging and physical performance in older adults before and after the COVID-19 pandemic. Methods: Women(n=18) and men(n=7) older were evaluated before the COVID-19 pandemic(PRE), 12 months after the lockdown(POST), and 10 months after resuming exercise training(POS-TR). Physical tests [gait speed(GS) and timed-up-and-go(TUG)]; muscle strength (handgrip-HG), and serum cytokine levels were assessed. Results: The older women showed higher GS and TUG at POST than PRE and POST-TR, besides lower HG at POST-TR than PRE, whereas older men exhibited lower HG at POST and POST-TR than PRE. Both groups presented (1)lower IL-10 and IL-12p70 values in contrast to higher IL-6/IL-10 and IL-8/IL-10 ratios at POST than PRE; (2)higher IL-10 values and lower IL-8/IL-10 ratio at POST-TR than POST; (3)higher IL-12p70/IL-10 ratio at POST-TR than PRE and POST. Particularly older women showed: (4)lower IL-6 values at POST and POST-TR than PRE; (5)lower IL-8 and IL-10 values at POST-TR than POST; (6)and higher TNF-α/IL-10 and IFN-γ/IL-10 ratios at POST than PRE and POST-TR. Significant correlations between the variables were found in both groups. Conclusion: During the COVID-19 pandemic, detraining and resumption of exercise training promoted distinct alterations in physical capacity and inflammaging among older women and older men population.

Abstract: Efforts for carbon emission reduction have been identified as a major climate change mitigation target in the Paris Agreement. The 26th Conference of Parties to the United Nations Framework Convention on Climate Change highlighted the worldwide effort for cleaner and greener mining, urging the mineral industry to embrace greener operational emissions reduction technologies and strategies. Ghana pledged at the Paris Conference to reduce CO2 emissions by 15% and increase renewable energy share to 10% of the national energy mix. Here the fundamental question is how Ghana’s mining industry can contribute to CO2 emission reduction efforts. This paper investigates barriers to low-carbon emission policy implementation among mining companies in Ghana. The questionnaire survey was conducted among Ghana’s four large-scale gold mining companies. As the sample number of available companies is limited, we used a descriptive analysis to assess the responses. The results show that the companies expressed widely different ways to respond to the issues of cost, a government policy/regulations for carbon emission reduction, and the adoption/acquisition of low-carbon technologies. Those companies that are relatively less prepared for this endeavor emphasized the uncertainties of securing sufficient energy in case of using low-carbon technologies. They also expressed a need for more trained workers to handle low-carbon technologies. All companies agreed with a expected high cost of adopting low-carbon options. There was a communication gap with the relevant government ministry about low-carbon mining options where the government should be able to facilitate the involvement of othe low technology providers, such as solar PV installation.

To secure logic circuits against the illegal copying the insertion of the so-called key bit inputs using XOR/XNOR gates has been proposed in . The inserted gates modify (lock) the behavior of the original circuit and only the correct choice of the secret key bits guarantees the equivalent behaviour of the locked circuit to the original circuit. To break this kind of security, i.e. to find the secret key bits, there was a SAT solver based attack proposed in . The aim of this work is to experimentally verify this attack on several locked circuits with various number of primary inputs and outputs and with various number of gates. Except for the random placement of the key gates we evaluate also the positioning with chaining elimination. The obtained and presented results show that we were able to unlock 10 of 11 circuits within few minutes on a common PC.

This study analyzes the eating behavior and factors associated with the presence of disordered eating attitudes in patients undergoing bariatric surgery. It is a cross-sectional, descriptive, and analytical study conducted at a hospital in the Amazon region of Brazil. The Disordered Eating Attitude Scale reduced version (DEAS-s) was used to assess the risk of eating disorders and the Three-Factor Eating Questionnaire (TFEQ-R21) to characterize eating behavior. A total of 205 patients participated, with a mean age of 37.5 ± 8.6 years, the majority being female (93.7%; p&lt;0.001), the mean BMI was 45.3±6.7 kg/m2. It was found that cognitive restraint had the highest mean (52.6±19.9; p&lt;0.001). As for the DEAS-s, the question with the highest mean was "spending one or more days without eating or consuming only liquids to lose weight" (2.80±1.99). Female participants had a higher score in the emotional eating (p=0.016). Disordered eating attitudes showed a correlation with emotional eating and uncontrolled eating. These results suggest that candidates for bariatric surgery may have susceptibility to eating disorders. The importance of a multidisciplinary team in monitoring during the preoperative period is highlighted.

Fingerprints are essential for human identification and are valuable tools in criminal investigations. The pursuit of new materials for digital printing is expanding, with increasing interest in natural compounds such as bixin, sourced from annatto seeds. Despite its traditional use as a natural dye with medicinal properties, the potential of bixin in papilloscopy remains largely untapped. In this study, we meticulously extracted bixin from annatto seeds and meticulously developed composites incorporating zinc carbonate (bixin/ZnCO3) and kaolinite (bixin/kaolinite). UV-visible spectroscopy was used for characterization, and the extracted bixin showed absorption peaks at 429, 453, and 481 nm, which were very similar to standard peaks at 429, 457, and 487 nm. The two samples also had the same retention times (7.07 min) according to further liquid chromatography analysis. Sweat pores were easier to detect thanks to the effectiveness of the bixin/ZnCO3 and bixin/kaolinite composites in creating high contrast sebaceous and natural latent fingerprints. These results highlight the composites’ potential as novel and fascinating instruments for papilloscopy applications, which might also improve forensic investigations.

This study analyzed methodologies for identifying β-casein genotypes in dairy cows and the purity of A2 fermented milk. The research was justified by the need for quick and accurate techniques for herd screening and A2 milk quality control. The methodology included the development of an ELISA test to detect β-casein A1 in fermented milk and the evaluation of a commercial Lateral Flow Immuno Assay (LFIA) to identify A2A2 genotypes and the purity of A2 milk. The results showed that the ELISA test had high sensitivity and specificity (100%) in detecting β-casein A1. The LFIA test was 100% effective in identifying A2A2 genotypes and verifying the purity of A2 milk with a minimum detectable contamination of 5% for raw milk and 10% for fermented milk. Compared to traditional genotyping, these methods proved to be highly reliable and more practical for large-scale screening. It was concluded that the ELISA and LFIA were valuable tools for ensuring the quality and authenticity of A2 milk, meeting the demands of producers and consumers for safe and healthy dairy products.

The advancement of mechanization in forestry has increased productivity in the forestry sector, bringing positive and negative impacts that require a deeper understanding for sustainable forest management. This study aimed to apply a simplified instrument for assessing damage and environmental impacts in forest harvesting of commercial eucalyptus plantations, using a combination of methodologies. The methodology used combined interaction networks and impact assessment matrices, carrying out field surveys, transposing them to interaction networks and weighting them through assessment matrices, resulting in environmental indices (ES) for prioritizing actions. The study was conducted on a commercial eucalyptus plantation in the municipality of São Pedro, São Paulo, Brazil. The mechanized harvesting of the area consists of the structure of a module with a mobile unit consisting of a harvester and forwarder. The results indicated that wood transport presented the highest ES, both positive and negative. The most significant negative impacts (ES) were the depletion of water resources and erosion, while the positive impacts included regional development and job creation. The most notable changes, positive and negative, were observed in the physical and anthropic environment, with a lesser impact on the biotic environment.

This study analyzed the risk of falls in the elderly and the safety of the environment in a teaching hospital in Brazil. The Morse Falls Scale was used to stratify the risk of falls in 45 hospitalized elderly individuals, and a checklist was used to analyze the hospital environment. The analysis was based on the chi-square test and multiple regression. The moderate risk of falls was predominant (51.1%). The variable age group (p-value = 0.024) showed statistical evidence of association with the risk of falls. However, the multiple regression analysis showed no difference between the age groups and the risk situation for falls. The hospital wards showed an adequate arrangement of furniture, but some aspects had inadequacies, such as objects in the corridors, non-functional bells in some beds, and inadequacy of the toilet bowls in terms of the recommended height, the absence of non-slip flooring and the support bar in some bathrooms. In conclusion, the moderate risk of falls among the elderly and the adequacy of the hospital environment to technical standards were evident, with the exception of failures in the emergency communication system and sanitary installation.

In this paper the one-to-one correspondences and equivalences between ideals, primals, filters and grills are introduced. It is shown the local functions and the topological spaces induced by them are the same. From this point of view, the topological properties with respect to one topology can be derived from topological properties valid in the corresponding topology.

Cultural recovery of Mucorales from hyphae-laden tissue is poor, and the clinical implications of culture positivity are scarcely studied. Therefore, we compared clinical and histological characteristics of culture-positive and culture-negative histology-proven pulmonary mucormycosis cases among cancer patients. Histology specimens were blindly reviewed by a Thoracic Pathologist and graded on four histopathologic features: hyphal quantity, tissue necrosis, tissue invasion, and vascular invasion. Twenty cases with a corresponding fungal culture were identified; five were culture-positive, and 15 culture-negative. Although no statistically significant differences were found, culture-positive patients were more likely to exhibit a high burden of necrosis and have a high burden of hyphae but tended to have less vascular invasion than culture-negative patients. In terms of clinical characteristics, culture-positive patients were more likely to have acute myeloid leukemia (60% vs. 27%, p=0.19), a history of hematopoietic cell transplant (80% vs. 53%, p=0.31), severe lymphopenia (absolute lymphocyte count ≤500/µL, 100% vs. 73%, p=0.36), and monocytopenia (absolute monocyte count ≤100/µL, 60% vs. 20%, p=0.11). Forty-two-day all-cause mortality was comparable between culture-positive and culture-negative patients (60% and 53%, p=0.80). This pilot study represents the first comprehensive histopathological scoring method to examine the relationship between histopathologic features, culture positivity, and clinical features of pulmonary mucormycosis.

Major factors affecting in vitro immature embryo rescue efficiencies from Prunus persica or P. armeniaca accessions, have been identified along with improving its economic feasibility. Three culture media, based on Woody Plant Medium (WPM) have been used depending on the embryo size. Embryos less than 5-mm-long were cultured in WPM supplemented with 1 mM BAP and 1 mM GA3, while embryos bigger than 5-mm-long were cultured in hormone-free medium, with or without vermiculite. Environmental in vitro culture conditions consisted of three phases, a (I) stratification at 4 C during a 3 to 5-month-long period in the dark, followed by (II) growth of germinated embryos at 14 C for 4-week-long period, with 12h light a day, which favors plantlet development, and finally (III) growth at 24 C, with 16h light a day, until the plantlets were moved to the greenhouse for acclimatization. Germination of smaller embryos, at the end of the phase I, ranged from 82.2% to 22.1%, for apricot and flat peaches, respectively, whereas for bigger embryos the germination varied from 97.4% to 53.2% for the same type of fruits. Embryo germination for peaches and nectarines ranged from 40.1% to 30.3% for smaller embryos, and from 91.9% to 64.0% for bigger embryos. Endo- and exophytic contamination, affecting from 7.4% to 52.9% of cultured embryos depending on the fruit type and conservation conditions, and the capacity to acclimate to soil conditions, ranging from 50.4% to 93.2%, were the two most important factors influencing protocol’s efficiency and economic feasibility. Considering the overall efficiencies, expressed as hardened plants transferred to field plots over clean uncontaminated embryo, values ranged from 55.8% for nectarines, 54.0% for peaches, 45.6% for apricot, and 23.3% for flat fruits. Addition of vermiculite to the culture medium significantly improved plantlet development, avoiding subculture to fresh medium when an extension of the phase III was required before acclimatization. Compared to laboratory glassware, use of food glass containers with air-permeable sealing film, along with vermiculite-containing medium significantly reduced costs, when handling the large number of embryos required for breeding programs.

Background: Gait analysis systems offer invaluable insights for rehabilitation, yet their expense limits clinician access. We developed GAITWell®, a low-cost modular system for capturing spatiotemporal gait variables, and evaluated its measurement properties. Methods: The GAITWell® uses discreet binary sensors on interconnected boards to collect gait data, which is then analyzed using the DBSCAN algorithm on the Cartesian points from sensor outputs. Reliability was assessed using ICC2,1, standard error of the mean (SEM) and Bland-Altman plots. Validity was determined by comparing GAITWell® with the Qualisys Pro-Reflex system. Results: Participated 38 healthy adults, with an average age of 33.2 years (SD 13.0). Correlations between GAITWell® and the Qualisys system ranged from moderate to very high for most gait variables, with the exception of stride length, which demonstrated a low but significant correlation (r = .360, p &lt; .05). The ICC2,1 indicated moderate to good agreement for most gait variables. Stance and double support times, cadence, and base of support exhibited poor reliability, characterized by larger SEM and limits of agreement. Conclusions: Our findings indicate that the GAITWell® system is a promising tool for gait analysis. Future research will enhance sensor accuracy and refine algorithms to improve reliability.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. In contemporary times, the transition from conventional electricity grids to Smart Grids constitutes an area where extensive research is conducted on a global scale. Among the research challenges, the investigation of Deep Transfer Learning (DTL) in the field of electricity load forecasting represents a fundamental effort that imparts generality to Artificial Intelligence applications, due to new capabilities, such as knowledge transfer and computational power reduction. In this paper a comprehensive study is conducted for day-ahead electricity load forecasting. For this purpose, three Sequence-to-Sequence (Seq2seq) Deep Learning (DL) models are used, namely the Multilayer Perceptron (MLP), the Convolutional Neural Network (CNN) and the Ensemble Learning Model (ELM), which is consisted of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL and emphasizing the way the datasets are trained and fine-tuning for higher forecasting accuracy. In order to implement the forecasting strategies using Deep Learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1-24 hours) for each month of the year for Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. After several trials, four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) contains the training of the DL models using Lesvos dataset, and the testing period performed directly on the Chios dataset without fine-tuning. The fourth strategy is a Multi-task Deep Learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. In this paper, we explore the performance of DTL and compare the results with those produced with MTDL. The results demonstrated that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, the two cases with fine-tuning (DTL Case 1 and 2) outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it has been concluded that the MLP model presents best monthly forecasts with a MAPE of 6.24% and 6.01% for the first two cases, the CNN model presents best monthly forecasts with a MAPE of 5.57% and 5.60% respectively and the ELM model achieves best monthly forecasts with a MAPE of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

The accumulation of brown algae from the genus Sargassum has been increasing over the years in coastal regions of the Caribbean, Africa, Brazil, and Mexico. This causes harmful effects to the ecosystem, health, economy, and climate due to gas emissions from its decomposition process. There is the possibility of this biomass being reused in civil construction, and few studies have been carried out on its application to common Portland cement mortar. As such, the objective of this study is to evaluate the potential of sargassum ash as a mineral addition to partial replacement of fine aggregates in Portland cement mortar. Characterizations of the raw materials were carried out through X-ray fluorescence spectroscopy, loss on ignition, particle size distribution, Brunauer–Emmett–Teller (BET), real density, X-ray diffraction, scanning electron microscopy, and dispersion spectroscopy of electrons. The mortars were prepared by partially replacing the fine aggregate (sand) with sargassum ash, at 0%, 5%, 10% and 20%. Mortar performance was evaluated through water absorption, apparent porosity, apparent specific mass, compressive strength after 7, 28, and 63 days after curing. Lastly, a life cycle assessment in accordance with ISO standards 14040:2006 and 14044:2006. The results showed that replacing sand with sargassum ash increases water absorption, apparent porosity, and decreases the apparent specific mass and compressive strength as replacement increases. Nevertheless, the compressive strength results after 63 days, for 5 and 10% replacement did not differ statistically from reference values. The life cycle assessment indicates that mortars with partial replacement of sand by sargassum ash showed positive environmental impacts when compared to reference values for most categories regardless of the scenario analyzed, especially for mortar with 10% replacement. As such, the use of sargassum ash at 10% does not alter compressive strength values after 63 days while reducing its environmental impact. The application of this biomass in civil construction materials provides a destination for this algae that has been causing several social, environmental, and economic problems.

Fires in houses are inevitable and they cause loss of life and major repair costs for the damage structure and contents. There is a clear need to prevent its ignition and spread to adjacent areas. This article presents the steps taken, namely: contacts, data collection, tests, analysis and conclusions regarding the accident resulting from the outbreak and spread of a fire, on December 25, 2020, in a traditional Portuguese house in Arganil, Portugal. The owners and the court requested to carry out an expert opinion by a person with specialized technical-scientific knowledge, which led to this work as an expert. the work consists of carrying out observations and data analysis. Then, the observed and analysed data were laboratory tested in order to consistently identify characteristics that lead to the ignition and propagation of the fire. This work identified construction problems, materials used and heat sources that are responsible for the accident. The conclusions may be used to avoid future incidents or accidents with houses of same construction type.

Objectives: Evaluate the performance of diagnostic measures (sensitivity and specificity) of the intrinsic capacity (IC) construct recommended by the WHO ICOPE guidelines by domain and by age group. Study design: This cross-sectional study included 164 community-dwelling elderly (95 women and 69 men). The participants completed the assessment of the 5 domains of IC (cognition, psychological, vitality, locomotion and sensory). In addition, they were assessed for activities of daily living (ADL), instrumental activities of daily living (IADL), frailty and urinary incontinence. Main outcomes measures: Descriptive statistics were used to summarize the intrinsic capacity and identify which domains were compromised. Comparisons between three or more samples were made by 1-way ANOVA with Bonferroni post-test and those with two samples by the t-test or Mann-Whitney test for quantitative variables. Chi-square test was used for qualitative variables. The sensitivity and specificity of each domain and each domain by age group were evaluated using the ROC curve. Results: Participants with a decline in IC were older, worse ADL, IADL and mental function, low gait speed, and higher depression scores. In addition, people with a decline in IC had frailty, urinary incontinence. Other issues related to the decline in CI were self-reported non-practice of physical activities and level of schooling.The evaluation of each domain did not prove to be a good screening tool (sensitivity or specificity &lt; 80%, but both values &gt; 50%). When the analyses were separated by domain and age group, good performance was found in the psychological (69-69 years and ≥80 years), vitality (≥80 years) and sensory (≥80 years) domains. Conclusions: The ICOPE screening tool performed well in terms of diagnostic measures when applied by domain and age group.

This article provides a detailed analysis of non-invasive techniques for prediction and diagnosis of faults in internal combustion engines, focusing on the application of the Proknow-C and Methodi Ordinatio systematic review methods. Initially, the relevance of these techniques in promoting energy sustainability and mitigating greenhouse gas emissions is discussed, aligning with the Sustainable Development Goals (SDGs) of Agenda 2030 and the Paris Agreement. The systematic review conducted in the subsequent sections offers a comprehensive mapping of the state-of-the-art, highlighting the effectiveness of combining these methods in categorizing and systematizing relevant scientific literature. The results reveal significant advancements in the use of artificial intelligence (AI) and digital signal processors (DSP) to enhance fault diagnosis, as well as underscore the crucial role of non-invasive techniques in minimizing interference in monitored systems. Finally, concluding remarks point towards future research directions, emphasizing the need to develop digital twins for internal combustion engines and identify gaps for further improvements in fault diagnosis and prediction techniques.

This study aimed to compare the effects of a permanent ischemic stroke model (focal ischemia by thermocoagulation of pial vessels) on sensorimotor function (cylinder test and adhesive removal test), behavioral tasks (novelty habituation memory-open field task), and cerebral infarct size in adult male spontaneously hypertensive rats (SHR) and normotensive Wistar Kyoto (WKY) rats over 42 days after the occurrence of stroke. We observed that the stroke caused asymmetry in the front paws and delayed adhesive removal. These effects were spontaneously reduced in WKY rats, but not in SHR. Short- and long-term novelty habituation memories were abolished by stroke in WYK and SHR. On the 3rd day after stroke, the size of the focal cerebral infarct was the same in WKY and SHR. However, on the 7th day, the infarct size decreased in WKY rats, but not SHR. These results suggested that SAH impairment of sensorimotor recovery in rats subjected to cerebral ischemia could be related to augmented focal cerebral infarct size. Moreover, the behavioral tasks used in this study were unaffected by Systemic Arterial Hypertension. Our results highlight the need for animal models of comorbidities in stroke research.

Among natural disasters, earthquakes have a considerable impact and are among the ten deadliest, with an extreme impact on the healthcare sector. This study aimed to analyze best practices in nursing care for earthquake victims. An in-depth analysis was carried out by using a scoping review, a method used in accordance with the PRISMA-ScR recommendations to identify best nursing practice in these circumstances based on searches of eight databases: MEDLINE via PubMed; Cochrane Library; Embase; VHL; PDQ-Evidence; Scopus; ProQuest; Google Scholar. Twenty-one studies were selected. The nursing practices identified were grouped into two distinct dimensions, each subdivided into four subcategories: (i) care practices: a) immediate care, b) intermediate care, psychosocial care and d) ethical care; (ii) care management and coordination practices, which cover (a) care coordination, (b) victim care network organization, (c) teamwork and (d) training. By analyzing these nursing practices during care and relief operations for earthquake victims, this study identified the various actions carried out, the nursing skills to be developed and the reinforcement of these advanced practices through the systematization of nurses’ skills, in order to promote victims’ rehabilitation, minimize their suffering and improving their quality of life during and after an earthquake.

Titanium dioxide nanoparticles (TiO2NPs) are used in products that are applied to the human body, such as cosmetics and food, but their biocompatibility remains controversial. Pycnogenol (PYC), a natural extract of pine bark, exerts anti-inflammatory and antioxidant effects. In this study, we investigated whether PYC effectively alleviates pulmonary toxicity induced by airway exposure to TiO2NPs, and the beneficial effects of PYC were explained through the analysis of changes to the mechanism of cytotoxicity. TiO2NPs induced pulmonary inflammation and mucus production, increased the levels of malondialdehyde, and upregulated thioredoxin-interacting protein (TXNIP) and cleaved-caspase 3 (Cas3) in the lungs of mice. However, PYC treatment reduced the levels of all toxicity markers of TiO2NPs and restored glutathione levels. These antioxidant and anti-inflammatory effects of PYC were also demonstrated in TiO2NP-exposed human airway epithelial cells by increasing the mRNA levels of antioxidant enzymes and decreasing the expression of TXNIP, cleaved-Cas3, and inflammatory mediators. Taken together, our results showed that PYC attenuated TiO2NP-induced lung injury via TXNIP downregulation. Therefore, our results suggest the potential of PYC as an effective anti-inflammatory and antioxidant agent against TiO2NP-induced pulmonary toxicity.

Wind energy is a kind of renewable energy with great potential for development. This study mainly investigated the application of shock waves generated by high-pressure gases (wind energy) for generating energy. In this study, we designed a new supersonic shock wave generator that can be reused without disassembling and assembling bolts and developed a supersonic shock wave monitoring system. This system monitored the velocity of the generated shock waves, which was Mach 3-5, and the output pressure was more than 100 times higher than the input pressure of the shock wave generator tank.Then, we developed a power generation system driven by supersonic shock waves based on the characteristics of the new shock wave generator, which can generate high-pressure and high-speed blast waves and can be reused. The shock wave generator can repeatedly generate high-pressure waves to drive the Tesla turbine and then the magnetic energy generator for power generation. We established a complete control process for power generation driven by shock waves by controlling the valve stem. The system can detect the tank pressure, output pressure, gas flow, rotation speed, voltage, and current during the control process for the performance analysis of power generation.

The effective macroscopic model describing interactions of a viscous compressible heat-conducting fluid and a two-level fine bristly thermoelastic structure is derived from microstructure by means of the Allaire–Briane multi-scale convergence method. This new model naturally generalizes the isothermal formulation earlier constructed by the authors in 2020, see in Siberian Electronic Mathematical Reports, vol. 17 (https://doi.org/10.33048/semi.2020.17.100). In applications, the established model can be used, for example, in description, with account of heat transfer phenomemon, of airflow near surface of plant’s leaf, in simulation of epithelium surfaces of blood vessels, and in design of biotechnological devices operating in liquids.

The understanding of spatiotemporal variations in reference evapotranspiration (ETo) and its long-term trends is of paramount importance for water cycle studies, modeling, and water resource management, especially in the context of climate change. Therefore, the primary aim of this study is to critically evaluate the performance of various CMIP5 global climate models in simulating the Penman-Monteith reference evapotranspiration and its associated climate variables (maximum and minimum air temperature, incident solar radiation, relative humidity, and wind speed). This evaluation is based on data from nine climate models and 33 automatic meteorological stations (EMA) in the state of Mato Grosso, spanning the period 2007-2020. The statistical metrics used for evaluation include Bias, root mean square error, and Pearson and Spearman correlation coefficients. The projections of the most accurate model were then used to analyze the spatial and temporal changes and trends in ETo under the RCP 2.6, 4.5, and RCP 8.5 scenarios from 2007 to 2100. The HadGEM2-ES model projections indicate static averages similar to current conditions until the end of the century in the RCP 2.6 scenario. However, in the RCP 4.5 and 8.5 scenarios, there is a continuous increase in ETo, with the most significant increase occurring during the dry period (May to September). The areas of the Amazon biome in the north of Mato Grosso exhibit the largest increases in ETo when comparing the observed (2007-2020) and projected (2020-2100) averages. The trend analysis reveals significant changes in ETo and its variables across the state of Mato Grosso in the RCP 4.5 and 8.5 scenarios. In the RCP 2.6 scenario, significant trends in ETo are observed only in the northern Amazon areas. Despite not being observed in all AWSs, the trend analysis of the observed data demonstrates more intense changes in ETo and the existence of the evapotranspiration paradox with an increase in the Cerrado areas and reductions in the Pantanal and southern Amazon areas.

With the advances in industry and commerce, passengers have become more accepting of environmental sustainability issues; thus, more people now choose to travel by bus. Government administration constitutes an important part of bus transportation services as the government gives the right-of-way to transportation companies allowing them to provide services. When these services are of poor quality, passengers may lodge complaints. The increase in consumer awareness and developments in wireless communication technologies have made it possible for passengers to easily and immediately submit complaints about transportation companies to government institutions, which has brought drastic changes to the supply-demand chain comprised of the public sector, transportation companies, and passengers. This study proposed the use of big data analysis technology including systematized case assignment and data visualization to improve management processes in the public sector and optimize customer complaint services. Taichung City, Taiwan was selected as the research area. There, the customer complaint management process in public sector was improved, effectively solving such issues as station-skipping, allowing the public sector to fully grasp the service level of transportation companies, improving the sustainability of bus operations, and supporting the sustainable development of the public sector-transportation company-passenger supply chain.

Sentiment analysis has become an important tool for understanding public opinion across languages and domains. Recently, there has been an increase in the number of studies on sentiment analysis in low-resource languages such as Kazakh. This is important to ensure that modern text analysis technologies are accessible to all users, regardless of their language background. The aim of the study is to create a sentiment analysis model for analyzing texts in Kazakh. As part of this work, we aim to use fine-tuning techniques on our own dataset for already existing models, thus improving their accuracy and efficiency for analyzing Kazakh language texts. This paper presents a manually collected dataset "KazIntTelCom" from the city information service 2GIS, consisting of user reviews, manually annotated by the authors taking into account the polarity of sentiment (i.e. negative positive or neutral). This dataset was used to fine-tune two pre-trained multi-lingual Transformer-based sentiment analysis models taken from the HuggingFace platform. The distillBERT and XLM-RoBERTa models were used for tuning. Also, the models were tested on the dataset "KazSAnDRA". The results show that accurate tuning even on a relatively small dataset gives a significant increase in performance, which is confirmed by an increase in the accuracy index by 20%-30%. In addition, false misses and false detections are analyzed, which allows us to identify directions for further improvement of the models. The contribution of this work, in addition to the dataset, is the analysis of model errors, which will help future developers to make more accurate settings of hyperparameters of training for sentiment analysis in Kazakh. These results are important for natural language processing and their adaptation to low-resource languages, promoting more inclusive and equitable access to modern analytical tools. Thus, this study demonstrates the effectiveness of the Transformers architecture for sentiment analysis in Kazakh and opens new opportunities for further model improvement.

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

Clopidogrel; a prescription drug to reduce ischemic events in cardiovascular patients; has been extensively studied in mostly European individuals but not among Caribbean Hispanics. This study evaluated the low abundance and reduced activity of paraoxonase-1 (PON1) in clopidogrel-resistant patients as a predictive risk biomarker of poor responders and disease severity in this population. Thirty-six patients on clopidogrel (cases; divided into poor and normal responders) were enrolled along with 11 cardiovascular patients with no clopidogrel indications (positive control) and 13 healthy volunteers (negative control). Residual on-treatment platelet function (PRU); PON1 abundance by western blotting and PON1 activity by enzymatic assays were measured. PON1 genotyping and computational haplotype phasing were performed on 512 DNA specimens for two genetic loci (rs662 and rs854560). No statistical differences in mean relative PON1 abundance were found among the groups (p> 0.05). However; a significantly lower enzymatic activity was found in poor responders (10.57 ± 6.79 µU/mL) when compared to controls (22.66 ± 8.30 µU/mL and 22.21 ± 9.66 µU/mL; p= 0.004). PON1 activity among carriers of the most prevalent PON1 haplotype (AA|AA) was significantly lower than in wild-types (7.90 µU/mL vs 22.03 µU/mL; p= 0.005). Our findings suggested that PON1 is a potential biomarker of cardiovascular disease severity in Caribbean Hispanics.

This article introduces a new method for modelling and verifying the execution of timed security protocols (TSP) and their time-dependent security properties. The method, which is both novel and reliable, uses an extension of interpreted systems, accessible semantics in multi-agent systems, and timed interpreted systems (TIS) with dense time semantics to model TSP executions. We enhance the models of TSPs by incorporating delays and varying lifetimes to capture real-life aspects of protocol executions. To illustrate the method, we model a timed version of the Needham-Schroeder Public Key Authentication Protocol. We have also developed a new SMT-based bounded model checking reachability algorithm for the proposed structures and implemented it with the tool. The method comprises a new procedure for modelling TSP executions, a translation of TSP into TIS, and a translation of TIS's reachability problem into the SMT problem. The paper also includes thorough experimental results for nine protocols modelled by TIS and discusses the findings in detail.

The computer program "Histropy" is an interactive Python program for the quantification of selected features of two-dimensional (2D) images/patterns (in either JPG/JPEG, PNG, GIF, BMP, or baseline TIF/TIFF formats) by means of calculations based on the pixel intensities in this data, their histograms, and user-selected sections of those histograms. The histograms of these images display pixel-intensity values along the x-axis (of a 2D Cartesian plot), with the frequency of each intensity value within the image represented along the y-axis. The images need to be of 8-bit or 16-bit information depth and can be of arbitrary size. (Up to 1024 pixels maximum on both sides is recommended as larger images tend to significantly slow the program’s performance). Histropy generates an image’s histogram surrounded by a graphical user interface that allows one to select any range of image-pixel intensity levels, i.e. sections along the histograms’ x-axis, using either the computer mouse or numerical text entries. The program subsequently calculates the (so-called Monkey Model) Shannon entropy and root-mean-square contrast for the selected section and displays them as part of what we call a "histogram-workspace-plot." To support the visual identification of small peaks in the histograms, the user can switch between a linear and log-base-10 display scale for the y-axis of the histograms. Pixel intensity data from different images can be overlaid onto the same histogram-workspace-plot for visual comparisons. The visual outputs of the program can be saved as histogram-workspace-plots in the PNG format for future usage. The source code of the program and a brief user manual are published in the supporting materials as well as on GitHub . Instead of taking only 2D images as inputs, the program’s functionality could be extended by a few lines of code to other potential uses employing data tables with one or two dimensions in the CSV format.

Mercury contamination in the Amazon is a significant public health concern arising from both natural sources and intensive mining activities in the region. This metal is used to separate Au from sediments, forming an amalgam that facilitates the extraction of precious metals. Accordingly, this study aimed to address the following question: What is the impact of mining on mercury contamination in the animal and human populations of the Amazon? The overall objective of this study was to conduct a systematic review of the existing literature to identify gaps in geographic coverage and assess the impact of Hg contamination on human and animal populations in the Amazon region. Our hypothesis was that higher mercury concentrations in tissue samples would be found closer to the mining areas. We employed the PECO and PRISMA-ScR protocols to collect articles published between 2017 and 2023, selected based on criteria representing projected points on a map within the biogeographic boundaries of the Amazon. Mercury concentrations increase with trophic levels, reaching high values of 3.7 µg/g in the muscles of predatory fish and 34.9 µg/g in human hair. The average level of mercury in humans exceeds 6 µg/g, surpassing tolerance levels. Although mining regions show high concentrations of Hg, the highest incidence was observed among populations with fish-based diets. Research on fish is crucial for assessing the risk to human health, given that fish are the main source of protein in the region.

This manuscript presents a new two-parameter unit stochastic distribution, obtained by transforming the Laplace distribution, using generalized logistic map, into a unit interval. The distribution thus obtained is named the Laplace-logistic unit (abbreviated LLU) distribution, and its basic stochastic properties are examined in detail. Also, the procedure for estimating parameters based on quantiles is provided, along with the asymptotic properties of the obtained estimates and the appropriate numerical simulation study. Finally, the application of the LLU distribution in dynamic and regression analysis of real-world data with accentuated "peaks" and "fat" tails is also discussed.

Hazelnut (Corylus avellana L.) is of great agroeconomic importance, prized for its nutritional value and antioxidant properties. Its yield and quality may differ due to factors such as season (S), locality (L) and cultivar (C). Our objective was to determine the dynamics of morphological features and antioxidant properties in nuts of Barcelona and Tonda di Giffoni (TDG) cultivars planted at two localities (Cunco and Perquenco) during three productive seasons (S1, S2 and S3). Barcelona grown in Perquenco in S1 and S3 (less precipitation, more solar radiation), performed better than in Cunco with respect to most morphological nut, kernel and shell features, and the same parameters generally had the same trends for TDG in S2 and S3. Among the seasons at the same localities, the differences between the predominant condition defects for both cultivars were in the decreasing order wrinkled/deformed kernels, double kernels, blank and mold ker-nels, with about 75% of hazelnuts without defect. In both cultivars, the shell had higher antioxi-dant properties that increased in S1 and S3 at both localities, and were consistently higher than in the kernels. Lipid peroxidation was higher in the kernel than shell in both cultivars and local-ities for S1 and S3. The results of this study suggest that the different hazelnut cultivar features including physical and chemical components depend on season and locality, which offers im-portant clues for all those interested in the hazelnut industry.

The seismic events of 2015 in Nepal exposed the challenges associated with constructing earthquake-resistant 2-story self-built stone masonry structures in high-elevation rural regions, especially post-disaster. The Department of Urban Development and Building Construction (DUDBC) provided designs that failed to preserve the local architectural landscape and craftsmanship while adding unfamiliar knowledge and technology. To address the challenges related to architecture, structure, and socio-economic concerns in designing and implementing post-earthquake houses, a need-based building development framework is required for standardized practice. This study presents a novel framework to assess, design, and implement a self-built house after a disaster, consisting of five stages: 1) post-disaster problem assessment, 2) need identification, 3) material selection, 4) design development, and 5) validation and implementation. Based on this framework, we proposed a novel gabions building construction technology for two-story stone masonry structures that effectively mitigate post-disaster challenges such as logistic, resilience, and socioeconomic aspects while improving disaster resiliency specifically in the high-elevation rural areas of Nepal. The proposed two-story gabion building preserves architectural values, enhances structural integrity, and provides cost-effectiveness when compared with its DUDBC peer models while providing much-needed relief to the vulnerable community. The proposed resilient house, G- 2.1, utilizes locally sourced materials and craftsmanship, and innovative gabion technology that ensures affordability and facilitates knowledge transfer. The contribution of this study includes a development framework and a two-story gabions house that is most suitable for self-built post-disaster reconstruction and resilient to local architecture and socio-economic conditions while providing structural integrity and safety.

Several Lung diseases can result in lung damage structural changes, with lung transplantation as a only therapeutic option when the disease reaches an advanced progression, unfortunately, the success of lung transplantation is limited. In this context, lung bioengineering is considered a potential therapeutic alternative and the current approach is based on the use of the natural extracellular matrix (ECM) of the decellularized lung. The use of undifferentiated cells to seed an ECM can be a more practical strategy for lung bioengineering but the sterilization of the organ for this recellularization process is still a challenge. In this sense, Low-Level Laser Therapy (LLLT) can emerge as a potential treatment alternative. Therefore, the objective of this study was to verify if LLLT, can optimize lung recelularization with mesenchymal stem cells by cytokine modulation. For this, we decellularized lungs from C57BL/6 mice perfusing SDS 1% via pulmonary artery, sterilized with LLL (660nm, 100mW, 30s). Whole lung scaffolds were recellularized: (1) Recellularized Lung (2) Recellularized Lung + LLLT. Mesenchymal stem cells derived from human tooth pulp (DPSc) were seeded into lungs and incubated for 24 hours at 37ºC, 5% CO₂, in a bioreactor-like conditions, maintaining CPAP at 20cmH2O, 90% O2, and LLL at wavelength of 808nm, 100mW, 30s. Culture medium were analyzed at 24 hours. H&amp;E, immunostaining, SEM and ELISA assay were performed. We produced viable biological scaffolds absent of DNA content, preserving glycosaminoglycans and collagen I, III and IV, fibronectin, laminin and elastin and lung structure (SEM). The levels of IL-6 and IL-8 were stable during the 24 hours of culture. IFN-γ levels showed significant difference in the DPSc and DPSc+LLLT group in the 24-hour evaluation. The levels of IL-10 decreased after LLLT. These findings highlight LLLT's role in cytokine regulation and contamination prevention during lung recellularization with mesenchymal stem cells. Overall, these results demonstrated that LLLT's modulated cytokine levels and could prevent contamination during the lung recellularization process with mesenchymal stem cells, offering promise for enhanced therapeutic strategies in lung bioengineering.

Traditional population-based metaheuristic algorithms are effective in solving complex real-world problems but require careful strategy selection and parameter tuning. Parameter-less population-based optimization algorithms have gained importance due to their simplicity and efficiency. However, research on their applicability for solving large systems of nonlinear equations is still incipient. In this paper, a systematic review and detailed description of metaphor-less optimization algorithms is made, including the Jaya algorithm, the main Jaya variants for continuous optimization problems, with a focus on the Enhanced Jaya (EJAYA) algorithm, the three Rao algorithms, the best-worst-play (BWP) algorithm, and the very recent max-min-greedy-interaction (MaGI) algorithm. This paper also discusses a few previous parallelizations of the Jaya optimization algorithm, as well as recent GPU-based massively parallel implementations of the Jaya, Enhanced Jaya (EJAYA), Rao, and BWP algorithms developed by the authors. In addition, a novel GPU-accelerated version of the MaGI algorithm is proposed. The GPU-accelerated versions of the metaphor-less algorithms developed by the authors were implemented using the Julia programming language and tested primarily on GeForce RTX 3090 and NVIDIA A100 GPUs, as well as on Tesla V100S and Tesla T4 GPUs, using a set of difficult, large-scale nonlinear equation system problems. The computational experiments carried out produced quite significant speedups, which highlights the efficiency of the GPU-based versions of the metaphor-less algorithms developed for solving large-scale systems of nonlinear equations.

As the global population ages, the prevalence of neurodegenerative diseases has become a major public health concern. The adoption of a less sedentary lifestyle has been shown to have a beneficial effect on cognitive decline, but the molecular mechanisms responsible are less clear. Here we provide a detailed analysis of the complex molecular, cellular, and systemic mechanisms underlying age-related cognitive decline and how lifestyle choices influence these processes. A review of the evidence from animal models, human studies, and postmortem analyses emphasizes the importance of integrating physical exercise with cognitive, multisensory, and motor stimulation as part of a multifaceted approach to mitigating cognitive decline. We highlight the potential of these non-pharmacological interventions to address key aging hallmarks—such as genomic instability, telomere attrition, and neuroinflammation—and underscore the need for comprehensive and personalized strategies to promote cognitive resilience and healthy aging.

This study investigates the land surface temperature (LST) distribution from thermal infrared data for analyzing the characteristics of surface coverage using the Vegetation-Impervious-Soil (VIS) approach. A set of ten images, obtained from Landsat-5 Thematic Mapper, between 2001 and 2010, were used to study the urban environmental conditions of 47 neighborhoods of Porto Alegre city, Brazil. Porto Alegre has had the smallest population growth rate of all 27 state capitals in the last two decades in Brazil, with an increase of 11.55% in inhabitants from 1,263 million in 1991 to 1,409 million in 2010. We applied the environmental Kuznets curve (EKC) theory in order to test the influence of the economically-related scenario on the spatial nature of social-environmental arrangement of the city at neighborhood scale. Our results suggest that the economically-related scenario exerts a non-negligible influence on the physically driven characteristics of the urban environmental conditions as predicted by EKC theory. The linear inverse correlation R2 between household income (HI) and LST is 0.36 and has shown to be comparable to all other studied variables. Future research may investigate the relation between other economically-related indicators to specific land surface characteristics.

This research focuses on optimization of coupled solar production and battery storage system to achieve Nearly Zero-Energy Building (nZEBs) based on building load behavior patterns. Moni-toring data from an office building with 120kW peak and 40kW average load consumption equipped with a 160kW solar photovoltaic (PV) system and a 50kW-45kWh energy storage system (ESS). The primary objective is to the primary objective is to analyze collected data from existing system and setup guideline to optimize ESS capacity to maximize clean energy consumption and thus minimize excess production losses, progressing the building toward Nearly ZEB Level 1 status (87.5%-100% energy usage). The study outcome has identified parameter impacts and demonstrates additional optimized cases with various PV and ESS installed capacity. Further analysis is performed based on working day load profile and appliance patterns. This ap-proach aims to learn the usage HVAC, lighting, and electronics, enabling the optimization scheme to be applied to buildings with different load patterns. Additionally, the analysis predicts other building behavior patterns and optimized clean energy system. The findings are then validated against actual data and the results demonstrate good model accuracy, especially daytime predic-tion. These insights also provide guidelines and strategies for further interventions on HVAC operations and adjusting concerned schedules to achieve desired ZEB level.

This code provides computational facilities to simulate current versus time during the charging of Li-ion cells at desire constant voltage by considering multiscale physical phenomena. This code only considers a powder of active materials (at microscale or nanoscale) and a small part of electrolyte around it as a half cell. Then it is extended to a complete cell by applying correct boundary conditions. This code is very useful by modifying code parameters to understand the effect of the complex shape of active materials powder (surface area and powder size), kind of electrolyte, and the applied voltages on the charging response of Li-ion cell. As a summary, a microscale approach to the design of Li-ion cells has been provided via this code.

Interest on psychosocial predictors of doping has been increasing as a way of finding new approaches to reduce use of performance-enhancing drugs. This investigation aimed to test the psychometric properties of an instrument to asses doping attitudes in Brazilian athletes. The PEAS was validated to Brazilian sports through a process of translation, back-translation and content validity assessment, presenting satisfactory evidences based on its content (CVC&gt;0.80). Then, 994 athletes, from different sexes, types of sports and competitive levels, answered to the Brazilian version of the PEAS. Results showed satisfactory evidences of validity based on its response process, internal structure [X2/df = 2.04; RMSEA = 0.032 (0.026-0.038); CFI = 0.96; TLI = 0.95] and reliability (Cronbach’s α, McDonald’s ω, and CR &gt; 0.70). Network analysis was also used to further explore PEAS’s internal structure. Overall results provide support for the adoption of the PEAS for Brazilian athletes and possibly other Portuguese-speaking countries.

Despite recent efforts by the government to combat Food Insecurity (FI), this issue remains a significant problem in Brazil, particularly among populations experiencing social vulnerability, such as Indigenous peoples. This study aimed to assess the prevalence and factors associated with FI in Indigenous families in the state of Alagoas. It was a population-based cross-sectional survey using a probabilistic sample (n=1270 families) representing the 11 ethnic groups present in the state. FI was defined according to the Brazilian Food Insecurity Scale. Factors associated with moderate and severe FI were determined through multivariable analysis using Poisson regression with robust variance adjustment (prevalence ratio – PR and 95% CI). The prevalence of FI was 69.1%, with 29.5% in moderate (23.2%) and severe (6.3%) forms. Factors associated (p<0.05) with moderate and severe FI included: female-headed households; houses made of mud or wood; with ≤4 rooms; head of the household with ≤8 years of schooling; retired or unemployed household head; and total family income <2 National Minimum Wages. The prevalence of FI among Alagoas Indigenous peoples is considerably high and associated with poorer demographic, socioeconomic, and environmental conditions, highlighting the social inequities they face and emphasizing the need for intersectoral public policies to address this scenario.

In the last few years, the agricultural field has undergone a digital transformation, incorporating artificial intelligence systems to make good employment of the growing volume of data from various sources and derive value from it. Within artificial intelligence, machine learning is a powerful tool for confronting the numerous challenges of developing knowledge-based farming systems. This study aims to comprehensively review the current scientific literature from 2017 to 2023, emphasizing machine learning in agriculture, especially viticulture, to detect and predict grape infections. Most of these studies (88%) were conducted within the last five years. A variety of machine learning algorithms were used, with those belonging to the neural network (CNN) standing out having the best results most of the time. Out of the list of diseases, the ones most researched were Grapevine Yellow, Flavescence Dorée, Esca, Downy mildew, Leafroll, Pierce’s, and Root Rot. Also, some other fields were studied which are water management, plant deficiencies, and classification. Because of the difficulty of the topic, it was collected all datasets that were available about grapevines, and it is described each dataset with the type of data (e.g., statistical, images, type of images), along with the number of images where it was mentioned. This work provides a unique source of information for a general audience comprising AI researchers, agricultural scientists, wine grape growers, and policymakers. Among others, its outcomes could be effective at curbing diseases in viticulture, which in turn will drive sustainable gains and boost success. Additionally, it could help build resilience in related farming industries such as winemaking.

Staphylococcus aureus is a gram-negative bacterium that causes superficial and deep infections that can be mild or potentially fatal. Recently, the bacterium has gained significant attention due to the increased incidence of multidrug-resistant (MDR) strains that make treatment with antibiotics dif-ficult. Owing to the MDR strains, alternative therapies such as antimicrobial photodynamic therapy (PDT) have emerged as good options to treat non-systemic infections. PDT combines a photosensi-tiser (PS) with light and oxygen to generate free radicals that destroy bacterial structures such as the plasma membrane, matrix, and genetic material. This systematic review aimed to identify the ef-fectiveness of PDT delivered using different types of hydrogels for treating wounds, burns, and contamination by S. aureus. This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Bibliographic research was conducted using the PubMed, Web of Science, and Scopus databases. Only full articles published in English between 2013 and 2024 from any country of origin (without restrictions) were included. The research was carried out from 15 June 2023 to 15 June 2024 and did not use any automatic biblio-graphic search tool. The articles collected were identified using keywords and selected using in-clusion and exclusion criteria in accordance with the PRISMA protocol. Seven articles were in-cluded that presented a good set of evidence associated with the use of PDT against S. aureus in in vitro and in vivo studies. To conclude, PDT can effectively complement antimicrobial therapy against S. aureus, in the healing of wounds and burns. The effectiveness of this technique depends on the PS used, hydrogel type, and lesion location. Some studies have already demonstrated the ef-fectiveness of PDT but most were in vitro and a few in vivo. Further studies are required to demonstrate the safety and efficacy of PDT delivered via hydrogels in in vivo models of bacterial infection.

Maintaining the optimal microclimate in broiler houses is crucial for bird productivity, yet enabling efficient temperature control remains a significant challenge. This study developed and validated a computational fluid dynamics (CFD) model to predict temporal changes in indoor air temperature in response to variable ventilation operations in a commercial broiler house. The model accurately simulated air velocity and airflow distribution for different numbers of tunnel fans in operation, with air-velocity errors ranging from -0.22 to 0.32 m s-1. The predicted airflow rates through inlets and cooling pads showed good agreement with measured values with an accuracy of up to 108.1%. Additionally, the CFD model effectively predicted temperature dynamics, accounting for chicken heat production and ventilation effect. The model successfully predicted the longitudinal temperature gradients and their variations during ventilation cycles, validating its reliability through comparison with experimental data. The study also explored different variable inlet configurations to mitigate the temperature gradient. The variable inlet adjustment showed the potential to relieve the high temperatures but may reduce overall ventilation efficiency or intensify temperature gradients, which confirms the importance of optimising ventilation strategies. This CFD model provides a valuable tool for evaluating and improving ventilation systems and contributes to enhanced indoor microclimates and productivity in poultry houses.

Global attention to climate change has surged since the advent of the Paris Agreement, intensifying the importance of measuring and managing carbon productivity indicators on a national level. Nevertheless, concerns persist regarding the reliability of such measurements because of inherent discrepancies in implementing and operating national-level carbon productivity indicators, coupled with their inherent uncertainty. This study proposes a multiple regression model to address these issues aimed at refining national-level carbon productivity indicators metrics, accounting for factors such as the gross domestic product and total greenhouse gas emissions by sectors. The objective was to offer insights into enhancing and effectively utilizing current indicators, enabling a more nuanced interpretation of the variation in the carbon productivity indicators across diverse industrial landscapes. This study showed that adjustments of the carbon productivity metrics reflect disparities in emissions across industrial structures, with countries characterized by high emissions from non-service industries showing improving trends. In addition, this paper proposes an auxiliary indicator estimating method for carbon productivity, emphasizing its utility in interpreting productivity indicators within the context of varying industrial compositions across OECD countries. This study underscores the inadequacies of the current national productivity estimating method, pinpointing areas requiring refinement. Specifically, the method for estimating the auxiliary indicator for carbon productivity guarantees enhanced rationality when integrated with current methodologies. Moreover, by elucidating the nuances of industrial structures, this study advocates for more sophisticated approaches to interpreting and managing the productivity indicators tailored to each unique economic landscape of each country. Nevertheless, the limitations stemming from data availability underscore the need for further research, particularly in refining the national-level carbon resource productivity indicators analyses and exploring the thematic productivity variations in greater depth. By addressing these gaps, future studies will contribute to a more comprehensive understanding of national-level carbon resource productivity indicators dynamics and reveal targeted strategies for sustainable development.

The research showsThis paperstudy presents a theoretical model of sustainable tourism in protected areas based on the experiences obtained in fromthrough field work carried out in the Machalilla national park. The units of analysis that compose it and their theoretical–-methodological foundation, and as well as the indications for their practical implementation in the face of the changes imposed by future tourism are exposed. Using the deductive method, the an analysis of the problem and the general theories linked to sustainable tourism in protected areas was carried out., the This involved establishingment of the premises and the developingment of the objectives to reach conclusions on the topic studied. A qualitative investigation is presented that allowed for the integration of contributions from the analyszes carried out for the treatment and processing of information. Among the results, the systematization of theoretical models linked to tourism in protected areas and, from a practical point of view, the application of a sustainable tourism model was were achieved. The results show the relevance of the proposed theoretical model that is proposed as a contribution to the scientific development of tourism. This model highlights, the systemic nature of its components from a new perspective of analysis, which considerings the need for changes as a development factor.

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

This study explored the intricate nexus between public service provision and land use planning, with a specific focus on the conservation of local plant species in Tambon Khao Phanom, Amphoe Khao Phanom, Krabi Province. Rooted in commitment. The research framework encompasses three fundamental activities: resource survey and collection, resource conservation and utilization, and resource development planning for public services, emphasizing land use planning for local plant species conservation. This multidisciplinary approach integrates the one-map method and draws data from the Department of Land Development to formulate a comprehensive land utilization plan that optimizes land use for biodiversity conservation. This study underscores the critical synergy between public service delivery and land use planning, highlighting their collective potential to advance community well-being and sustainable development.

Urinary tract stones can obstruct the urinary tract, resulting in severe lower back pain, blood in the urine, vomiting, and painful urination. While small stones can pass naturally through the urine stream, larger ones necessitate fragmentation with shock wave lithotripsy (SWL) or laser ureteroscopy (URS) to prevent urinary tract blockage. A smart computer program was developed to predict treatment efficacy for removing a stone and potential complications for individual patients based on factors such as age, health conditions, stone details, and treatment instruments, ultimately recommending the most suitable option. Here, I present three case studies and their predicted outcomes. The program is available at: http://peteranoble.com/webapps.html

Human milk (HM) feedings are associated with reduced risks of neurodevelopmental and neurocognitive delays and systemic inflammatory diseases in infants born prematurely and/or at a low birth weight. Utilizing a neonatal piglet model, HM feedings were compared to bovine milk-derived infant formula (IF)-feedings with the aim of understanding the underlying mechanisms involved in reducing these risks. Six male piglets at postnatal-day (PD)2 were randomized into two feeding groups: HM-fed (n = 3) or IF-fed (n = 3) for 28 days. At 3- and 4-weeks-old, piglet memory and learning performance were assessed using novel object recognition (NOR). At PD30 piglets were euthanized, and whole brains were excised, weighed, and preserved for further analyses, and brain microglial morphology, and systemic inflammatory cytokines were quantified. During NOR, HM-fed piglets had significantly more non-novel revisits at both 3- and 4-weeks-old compared to IF-fed piglets. Microglia of HM-fed piglets had longer processes, more branch points, more endpoints, and a larger territorial volume compared to IF-fed piglets at PD30. In a neonatal piglet model, HM-feedings may improve cognition and reduce brain inflammatory response compared to IF-feedings.

Carotenoids are common and diverse organic compounds with various functional roles in ani-mals. Except for certain aphids, mites, and gall midges, all animals acquire necessary carotenoids only through their diet. The House fly (Musca domestica) is a cosmopolitan pest insect that populates diverse habitats. Its larvae feed on organic substrates that may vary in carotenoid composition according to their specific content. We hypothesized that the carotenoid composition in the adult House fly’s body would reflect its composition in the larval feed. We reared house fly larvae on substrates that differed in carotenoid composition and characterized the carotenoid composition of the emerging adults. Our results indicate that the carotenoid composition of adult House flies is related to the carotenoid composition in its natal substrate, but does not directly reflect it. We suggest that these findings may be developed to identify sources of House fly in-festations. We also recommend that care should be taken when considering the rearing substrates of House fly larvae used for animal feed.

The work aims to develop an electromagnetic shielding measurement methodology and design an electromagnetic shielding effectiveness test setup for thin sheet material testing. In brief, multiple shielding effectiveness standards are described and analyzed in the thin sheet material testing framework. Two types of the developed shielding effectiveness test probe in high and low-frequency ranges are validated comprehensively. Extensive descriptions and data of developed testing methodology are also reported.

Background: Pharmacists need up-to-date knowledge and decision-making support in HIV care. We aim to develop MARVIN-Pharma, an adapted artificial intelligence-based chatbot initially for people with HIV, to assist pharmacists, considering evidence-based needs. Methods: From Dec 2022 to Dec 2023, an online needs-assessment survey evaluated Québec pharmacists' knowledge, attitudes, involvement, and barriers relative to HIV care, alongside perceptions relevant to the usability of MARVIN-Pharma. Recruitment involved convenience and snowball sampling, targeting National HIV and Hepatitis Mentoring Program affiliates. Results: Forty-one pharmacists (28 community, 13 hospital-based) across 15 Québec municipalities participated. Participants perceived their HIV knowledge as moderate (M=3.74/6). They held largely favorable attitudes towards providing HIV care (M=4.02/6). They reported a “little” involvement in the delivery of HIV care services (M=2.08/5), most often ART adherence counseling, refilling, and monitoring. The most common barriers reported to HIV care delivery were a lack of time, staff resources, clinical tools, and HIV information/training, with pharmacists at least somewhat agreeing that they experienced each (M≥4.00/6). On average, MARVIN-Pharma's acceptability and compatibility were in the ‘undecided’ range (M=4.34, M=4.13/7, respectively), while pharmacists agreed to their self-efficacy to use online health services (M=5.6/7). Conclusion: MARVIN-Pharma might help address pharmacists' knowledge gaps and barriers to HIV treatment and care, but pharmacist engagement in the chatbot’s development seems vital for its future uptake and usability.

With the rapid developments that have been made in computer vision and computer technology, semantic segmentation of high-resolution images has emerged as a mainstream and challenging task. The, segmentation of water areas remains a complex task due to the similar features between water areas or between water areas and other land objects. Meanwhile, since coastlines and riverbanks are typically presented in irregular shapes, it is difficult to obtain the categories of pixels at boundaries. This paper proposed the use of a Water-Land Boundary Attention Network (WLBANet) to improve the accuracy of pixels at boundaries between water and land. This network also reinforced the performance of contextual information extraction into distinguishing between water areas and other land objects. To prove the validity of this proposed network, WLBANet was performed on the Water Land Dataset (WLD), which is also by this paper, and which contains water areas and other land objects categories. The results demonstrates that the proposed method has significant effectiveness.

This paper explores the capability of various machine learning algorithms, including Random Forests and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection; following the application of SMOTE and ADASYN sampling methods; and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and ADASYN. The outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post- ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies.

Sustainable supply chain management (SSCM) represents a critical strategy for the electronics industry to balance economic objectives with environmental and social responsibility across complex global production networks. This multiple case study analysis investigates SSCM integration within leading Korean electronics manufacturers Samsung, LG, and Hyundai. In-depth qualitative data, focus groups, and sustainability report analysis reveals the policies, practices, results and implementation barriers related to incorporating sustainability criteria across procurement, manufacturing, distribution and recycling functions. Findings show sustainability considerations increasingly driven by cost, risk, reputational and regulatory factors, with vertical coordination initiatives helping transfer knowledge to assist suppliers on compliance and emissions reductions. Yet substantial obstacles persist around monitoring, coordination, transparency and technical capabilities, especially amongst lower tiers. The paper discusses how common reporting standards, ethical sourcing certification and partnerships can accelerate electronics supply chain sustainability. By mapping integration achievements and gaps, this study informs managerial best practices and policy reforms toward promoting socially and ecologically responsible models for Korea's vital export industry as it navigates rising stakeholder pressures.

This study analyzes future hydrological droughts in LCKW using the SWAT model and data from 2010-2021, evaluating the Streamflow Drought Index (SDI) for 5, 10, 25, and 50-year return periods (RP) for 2029 and 2039 under RCP4.5 and RCP8.5 scenarios. The model shows high accuracy (R² = 0.82, NSE = 0.78). Under RCP4.5, streamflow may increase by 34.74% and 18.74%, while RCP8.5 predicts significant decreases of 37.06% and 55.84%. Historical data reveal frequent short-term droughts during SDI-3, notably in 2014–2016 and 2020–2021, with SDI-6 indicating less frequent but severe droughts, especially in 2015 and 2020. Future projections under RCP8.5 suggest wors-ening drought conditions in 2029 and 2039, with critical drought periods from January to April and November. Wavelet analysis indicates a high risk of severe hydrological drought in LCKW due to low annual recurrence rates, with the highest drought severity over a 50-year RP. The study high-lights an increasing likelihood of frequent and severe droughts, particularly under high emission scenarios such as RCP8.5, which could result in significant water shortages and ecosystem disrup-tions. It emphasizes the significance of adaptive water resource management strategies in mitigating negative impacts on water security and ecosystem health.

This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected was merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port's activities.

Chronic stress, culminating in burnout, evolves gradually and is notoriously difficult to detect due to vague symptoms and individuals' hesitance to acknowledge their struggles. To expedite the identification and recognition of this condition, enabling timely psychological intervention, the integration of Artificial Intelligence (AI) is increasingly considered. This research does not assert the feasibility of an AI system managing all aspects of chronic stress's impact. However, it highlights the capability of current technology to detect stress indicators based on quantifiable data like Heart Rate (HR) and organizational climate dimensions. Although other physiological markers such as oximetry, skin galvanic response, and EKG have been explored, they have not shown reliable differentiation between stress and joy. Focused on the unique context of school principals in the Bedouin regions of Israel, this study investigates the interplay between HR, organizational climate, and stress levels. It introduces a novel application of a fuzzy logic tool that combines HR and organizational climate metrics to aid in stress diagnosis. This tool incorporates the psychologist's expertise to provide real-time data crucial for developing effective coping strategies. While the AI-supported fuzzy system does not replace professional psychological intervention, it significantly enhances the speed of condition identification and intervention planning, thus shortening the response time to stress-related issues in educational leadership within culturally specific settings. The application of such AI-driven tools is pivotal for sustaining the well-being and effectiveness of educational leaders, thereby supporting the broader goal of educational sustainability.

The scope of this work was part of our previous research on Pulsed Power Plasma Stimulation Technique. Electromagnetic fields generated during a pulsed power plasma discharge were simulated using a finite element method in the COMSOL RF module. The field distributions were calculated during and after the pulse. The current paper takes advantage of prior work measuring and modeling the current distribution in the plasma arc. Agreement with laboratory experimental measurements provides support for extension of the model to reservoir scale. The validated model was used to quantify the signal attenuation level in different medium environment.

Session-based recommendations which aim to predict subsequent user-item interaction based on historical user behavior during anonymous sessions can be challenging tasks. Two main challenges need to be addressed and improved: (1) How to analyze these sessions to accurately and completely capture users’ preferences, and (2) how to identify and eliminate any interference caused by noisy behavior. Existing methods have not adequately addressed these issues since they either neglected the valuable insight that can be gained from analyzing consecutive groups of items or failed to take these noisy data in sessions seriously and handled them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths as varying multi-order semantic units to mine the user’s primary intention from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The result of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendation.

This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The proposed semi-supervised learning (SSL) framework uses sparsely labelled user activity events acquired from Inertial Measurement Unit (IMU) sensors installed as wearable device. The objective is to learn classification of multiple classes of human activities in real-time. The proposed cluster-based learning model in this approach is trained with data from the same target user, thus preserving data privacy while providing personalized activity detection services. Two different cluster labelling strategies, namely, population-based, and distance-based, are employed to achieve the desired classification performance. A comparative study of these strategies has been presented in terms of classifiability and classification accuracies. The proposed system is shown to be computationally efficient, which is relevant in the context of limited computing resources on typical wearable devices. The trade-off between classification accuracy and computation complexity is analyzed for different algorithmic hyper-parameters and other system parameters. Extensive experimentation and simulation study have been conducted on a multi-user human activity data from the public do-main in order to validate the proposed learning paradigm.

The Mining Industry has changed significantly in recent decades with the introduction of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML). These innovations contribute to the creation of expert systems that help in optimizing processes, increasing the safety and sustainability of operations. This article is a literature review of modern AI and ML methods and technologies used in the Mining Industry. Discusses various intelligent and expert systems used to improve productivity, reduce operating costs, improve occupational safety, environmental sustainability, machine automation, predictive analytics, quality monitoring and control, and inventory and logistics management. The advantages and disadvantages of different approaches are analyzed, as well as their potential impact on the future of the Mining Industry. The review highlights the importance of integrating AI and ML into mining processes to achieve more efficient and safer solutions.

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

Computer vision is a rapidly developing field that focuses on highly sophisticated picture analysis, manipulation, and comprehension. Its objective is to analyze what is happening in front of a camera and utilize that understanding to control a computer or robotic system or to present users with fresh visuals that are more enlightening or appealing than the original camera images. Computer vision technologies make it feasible for new user interfaces, augmented reality gaming, biometrics, automobile, photography, movie creation, Web search, and many more applications. This essay seeks to explain how computer vision can be utilized to play blackjack successfully.

Background: Leprosy control remains challenging in Brazil and has been aggravated by the Covid-19 pandemic. Objective: To analyze the impact of the Covid-19 pandemic on the epide-miological scenario of leprosy through the detection rate of new cases, risk of illness and hidden prevalence according to high-risk micro-region in Minas Gerais, Brazil. Methods: Ecological study conducted in the health micro-regions, using data on new leprosy cases diagnosed between 2015 and 2023. The annual detection rate of new cases, the Risk of Illness Index and the Hidden Preva-lence were evaluated. The time trend was evaluated by calculating the Annual Percentage Change (APC) of the detection rate, based on segmented linear regression. Results: A reduction in the detection of new cases was identified in most of the micro-regions evaluated. Minas Gerais and some micro-regions showed a positive increase in the variation of the overall detection rate in 2021. The reduction in hidden prevalence in certain micro-regions is linked to the reduction in diagnoses, especially those cases with installed physical disability. Despite the reduction in the risk of becoming ill with leprosy in some micro-regions, Minas Gerais showed an increase in risk. Conclusions: The pandemic has had a considerable and heterogeneous impact on the detection of new cases, as well as on the risk of becoming ill and the hidden prevalence, with repercussions for the control of the endemic in populations. There is a need to implement public health policies that prioritize the early identification of cases and ensure that vulnerable populations are monitored.

Transportation contributes significantly to greenhouse gas emissions, with electrification offering a promising solution. This study evaluates the degradation of State of Health (SOH) over 2.5 years for electrified buses under different charging protocols: DC Fast charging and Flash charging. The vehicles’ models, charging protocols, and financial implications are discussed. High-level simulations reveal that after 2.5 years, the Flash charging vehicle’s SOH reaches 88.93%, while the DC fast charging vehicle reaches 89.46%, showing a 0.47% difference. This slight variance is attributed to Flash charging vehicles avoiding full charge/discharge cycles, reducing battery aging impact. Considering this minimal SOH degradation difference, the choice between DC Fast charging and Flash charging primarily hinges on capital expenditure disparities. Flash charging, with its lower initial battery capacity and reduced capital costs, presents a compelling alternative for city busoperators seeking to electrify their fleets.

Modular inversion on large operands is a time-consuming calculation used in elliptic curve cryptography. Its hardware implementation requires extensive hardware resources such as lookup tables and registers. We investigate state-of-the-art modular inversion algorithms and evaluate the performance and cost of the algorithms and their hardware implementations. We then propose a high-radix modular inversion algorithm aimed at short execution time and low hardware cost. We present a detailed radix-8 hardware implementation based on 256-bit primes in Verilog HDL and compare its cost performance with other implementations. Our implementation on the Altera Cyclone V FPGA chip uses 1227 ALMs (Adaptive logic modules) and 1037 registers. The modular inversion calculation takes 3.67 microseconds. The AT (Area time) factor is 8.30, outperforming other implementations. We also present an implementation of elliptic curve cryptography using the proposed radix-8 modular inversion algorithm. The implementation results also show that our modular inversion algorithm is more efficient in area time than other algorithms.

Pigs are a social species and they stablish hierarchies for a better use of resources and to reduce conflicts. However, in pig production, the opportunities for growing can differ in dominant and subordinate animals. In the present study a system was tested to perform a Dominance versus Subordinate test in growing pigs to investigate how the hierarchy affects feeding behaviour, growth and gut microbiota assessed in faeces. 64 animals housed in eight different pens were used, with four castrated males and four females in each one, weighing 18kg at arrival and maintained during the whole growing period, until 140 kg. Three stool samplings were obtained from the animals directly from the anus to avoid contamination of the faeces on 58, 100 and 133 days after the start of the study to investigate the microbiota composition. The dominant animals had higher gains during the growing period that the subordinate. In addition, they were performing more visits to the feeder throughout the day. Differential abundance patterns were observed in five bacterial genera, Oliverpabstia, Peptococcus, and Faecalbacterium being more abundant in dominant animals and Holdemanella and Acetitomaculum being overrepresented in subordinate ones. This microbial biomarker accurately classified dominant versus subordinate groups of samples with an AUC of 0.92.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

Wearable technology has been proposed as a potential tool to change the way of human life, such as the smart bracelet and the Google Glass. In the wearable technology, the inertial sensor has great significance in tracking the object movements. The paper focused on detecting the movements of user’s finger based on the inertial sensor to give the control signals. Firstly, the attitude matrix, which represented the transformation relation of carrier coordinate system and the navigation coordinate system, was obtained. Secondly, the attitude matrix was expressed based on the quaternions. Thirdly, the finger gesture was processed by the attitude matrix to get the attitude angle. Finally, the robot was controlled by attitude angle to make the moving action. The experimental results showed the detection of the finger movement is effective.

In the intersection of the Internet of Things (IoT) and Machine Learning (ML), the choice between high-level and low-level programming libraries presents a significant dilemma for developers, impacting not only the efficiency and effectiveness of ML models but also their environmental footprint. We have proposed a comprehensive framework to aid in this decision-making process, underpinned by a detailed comparative analysis of both types of libraries on one of the key IoT ML task; image classification. We have introduced a novel algorithm designed to calculate the green footprint of ML model training, factoring in execution time, memory utilization, power consumption, and CPU temperature, addressing the urgent need for sustainable ML practices. Through an empirical evaluation of popular libraries such as PyTorch for high-level and Libtorch for low-level development, we have assessed their performance, development efficiency, and hardware compatibility. The culmination of our research is a decision support system that synthesizes the experimental findings to guide developers toward choices that harmonize model performance with environmental sustainability.

Aging-related disorders pose significant challenges due to their complex interplay of physiological and metabolic factors, including inflammation, oxidative stress, and mitochondrial dysfunction. Curcumin, a natural compound with potent antioxidant and anti-inflammatory properties, has emerged as a promising candidate for mitigating these age-related processes. However, gaps in understanding the precise mechanisms of curcumin's effects and the optimal dosages for different conditions necessitate further investigation. This systematic review synthesizes current evidence on curcumin's potential in addressing age-related disorders, emphasizing its impact on cognitive function, neurodegeneration, and muscle health in older adults. By evaluating the safety, efficacy, and mechanisms of action of curcumin supplementation, this review aims to provide insights into its therapeutic potential for promoting healthy aging. A systematic search across three databases using specific keywords yielded 2,256 documents, leading to the selection of 15 clinical trials for synthesis. Here we highlight the promising potential of curcumin as a multifaceted therapeutic agent in combating age-related disorders. The findings of this review suggest that curcumin could offer a natural and effective approach to enhancing the quality of life of aging individuals. Further research and well-designed clinical trials are essential to validate these findings and optimize the use of curcumin in personalized medicine approaches for age-related conditions.

Examining contemporary developments and cutting-edge approaches in the field, this literature review explores how antenna design for transmission lines is changing. Review findings are compiled from a variety of academic sources, including pertinent technical reports published as recently as 2018, conference papers, journal articles, and reports. A variety of antenna topologies suited for transmission lines are evaluated in this paper, including microstrip, slot, and patch antennas, with a focus on theoretical frameworks and practical implementations. This overview clarifies the complex relationship between antenna design and transmission line characteristics by looking at important factors like impedance matching, bandwidth, and radiation pattern management. Notable developments in materials, fabrication methods, and numerical simulations are also examined and their effects on antenna performance explained. Through a comprehensive analysis of the literature, this review offers valuable insights into the state-of-the-art antenna design practices for transmission lines, facilitating informed decision-making and inspiring future research endeavors in this field.