Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must carefully be evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis, and energy system modelling to optimize decision-making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends signifi-cantly on three factors: 1. the residential electricity consumption patterns that are influenced by the socio-economic composition of the community, 2. the degree of flexibility of i) PUE assets and ii) operational preferences of the PUE user, and 3. the capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy system can reduce the financial risk of operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project´s financial sustainability, and is achieving the overall objectives of decentralized energy system planning.

This paper investigates the efficacy of YOLOv8 variants for vehicle detection and license plate detection within smart parking applications, emphasizing performance under varying ambient lighting conditions. The proposed system is to seize full video frames, extracts regions of interest containing vehicles, and feeds them into separate, pre-trained YOLOv8 models – one dedicated to vehicle detection and another for license plate detection. Four YOLOv8 variants, nano, small, medium, and large, are evaluated. As a pre-processing step, the images are processed with the help of OpenCV and Pillow libraries to adjust the luminosity and increase the images’ DPI so that they would be easy to perceive by the Tesseract OCR engine. Sixteen potential combinations arise from pairing the four YOLOv8 models for vehicle and license plate detection tasks. To identify the most suitable combinations, we employ the Multi-Criteria Decision-Making method, specifically Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. This analysis considers 4 critical metrics: Precision, mean average precision at 95% Intersection of Union threshold, Recall and Total Inference Time. The objective is to achieve an optimal balance between high accuracy and real-time processing. Following the selection of optimal YOLOv8 combinations through TOPSIS analysis, we assess their performance under varying ambient light intensity (measured in lux). This evaluation aims to identify the most robust model combinations that ensure accurate vehicle and license plate recognition across the diverse lighting conditions encountered in real-world environment.

The realization of a perfect absorber A = 1 with transmittance and reflectance T=R=0 by a thin metasurface is one of the hot topics in recent nanophotonics prompted by energy harvesting and sensor applications (A + R + T =1 is the energy conservation). Here we tested optical properties of over 400 structures of metal-insulator-metal (MIM) metasurfaces for a range of variation in thickness of insulator, the diameter of a disc and intra-disc distance experimentally and numerically. Conditions of a near-perfect absorption A > 95% with simultaneously occurring anti-reflection property (R < 5%) were experimentally determined. Differences between the bulk vs. nano-thin film properties at mid-IR of the used materials can be of interest for plasmonic multi-metal alloys and high entropy metals.

Accurate determination of the concentration of alcohols and their metabolites is important in forensics and in several life science areas. A new method has been developed to quantify alcohols and their oxidative products using a headspace gas chromatography- mass spectrometer and isotope-labelled internal standards. This work demonstrates the application of this method, specifically in quantifying the concentration of oxidative products of alcohol and other minor alcohols found in hand sanitizers, which have become an essential household item since the COVID-19 pandemic. Apart from the major component, the minor alcohols found in hand sanitizers include methanol, isopropanol, and n-propanol. In ethanol-based hand sanitizers, a significant amount of acetaldehyde was observed as an oxidation product, while in isopropanol-based hand sanitizers, acetone was observed. The concentration of acetaldehyde in ethanol-based hand sanitizers significantly increased with storage time and temperature, whereas no such increase of acetone concentration was observed in isopropanol-based hand sanitizers with storage time and temperature.

Aiming to improve medical diagnosis and treatment, artificial intelligence (AI) has emerged as a transformational force in the healthcare industry. In this article, we examine how AI is increasingly being used to improve healthcare, with an emphasis on how it is changing medical diagnosis and treatment. We explore the opportunities, threats, and ethical concerns of incorporating AI into healthcare practice. In addition, we feature important AI applications in radiology, pathology, cardiology, and cancer. AI has great potential to improve patient outcomes, diagnostic accuracy, and the ability to provide individualized treatment plans in the future if it is used and refined with care.

Rheumatoid arthritis (RA) is an autoimmune disease in which joints are gradually de-stroyed, and early diagnosis and treatment before joint deformation or destruction is im-portant. The detection of novel RA biomarkers in saliva may facilitate the early detection of RA before the onset of disease. In this study, we conducted a comprehensive proteomic analysis of salivary proteins from RA model mice. Proteins were identified by Western blotting and enzyme-linked immunosorbent assay in serum, saliva, and ankle joints from DBA/1JJmsSlc mice, a model of rheumatoid arthritis. Ankle joints and submandibular glands were hematoxylin and eosin stained and immunostained, and the results were compared with those of control mice. Citrullinated α1 antitrypsin (A1AT, 46 kDa) was commonly detected in saliva, serum, and ankle joints of severe RA model mice, and was confirmed by proteome analysis. Western blotting detected a band corresponding to 46 kDa in serum, saliva and ankle joints, and immunostaining of ankle joints with A1AT an-tibody showed a strong positive signal in the synovium. In DBA/1JJmsSlc mice, not only cyclic citrullinated peptide antibodies but also A1AT may be involved in protein citrulli-nation and contribute to the development and severity of RA.

The search of DNA polymorphisms useful for the genetic improvement of dairy farm animals lasts more than 40 years with relevant findings in cattle for milk traits, where the best combination of alleles for dairy processing have been found in casein genes and in the DGAT1. Nowadays, similar results are not reached yet in river buffaloes despite advanced genomic technologies and accurate phenotype records are available. The aim of the present study was to investigate and validate in a larger buffalo population the effect on six milk traits of four single nucleotide polymorphisms (SNP) in the CSN1S1, CSN3, SCD and LPL genes, previously reported to be associated with or affect dairy traits in smaller populations often belonging to one farm. A total of 800 buffaloes were genotyped. Daily milk yield (dMY, kg), protein yield (dPY, kg) and fat yields (dFY, kg), fat and protein contents (dFP, % and dPP, %), somatic cell count (SCC, 103cell/ml) and urea (mg/dl) were individually recorded in each month for the whole lactation from 2010 to 2021. A total of 15,742 individual milk test day records (2,496 lactations) were available on 680 buffalo cows with 3.6±1.7 parity (from 1 to 13) and 6.1±1.2 test day records per lactation on average. Three out four SNP in CSN1S1, CSN3 and LPL were associated with at least one of analyzed traits. In particular, the CSN1S1 (AJ005430:c.578C&gt;T) gave favorable associations with all yield traits (dMY, p=0.022; dPY, p=0.014 ; dFY, p=0.029) and SCS (p=0.032), whereas the CSN3 (HQ677596: c.536C&gt;T) positively associated with SCS (p=0.005) and milk urea (p=0.04). Favorable effect on dMY (p=0.028), dFP (p=0.027) and dPP (p=0.050) were observed for the LPL. Conversely, SCD did not show any association with milk traits. This is the first example of confirmation study carried out in the Mediterranean river buffalo for genes of economic interest in dairy field and it represents a very important indication for the preselection of young bulls destined to breeding programs in the view of a more sustainable dairy production.

Antibacterial bone cements (ABCs), such as antibiotic-loaded bone cements (ALBCs), have been widely utilized in clinical treatments. Currently, bone cements loaded with vancomycin, gentamicin, tobramycin, or clindamycin are approved by the US Food and Drug Administration. However, traditional ALBCs exhibit drawbacks like burst release and bacterial resistance. Therefore, there is a demand for the development of antibacterial bone cements containing novel agents. In this review, we summarize the latest advancements in ABCs. More importantly, we provide an overview and outlook on novel antibacterial agent-loaded bone cements, including metallic antibacterial agents, pH-switchable antibacterial agents, cationic polymers, N-halamines, non-leaching acrylic monomers, antimicrobial peptides, etc. Offering novel perspectives for designing ABCs and treating bone infections.

The matrix converter converts the input line voltage into a variable voltage with an unrestricted output frequency without using an intermediate circuit, dc link circuit. A pure sine in and pure sine out is the unique feature of the matrix converter. This research paper also analyzes the basic operating principle and the simulation modeling of the direct matrix converter, which is controlled by the Space Vector Pulse Width Modulation technique by using the software which is known as MATLAB/Simulink. The most desirable features in the power frequency changes can be fulfilled by using the matrix converters, and this is the reason for the tremendous interest in the topology. Since the power electronic circuits which is known as the motor drives are used to operate the AC motors at the frequencies other than that of the supply.

Respiratory syncytial virus (RSV) infection is a major cause of severe respiratory disease in infants and young children worldwide. Two vaccines targeting the elderly and pregnant women have been recently approved employing the prefusion form of the RSV-fusion protein (F). However, there are currently no vaccines for infants. Studies have shown a fusion-inactive prefusogenic F form is significantly more immunogenic and produces higher antibody titers to both the prefusion and postfusion structures of the F protein. Here we have developed RSV virus-like particles (RSV-VLPs) consisting of prefusogenic F, RSV glycoprotein and matrix proteins, produced them using the baculovirus expression system, and studied their protective efficacy in Balb/c mice. Morphology and successful assembly of VLPs were confirmed by transmission electron microscopy and western blot. Mice immunized with VLPs developed high levels of serum IgG and neutralizing antibodies as compared with mice immunized with inactivated virus. The VLP vaccine also induced higher levels of IFNγ and IL4, elicited limited proliferation of CD4+ and CD8+ T cells but higher cytotoxic-T-lymphocyte responses. VLP immunization abolished lung pathology in the mice after RSV challenge. Overall, our results indicate that RSV-VLPs consisting of prefusogenic F, glycoprotein and matrix proteins are a potential vaccine candidate against RSV.

Malassezia is a lipid-dependent genus of yeasts known for being an important part of the skin mycobiota. These yeasts have been associated in the development of skin disorders and cataloged as a causal agent of systemic infections under specific conditions, making them opportunistic pathogens. Little is known about the host-microbe interaction of Malassezia spp., and unraveling this implies the implementation of infection models. In this mini review we present different models that have been implemented in the fungal infections study with greater attention in Malassezia spp. infections. These models range from in vitro (cell cultures and ex vivo tissue), to in vivo (murine models, rabbits, guinea pigs, insects, nematodes, and amoebas). We additionally highlight the alternative models that reduce the use of mammals as model organisms, which have been gaining importance in the study of fungal host-microbe interactions. This is due to the fact that these systems have shown to have reliable results, which correlate with those obtained from mammalian models. Example of alternative models are Caenorhabditis elegans, Drosophila melanogaster, Tenebrio molitor, and Galleria mellonella. These are invertebrates that have been implemented in the study of Malassezia spp. infections in order to identify differences in virulence between Malassezia species.

Several recent studies have indicated that miR-30a plays critical roles in various biological processes and diseases. However, the mechanism of miR-30a participation in the regulation of idiopathic pulmonary fibrosis (IPF) is ambiguous. Our previous study demonstrated that miR-30a may function as a novel therapeutic target for lung fibrosis by blocking mitochondrial fission, which is dependent on dynamin-related protein-1 (Drp-1). However, the regulatory mechanism between miR-30a and Drp-1 has yet to be investigated. In addition, whether miR-30a can act as a potential therapeutic has not been verified in vivo. In this study, the miR-30a expression in IPF patients was evaluated. Computational analysis and a dual luciferase reporter system assay were used to identify the target gene of miR-30a, and cell transfection was used to confirm this relationship. Ten-eleven translocation 1 (TET1) was validated as a direct target of miR-30a, and the transfection of miR-30a mimic/inhibitor significantly reduced/increased the expression of TET1 protein. Further experiment verified that the interference on TET1(siRNA) could inhibit the hydroxymethlation of the Drp-1 promoter. Finally, miR-30a agomir was designed and applied to identify and validate the therapeutic effect of miR-30a in vivo. Our study demonstrated that miR-30a could inhibit the TET1 expression by base pairing with complementary sites in the 3′ untranslated region to regulate the hydroxymethlation of the Drp-1 promoter. Furthermore, miR-30a could act as a potential therapeutic target for IPF.

The 70/30 copper–nickel alloy is mainly used in critical parts with more demanding conditions in marine settings. There is a need for innovative methods that offer fast production and cost-effectiveness to supplement current copper–nickel alloy manufacturing processes. In this study, we employ wire-arc additive manufacturing (WAAM) to fabricate the 70/30 copper–nickel alloy. The as-built microstructure is characterized by columnar grains with prominent dendrites and chemical segregation in the inter-dendritic area. The aspect ratio of the columnar grain increases with increasing travel speed (TS) at the same wire feed speed (WFS). This is in contrast to an equiaxed grain structure with a more random orientation of conventional sample. The sample built with a WFS of 8 m/min, TS of 1000 mm/min, and a track distance of 3.85 mm exhibits superior corrosion properties in the 3.5 wt% NaCl solution compared to the conventional one, as evidenced by a higher film resistance and breakdown potential, along with a lower passive current density of the WAAM sample. The corrosion morphology reveals the critical roles played by the nickel element unevenly distributed between the dendrite core and inter-dendritic area.

HIV treatment strategies against viral enzymes are continuously hampered by viral drug resistance. Recent findings show that viral substrate Gag contributes to HIV-1 Protease Inhibitor (PI) resistance, leading to demands for new strategies in HIV treatment where Gag is recognized as a drug target. To successfully target Gag, there is a need of in-depth understanding of the Gag polyprotein and the effects of Gag mutations. Here, we propose new strategies in designing novel Gag inhibitors against existing and novel emerging Gag mutations via a structural understanding of the Gag-Protease relationship in PI resistance. In this review, we discuss the role of both novel and previously reported mutations, revealing insights to how they aid in PI resistance, and how new Gag inhibitors can be designed.

Biochar is a carbonaceous material derived from the thermochemical conversion of biomass under oxygen-lean conditions. Its properties render it many applications in agriculture, environmental remediation, and catalysis. Commonly produced by pyrolysis under a nitrogen atmosphere, the biochar composition is influenced mainly by residence time, heating rate, and process temperature. The existing literature primarily assesses biochar properties, such as ultimate, proximate, calorific, porosity, and combustibility, generated under various thermo-conversion conditions. However, there is a notable lack of sufficient investigation and attention given to the practical application of biochar. This article seeks to bridge that gap by enhancing the comprehension of the necessary experimental assessment for specific biochar applications and proposing process optimization methods using Multi-Criteria Decision Analysis (MCDA). By delving into these aspects, the study offers a concise overview of biochar production and presents a comprehensive panorama based on articles and patents. Additionally, the research explores the physicochemical characterization techniques associated with biochar and its various applications in conjunction with MCDA. This comprehensive approach aims to uncover valuable insights that could further advance the practical and sustainable utilization of biochar in a wide range of fields.

Stress has been identified as a prominent contributor to the development and advancement of bowel cancer, however its specific involvement is intricate and diverse. This abstract tries to offer a concise summary of the correlation between stress and bowel cancer, consolidating significant discoveries from several research fields. The investigation commences by studying the molecular pathways by which chronic stress impacts the development of colon cancer. Chronic stress causes disruption in the functioning of neuroendocrine, immunological, and inflammatory pathways, leading to the development of a microenvironment within the gastrointestinal system that promotes the growth of tumours. Furthermore, changes in the composition of gut microbiota, variations in epigenetics, and alterations in immune function caused by stress also have a role in increasing the risk and advancing the advancement of cancer. Psychological aspects, including how one perceives stress, the tactics they use to cope with it, and the level of psychosocial support they receive, are crucial in determining a person's susceptibility to bowel cancer and impacting the effectiveness of treatment. Stress management therapies, such as cognitive-behavioral therapy and mindfulness-based stress reduction, have demonstrated potential in enhancing patient well-being and promoting adherence to treatment. Moreover, epidemiological studies and clinical trials offer vital knowledge regarding the correlation between stress and the risk, prognosis, and treatment results of bowel cancer. Although the evidence is inconclusive, it is essential to continue researching the intricate relationship between stress and bowel cancer. This will help us understand the connection better and create specific strategies to reduce its impact on patient outcomes.

The high mutation rate of human immunodeficiency virus type 1 (HIV-1) plays a major role in treatment resistance from the development of vaccines to long-lasting drugs. In addressing the crux of the issue, various attempts to estimate the mutation rate of HIV-1 resulted in a large range of 10-5 - 10-3 errors/bp/cycle due to the use of different types of investigation methods. In this review, we discuss the different assay methods, their findings on the mutation rates of HIV-1 and how the location of these mutations can be further analyzed for their potential allosteric effects to reveal potentially new inhibitors with different pharmacodynamics that can be used to circumvent fast occurring HIV drug resistance. Given that HIV is one of the fastest mutating viruses, it is a good model for comprehensive study of its mutations that can give rise to much horizontal understanding towards overall viral drug resistance as well as emerging viral diseases.

Therapeutics antibodies have increasingly shifted the paradigm of disease treatments, from small molecules to biologics, especially in cancer therapy. Despite the increasing number of antibody candidates, much remains unknown about the antibody and how its various regions interact. In fact, the constant region can govern effects that might be useful in reducing the unwanted consequences resulted from systemic circulation. For this reason, apart from the commonly used IgG isotypes, IgA antibodies are promising therapeutics drugs, given its localized mucosal effects. While the antibody Fc effector cell activity has been well explored, recent research has shown evidences that the constant region of the antibody can also influence antigen binding, challenging the conventional idea of region-specific antibody functions. To further investigate this, we analyzed the IgA antibody constant and its allosteric effects onto the antigen binding regions, using recombinant Pertuzumab IgA1 and IgA2 variants. We found mutations in the C-region to reduce Her2 binding, and our computational structural analysis showed that such allosteric communications were highly dependent on the antibody hinge, providing the evidence to consider antibodies as a whole protein rather than a sum of functional regions.

Modern organic synthesis is majorly focused on developing environmentally benign synthetic protocols by employing green chemistry principles. Accordingly, in our recent research work, we herein report the use of modified supramolecular host cyclodextrin as an effective solid based green catalyst for accessing structurally diverse and medicinally relevant pyrimidone architectures. The catalyst and the synthesized compounds 4 (a-r) were characterized using FT-IR, NMR and GC-Mass spectroscopy. Major highlights of the reported work include: atom economical process, extremely milder reaction conditions, operational simplicity, high isolated yields, and excellent catalyst turnover number. The molecular docking studies suggest that the compound 4n has the hydrogen bonding, hydrophobic and π-pair interactions with the active site of active sites of CXT M 15 receptor.

Limited knowledge is available about the association between autistic spectrum disorder (ASD) and precocious puberty. Our study examined the association between the two medical conditions, and the effect modification by sex and neuropsychiatric comorbidities in a nationwide population. To compare the risk of precocious puberty between ASD and non-ASD cases, we conducted Cox regression analysis, using ASD as exposure and time-to-precocious puberty as outcome. We adjusted for sex, attention-deficit/hyperactivity disorder (ADHD), tics disorder, obsessive–compulsive disorder (OCD), anxiety disorder, intellectual disability, and epilepsy. We performed a moderation analysis to examine the potential moderating effects by sex or comorbidities. Patients with ASD were prone to have precocious puberty, with an adjusted hazard ratio (aHR) of 1.80 (95% CI: 1.61-2.01). For the effect modification, sex, specifically females, moderated the association between ASD and precocious puberty, with relative excess risk due to interaction (RERI) 7.35 (95% CI 4.90-9.80). No significant effect modification was found for any of the comorbidities within the scope of additive effect modification. We found that patients with ASD were prone to precocious puberty, regardless of sex or comorbid neuropsychiatric disorders. Girls with ASD are at particularly higher risk to develop precocious puberty.

The agricultural sector is amidst an industrial revolution driven by the integration of sensing, communication, and artificial intelligence (AI). Within this context, the internet of things (IoT) takes center stage, particularly in facilitating remote livestock monitoring. Challenges persist, particularly in effective field communication, adequate coverage, and long-range data transmission. This study focuses on employing LoRa communication for livestock monitoring in mountainous pastures in the north-western Alps in Italy. The empirical assessment tackles the complexity of predicting LoRa path loss attributed to diverse land-cover types, highlighting the subtle difficulty of gateway deployment to ensure reliable coverage in real-world scenarios. Moreover, the high expense of densely deploying end devices makes it difficult to fully analyze LoRa link behavior, hindering a complete understanding of networking coverage in mountainous environments. This study aims to elucidate the stability of Lora link performance in spatial dimensions and ascertain theextent of reliable communication coverage achievable by gateways in mountainous environments. Additionally an innovative deep learning approach was proposed to accurately estimate path loss across challenging terrains. Remote sensing contributes to land-cover recognition, while Bidirectional Long Short-Term Memory (Bi-LSTM) enhances the path loss model’s precision. Through rigorous implementation and comprehensive evaluation using collected experimental data, this deep learning approach significantly curtails estimation errors, outperforming established models. Our results demonstrate that our prediction model outperforms established models with a reduction in estimation error to less than 5dB, marking a 2X improvement over state-of-the-art models. Overall, this study signifies a substantial advancement in IoT-driven livestock monitoring, presenting robust communication and precise path loss prediction in rugged landscapes.

Renal cell carcinoma (RCC) accounts for approximately 90-95% of all kidney cancers in adults, with the clear cell RCC (ccRCC) the most frequently identified subtype. RCC is known for its responsiveness to immunotherapy, making it an area of significant research interest. Immune checkpoint (IC) molecules, which regulate immune surveillance, are established therapeutic targets in RCC. The aim of this study was to analyze the influence of polymorphisms in genes HVEM and CD160 on susceptibility to ccRCC and patient overall survival (OS) with over a ten-year period of observation. We genotyped three HVEM single nucleotide polymorphisms (SNPs): rs1886730, rs2234167, and rs8725, and two CD160 SNPs: rs744877 and rs2231375, in 238 ccRCC patients and 521 controls. Our findings indicated that heterozygosity within rs2231375 and/or rs2234167 increases ccRCC risk. Furthermore, in women, heterozygosity within additional HVEM SNPs, rs8725 and rs1886730, is associated with an increased ccRCC risk. The presence of a minor allele for rs1886730, rs2234167, rs8725, and rs2231375 was also correlated with certain clinical features of the disease. Moreover, the rs1886730 SNP was found to be associated with patients' OS. In conclusion, our study highlights an association between HVEM and CD160 polymorphisms and the risk of developing ccRCC as well as patient’s OS.

Enhancement in chemisorption is one of the active research areas in carbon materials. To remedy the thermally degraded chemisorption at high temperatures, we report a comprehensive study of kink structures in free-standing monoatomic carbon nanowires upon heating. Our Monte Carlo simulation considers multi-monoatomic carbon chains laterally interacted by the Van der Waals force. Our study reveals that the carbon nanowires maintain their linearity regardless of chain length at low temperatures, but this is not the case at high temperatures. Disordered kink structure is observed in the short carbon chains, especially above the Peierls transition temperature. The severe kink structure might increase the possibility of attaching negatively charged atoms, thereby contributing to the development of next-generation materials for chemisorption at high temperatures. We have also provided an important inspection that any physical property of the finite-length carbon chain predicted by ab-initio calculation should reconsider the atomic rearrangement due to the thermal instability at high temperatures.

In this study, we propose a radiomics-clinical probability weighted model for the prediction of prognosis for NSCLC. The model combines radiomics features extracted from RT planning images with clinical factors such as age, gender, histology, and tumor stage. CT images with radiotherapy structures of 422 NSCLC patients were retrieved from The Cancer Imaging Archive (TCIA). Radiomic features were extracted from gross tumor volume (GTV). Five machine learning algorithms, namely decision trees (DT), random forests (RF), extreme boost (EB), support vector machine (SVM) and generalized linear model (GLM), were optimized by a voted ensemble machine learning (VEML) model. A probabilistic weighted approach is used to incorporate the uncertainty associated with both radiomic and clinical features and to generate a probabilistic risk score for each patient. The performance of the model is evaluated using a receiver operating characteristic (ROC). Radiomic model, clinical factors model and combined radiomic-clinical probability weighted model demonstrated good performance in predicting NSCLC survival with AUC of 0.941, 0.856 and 0.949 respectively. The combined radiomics-clinical probability weighted enhanced model achieved significantly better performance than radiomic model in 1-year survival prediction (chi-square test, p<0.05). The proposed model has the potential to improve NSCLC prognosis and facilitate personalized treatment decisions.

This article discusses the relationship between the discourse of evangelical churches and the production of conservative neoliberal political rationalities. We start from the premise that the Large Evangelical Corporations (in Brazil), operate within the neoliberal logic, and offer biographical solutions to social exclusion systems, indicating the individual and spiritual causes for poverty and suffering, while identifying the "deserving" of blessings, suggesting elevation through personal responsibility. In this turn, they merge self-entrepreneur individualism with rhetoric that interweaves elements of conservative communitarianism, such as tradition, naturalizing gender roles, and cultivating traditional family models, seen as key to rehabilitating the wider society. We hypothesize that, in the Brazilian case of the state of Rio de Janeiro, evangelical churches are central parts of this gear, acting as one of the main "resonance machines" (Connolly) of neoliberal policies, providing individualized solutions for structural systems of social exclusion, in a pedagogical, efficient and extensive manner. In this sense, this work benefits from fieldwork and interviews conducted in the state of Rio de Janeiro with its large evangelical churches.

Biodiversity conservation is a key factor in meeting sustainable development goals. This is even more important in cities, where green spaces are becoming increasingly scarce. This study analyzes the Kinshasa's proliferating ornamental plant nurseries, known as informal horticultural sites (IHS). The analysis focused on characterizing the profile of horticulturists, their production conditions and the ornamental species produced. Fifteen IHSs were sampled using the "snowball" technique, and 178 horticulturists were surveyed. Based on the socio-professional profile of the horticulturists, five groups of IHS are distinguished after a hierarchical classification on principal components. We found that IHSs exclusively employed men, most of whom were new to the trade, from all levels of education, and most of whom ranged from 19 to 45 years old. Production conditions are relatively similar from one site to another. However, all IHS are characterized by permanent land insecurity, the use of phytosanitary products, plant conditioning methods that are not very diversified and calibrated to growers' investment capacities, and diversified seed acquisition methods. A total of 139 ornamental species, most of them exotic, were identified. Of these, 37% are phanerophytes and 24% are considered potentially invasive. We suggest ways of professionalizing the activity and protecting the urban environment.

This paper investigates the impact of price and real exchange rate volatility on Foreign Direct Investment (FDI) inflows in a panel of 10 Latin American and Caribbean countries, observed between 1990 and 2012. Both price and exchange rate volatility series are estimated through the Generalized Autoregressive Conditional Heteroscedasticity model (GARCH). Our results, obtained employing the Fixed Effects estimator, confirm the theory of hysteresis and option value, in so far it is found a statistically significant negative effect of exchange rate volatility on FDI. Price volatility, instead, turns out to be positive but insignificant. Moreover, we show that human capital and trade openness are key for attracting foreign capital. From the policy perspective, our analysis suggests the importance of stabilization policies as well as the one of government credibility in promoting trade openness and human capital formation.

Spinal cord injuries (SCI) can result in sensory and motor dysfunctions, which were long-considered permanent. Recent advancement in electrical neuromodulation has been proven to restore sensorimotor function in people with SCI. These stimulation protocols, however, were mostly invasive, expensive, and difficult to implement. In this study, transcutaneous electrical stimulation (tES) was used to restore over-ground walking of an individual with 21 years of chronic paralysis from a cervical SCI. After a total of 66 weeks of rehabilitation training with tES, which included standing, functional reaching, reclined sit-up, treadmill walking and active biking, significant improvement in lower-limb volitional movements and overall light touch sensation were shown by International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) score. By the end of the study, the participant could walk in a 4-meter walking test with the aid of a walking frame and ankle foot orthoses. The success in the sensorimotor recovery of our study participant sheds light on the future of non-invasive neuromodulation treatment for SCI paralysis.

Sechium compositum (Cucurbitaceae) is a wild species that is distributed in the Soconusco region, Chiapas, Mexico, and the border with Guatemala. This species has an intangible biochemical value resulting from the pharmacological relevance of its secondary metabolites. However, as a consequence of the lack of knowledge about its importance, it is being displaced from its habitat at an accelerated rate, incurring the risk of genetic loss. Therefore, an in vitro culture protocol with two experimental phases was evaluated to propagate, conserve, and regenerate this species. The first phases considered the multiplication of seedlings, adding seven concentrations (0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2 mg mL-1) of 6-benzylaminopurine (BA) and thidiazuron (TDZ) and evaluating the number of buds, shoots, and seedling height. The best multiplication response was recorded with 0.1, 0.2, 0.4, and 1.0 mg L-1 of BA and 0.1 mg L-1 of TDZ, as well as the MS base culture medium. With 0.1 mg L-1 of BA, 52-mm height, 1.36 shoots, and 9.22 buds were obtained, surpassing the MS control (MS culture medium alone); likewise, twice more roots (80%) and a 14% reduction in the bud structure were packed in 50 repetitions. The second phase consisted of inducing callus formation from stem and leaf explants through the addition of 0.5, 1.0, and 2.0 mg L-1 of TDZ and 2,4-Dichlorophenoxyacetic acid (2,4-D) to the medium. The results demonstrated that the induction of callogenesis in S. compositum has a better start from the stem in a medium with 2.0 mg L-1 of 2,4-D, whose value was 4.89 —equivalent to 97.8% callus formation around the explant. The addition of 500 mg L-1 of polyvinylpyrrolidone (PVP) is also suggested to reduce oxidation. This protocol represents a significant advance in the conservation, multiplication, and callogenesis of S. compositum and contributes to its rescue and revaluation in the face of the danger of extinction.

Hyperspectral imaging data holds great potential for the different stages of the mining life cycle in active and post-mining environments. However, the technology has yet to reach the stage of large-scale industrial implementation and acceptance. While hyperspectral satellite imagery can achieve high spectral resolution, signal-to-noise ratio (SNR) and global availability with break-through satellite systems like EnMAP, EMIT and PRISMA, limited spatial resolution poses chal-lenges for sectors like mining, which require decimetre to centimetre scale resolution for applica-tions such as reconciliation, ore/waste estimates, geotechnical assessments and environmental monitoring. Hyperspectral imaging from drones (referred to herein as Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the camp/ mine scale, with the capability for frequent, user-defined re-visit times. This has been made possible by the miniaturization of hy-perspectral imaging systems. Collection of data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions enables the detection of different minerals and surface altera-tion patterns potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we provide a review of relevant studies de-ploying hyperspectral imaging in or applicable to the mining sector, especially for the use of hy-perspectral VNIR-SWIR Uncrewed Aerial Systems. Where required, we draw on previous in-sights derived from satellite or ground-based systems. We also discuss UAS survey planning, and sampling considerations for validation and interpretation.

This study investigates the use of programming language Python to analyse and model chemical engineering processes. Using municipal sewage sludge (MSS) drying as an example, the study demonstrates how Python can support analyses and model drying data. The drying process was performed on a flat plate while maintaining a uniform, parallel drying air speed of 1.15 m s-1. The Python program helped to analyse the digitalised weight measurements from each sample. The program enables sorting input data, determining the drying critical point and evaluating the first and second drying periods.

It is well-known that simulation tools are essential for the design and optimization of wireless communication systems. In this paper proposes a python script that can be used for planning and predicting a connection link budget by analyzing its basic parameters. Our proposal consists of an application that calculates the connection budget for point-to-point links operating at 5 GHz, taking into account all the necessary microwave parameters. For validating the efficiency of the proposed tool, the paper presents comprehensive simulation results derived from comparing our tool to a couple of other simulation tools by means of calculating the same parameters.

The invasion of ship domain stands out as a significant factor contributing to the risk of collisions during vessel navigation. However, there is a lack of research on the mechanisms underlying collision risks specifically related to merchant and fishing vessels in coastal waters. This study proposes an assessment method for collision risks between merchant and fishing vessels in coastal waters and validated through a comparative analysis through visualization. First of all, the operational status of fishing vessels is identified. Collaboratively working fishing vessels are treated as a unified entity, expanding their ship domain during operation to assess collision risks. Secondly, to quantify the collision risk between ships, a collision risk index (CRI) is proposed and visualized based on the severity of collision risk. Finally, taking the high risk area of merchant and fishing vessels collision in the Minjiang River Estuary as an example, this paper conduct the analysis involves classifying ship collision scenarios, extracting risk data under different collision scenarios, and visually analyzing areas prone to danger. The results indicate that this method effectively evaluates the severity of collision risk, and the identified high-risk areas resulting from the analysis is verified by accidents occurred in the recent three years.

The fishmeal production industry is essential for providing protein for animal feed in the aquaculture sector. However, the industry faces challenges related to energy consumption and environmental sustainability. This study evaluates the energy efficiency and environmental benefits of waste heat recovery (WHR) technologies in a fishmeal production plant in Vietnam. Data were collected from the plant between 2016 and 2022, and a specific energy consumption (SEC) indicator and a comprehensive methodology were utilized. Implementing an economizer as a WHR technology resulted in a 55.5% decrease in SEC compared to the state before installation. The enhanced energy efficiency also translated to reduced energy consumption per output unit. Moreover, the economizer contributed to annual energy savings of 4,537.57 GJ/year and cost savings of $26,474.49. Additionally, carbon dioxide (CO2) emissions associated with producing one ton of fishmeal decreased by 58.37%. These findings highlight the potential for WHR technologies to improve energy efficiency and reduce the environmental footprint of fishmeal production. The study's results provide valuable insights for practitioners and policymakers in promoting energy efficiency practices and reducing environmental impact in the industry.

Titania is one of the most comprehensively studied nanostructures due to its widespread applications in production of catalytic, gas- sensing and corrosion- resistance materials [1]. M-polynomial of nanotubes has been vastly investigated as it produces many degree-based topological indices which are numerical parameters capturing structural and chemical properties. These indices are used in the development of quantitative structure-activity relationships (QSARs) in which the biological activity and other properties of molecules are correlated with their structure like boiling point, stability, strain energy etc of chemical compounds. In this paper, we determine M-polynomials of single-walled titanium (SW TiO2) nanotubes and recover important topological degree based indices of them to theoretically judge these nanotubes. We also use Maple to plot surfaces associated to different types of single-walled titanium (SW TiO2) nanotubes.

Human tuberculosis (TB) is caused mainly by Mycobacterium tuberculosis (MTB) and M. africanum (MAF) remains a major global health threat. The varying response of different host to contact with the TB bacteria, indicates the importance of host genetics in susceptibility to TB disease. We explored the association among selected human/host genomic variants and disease caused by the two causative pathogens in Ghana through a case control study. MTBC isolates (323) recovered from pulmonary TB patients recruited between 2016 and 2018 were genotyped using spoligotyping. A selection of 29 SNPs from MTB-related genes with high frequency among African populations were genotyped using a TaqMan® SNP Genotyping Assay and iPLEX Gold Sequenom Mass Genotyping Array. Associations between MTBC lineages and host variables were assessed using univariate and multivariate logistic regression. The prevalence of MTB and Maf among the participants were 79% and 21% respectively. Association analysis between the controls and MAF showed that rs2695342 variant on the SIC11A1 gene have the propensity to confer susceptibility to MAF infections (P = 0.093, OR = 8.35, 95% CI = 0.70 – 99.24) whilst the rs17048476 (P = 0.088, OR = 1.57, 95% CI = 0.93 – 2.63) and rs1482868 (P = 0.095, OR = 0.60, 95% CI = 0.33 – 1.09) were observed to be only suggestive. Our findings implicate SLC11A1 as a potential susceptibility gene of substantial interest for TB caused by MAF which is an important pathogen in West Africa and highlight the need for in-depth host pathogen studies in West Africa.

This paper introduces a structured design for an effective power-sharing technique among converter-interfaced distributed generation (DG) units within a microgrid that operates without a synchronous generator. The proposed power-sharing technique leverages the battery energy storage system (BESS) to promptly respond to network changes. The real power output of each commercial distributed energy resources (CDER) unit is governed by a frequency-droop characteristic and a complementary frequency restoration strategy. Deloading techniques are employed by solar and wind power generators to enhance response during power supply or demand disturbances. To ensure microgrid stability, small-signal analysis is conducted for controller design, including the determination of stability margins. Initial tuning of the power-sharing technique parameters is achieved using the Ziegler-Nichols Method, followed by further optimization through a meta-heuristical algorithm to enhance the response time of energy sources. The proposed power-sharing technique's performance is evaluated on a benchmark medium voltage network using industry-standard commercial software. The test results demonstrate the precise and rapid power-sharing capabilities among DGs facilitated by the proposed technique, highlighting its effectiveness in dynamic microgrid environments.

Molecular studies regarding regulatory elements such as small ncRNAs and their mechanisms are poorly understood in infectious diseases. Tuberculosis is one of the oldest infectious diseases of humanity, and it is still a challenge to prevent and treat it. The control of the infection as well as its diagnosis are still complex, and treatments used are linked to several side effects. This study aimed to investigate miRNA’s expression profile to identify possible biomarkers for tuberculosis. We applied NGS techniques to investigate miRNA’s global expression profile from blood samples of infected patients with tuberculosis, their respective healthy physicians, and external healthy individuals as controls. Samples from 22 individuals run through a differential expression, target genes, gene set enrichment, and miRNA-gene network analysis. We observed 153 altered miRNAs, among which, only three DEmiRNAs (hsa-let-7g-5p, hsa-miR-486-3p and hsa-miR-4732-5p) were found between the investigated patients and their respective physicians. These DEmiRNAs are suggested to play an important role in granuloma regulation and their immune physiopathology. Our results propose that miRNAs may be involved in immune modulation, regulating the repertoire of genes expressed in the immune system’s cells. Our findings encourage the application of miRNAs as potential biomarkers for tuberculosis.

Abstract: High step-up voltage gain nonisolated DC-DC converter have attracted much attention in photovoltaic, fuel cells and other renewable energy system applications. In this paper, by combining input current ripple-free boost cell with coupled-inductor voltage-doubler cell, an input current ripple-free high voltage gain nonisolated converter is proposed. In addition, passive lossless clamp circuit is adopted to recycle the leakage inductor energy and to reduce the voltage spike across the power switch. By utilizing voltage-doubler cell consisting of diode and capacitor, the voltage stress of switch is further reduced and the resonance between the leakage inductor and the stray capacitor of the output diode is eliminated. A low switch-on-resistance low-voltage-rated MOSFET can therefore be employed to reduce the conduction loss and cost. The reverse recovery loss of output diode is reduced, and the efficiency of converter can be improved. Furthermore, the proposed converter can achieve nearly zero input current-ripple and make the design of electromagnetic interference (EMI) filter easy. Steady state analysis and operation mode of the converter is performed. Finally, experimental results are presented to verify the analysis results of the proposed converter.

Count data analysis presents unique challenges due to its discrete nature, often exhibiting excessive zeros and overdispersion. To address these complexities, count models, such as Poisson regression and Negative Binomial regression, have been developed, enabling modeling and prediction of count-based phenomena. Additionally, it’s important to notice that zero inflation, a phenomenon commonly observed in count data, requires specialized techniques for robust analysis. This article provides an overview of count data and count models, explores zero inflation, introduces Likelihood Ratio Tests, and explains how the Vuong Test can be used as a model selection criteria. Furthermore, we created a Vuong Test implementation from scratch using the Python programming language. This implementation enhances the accessibility and applicability of the Vuong Test in real-world scenarios, providing a valuable contribution to the academic community, since Python didn’t have an implementation of this statistical test.

In the context of cognitive radio, smart city and Internet-of-Things, the need of advanced radio spectrum monitoring becomes crucial. However, surveillance of a wide frequency band without using extremely expensive high sampling rates devices is a challenging task. The recent development of compressed sampling approaches offers a promising solution to these problems. In this context, the Modulated Wideband Converter (MWC), a blind sub-Nyquist sampling system, is probably the most realistic approach and was successfully validated in real-world conditions. The MWC can be realized with existing analog components and there exist calibration methods which are able to integrate the imperfections of the mixers, filters and ADCs, hence allowing its use in real-world. The MWC underlying model is based on signal processing concepts such as filtering, modulation, Fourier series decomposition, oversampling and undersampling, spectrum aliasing, and so on, as well as in-flow data processing. In this paper we develop an MWC model which is entirely based on linear algebra, matrix theory and block processing. We show that this approach has many interests: straightforward translation of mathematical equations into simple and efficient software programming, suppression of some constraints of the initial model, and providing a basis for the development of an extremely fast system calibration method. With a typical MWC acquisition device we obtained a speed up of a factor greater than 20 of the calibration computation time, compared with a previous implementation.

Curcuma longa is only a rich source of curcumin (1) and its major analogues demethoxycurcumin (2) and bisdemethoxycurcumin (3) among the Curcuma species. The content ratio of these three curcumins in turmeric is depended on the varieties and growing environment. Recently, curcumin (1) has been reported as potential inhibitor of hepatic and enzymatic protein tyrosine phosphatase 1B (PTP1B) and its related disorders such as hypertriglyceridemia, hyperlipidemia and liver steatosis. Thus, we further purified curcumins (1–3) from C. longa and investigated their inhibitory effects against PTP1B and α-glucosidase enzymes. As the result, curcumins (1–3) exhibited potential PTP1B inhibition with IC50 values of 37.8 ± 1.4, 45.3 ± 0.7, and 72.6 ± 1.1 μM, respectively, and α-glucosidase inhibition with similar manner (IC50 values of 78.2 ± 0.2, 82.4 ± 0.6, and 90.6 ± 1.0 μM, respectively). These results reveal a key role of methoxylation in the variation of PTP1B and α-glucosidase inhibitory activity for these curcumins. In addition, density functional theory (DFT) was used accompanying with molecular docking (MD) to analyze the ligand stability and the interaction of curcumins (1–3) with PTP1B and glucoside hydrolase proteins. Assay-based results and the MD data obtained are highly correlation suggesting that the deterioration of the enzyme activity caused by the distortion of structural conformation of PTP1B and glucoside hydrolase may be related to the arrangement of amino acids in protein structure. This reported for the first time that inhibitory effects of curcumins (1–3) against PTP1B and glucoside hydrolase have been examined in vitro and in silico as well.

The current study characterizes the transient M/M/1 queue manifold info-geometrically, through devising Fisher Information matrix(FIM) and its inverse(IFIM). Additionally, the impact of stability on the existence of IFIM and explore the geodesic equations of motion has been revealed. More potentially, the paper discusses some info-geometric applications to Machine Learning are highlighted. Closing remarks with some challenging open problems combined with the next phase of research are given.

Circulating tumor cells (CTC) have shown to be prognostic in advanced colorectal cancer (advCRC), but their value for predicting response to treatment or as a source of molecular data is debated. We compared CellSearch® (Janssen Diagnostics, LLC) and IsoFlux^TM (Fluxion Biosciences Inc, South San Francisco, CA) systems for the enumeration of CTC in patients with newly diagnosed advCRC (group 1; n=34). Using castPCR^TM we studied KRAS status in CTC isolated with IsoFlux^TM and compared it with that of the primary tumor in patients from group 1 and in KRAS wild-type (KRAS^WT) patients with progressive disease (group 2; n=22). Median number of CTC detected with CellSearch® (groups 1 and 2) was 1 (range: 0-78) and with IsoFlux^TM (group 1) was 8 (range: 0-419), showing a modest correlation (r=0.345, P=0.036), which improved if lung metastases (r=0.805, P=0.016) or if lung and liver metastases were present (r=0.812, P=0.05). A Bland-Altman plot showed that the higher the number of CTC detected the larger the difference between both methods in favor of IsoFlux^TM. After a median follow-up since CTC collection of 16 months (range: 1-30) CellSearch® ≥ 3 CTC (HR 2.77, 95% CI 0.77-9.95) and IsoFlux^TM ≥ 11 CTC (HR 4.14, 95% CI 1.05-16.19) were established as the best cutoff points for predicting survival. Using castPCR^TM we found KRAS mutations in CTC in 4 out of 8 patients from group 1 and in 2 out of 3 patients from group 2. None of these mutations were found in the primary tumor using standard methods, possibly reflecting intratumor heterogeneity or treatment selection pressure. We conclude that IsoFlux^TM is more efficient than CellSearch® in the isolation of CTC in patients with advCRC, achieving, in a majority of cases, the established minimum of CTC for castPCR^TM-based genetic analyses.

Late onset Pompe disease is a recessive lysosomal storage disease. Clinical features include skeletal muscles deficiency and diaphragm weakness. Clinical management relies on supportive treatment and mechanical ventilation in patents with chronic respiratory failure. M mode ultrasound and tissue Doppler imaging can be used to assess and to follow up diaphragm function.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

This paper presents a hydrodynamic numerical model for a pitching wave energy converter (WEC). The model uses potential wave theory and is based on Cummins' equation, with nonlinear hydrostatic restoring stiffness and excitation forces based on instantaneous body position and water surface elevation. The numerical model can include non-linear forces, like quadratic drag, power-take-off and other forces that may account for unknown viscous effects observed in experiments. The paper discusses the applicability and limitations of the code and presents the cases where assumptions and simplifications can be made. The goal is to conclude on the simplest, yet accurate, version of the model by evaluating its accuracy using experimental data. The case study for validation is Floating Power Plant's (FPP's) WEC [1]. In the full-scale commercial project, FPP's device consists of a semisubmersible platform hosting a wind turbine (5-8MW) and 4 WECs, each one connected to the platform by a rotation shaft. Due to the configuration of the platform, strong interactions occur between the WECs and the structure, as they are very closely spaced. In order to validate a numerical model able to simulate these hydrodynamic interactions, wave basin experiments with a similar but simplified setup were performed.

High-performance motor drives that operate in harsh conditions require an accurate and robust angular position measurement to correctly estimate the speed and reduce the torque ripple produced by angular estimation error. For that reason, a resolver is used in motor drives as a position sensor due to its robustness. A resolver-to-digital converter (RDC) is an observer used to get the angular position from the resolver signals. Most RDCs are based on angle tracking observers (ATOs). On the other hand, generalized predictive control (GPC) has become a powerful tool in developing controllers and observers for industrial applications. However, no GPC-based RDC with zero steady-state error during constant speed operation was proposed. This paper proposes an RDC based on a second-order difference GPC (SOD-GPC). In SOD-GPC, the second-order difference operator is applied to design a GPC model with two embedded integrators. Thus, the SOD-GPC is used to design a type-II ATO whose steady-state angle estimation error tends to zero during constant speed operation. Simulation and experimental results prove that the proposed RDC system has better performance than other literature approaches.

The management of the electrical energy still raises a huge interest for end-users at the household level. Home electricity management systems (HEMS) have recently emerged both to warrant uninterruptible power and high power quality, and to decrease the cost of electricity consumption, by either shifting it in off peak time or smoothing it. Such a HEMS requires a bidirectional DC-AC converter, specifically when an energy transfer is required between a storage system and the AC-grid, and vice versa. This article points out the relevance of an innovative topology based on sinusoidal waveforms from the generation of sine half-waves. Such a topology is based on a DC-DC stage equivalent to an adjustable output voltage source and a DC-AC stage (H-bridge) which are in series. The results of a complete experimental procedure prove the feasibility to improve the power quality of the output signals in terms of total harmonic distortion (THD-values about 5%). The complexity of the proposed converter is minimized in comparison with multilevel topologies. Finally, wide band-gap semiconductor devices (SiC MOSFETs) are helpful both to warrant the compactness and the high efficiency (about 96%) of the bidirectional converter, whatever its operation mode (inverter or rectifier mode).

Abstract: Chryseobacterium indologenes is an opportunistic pathogen isolated from human infec-tions and rarely from some aquatic animals. A 3-year-old male ball python (Python regius) was admitted to the veterinary clinic by a pet owner because of acute respiratory and swallowing failure. During physical examinations, oral secretions and abscesses were observed on the mouth cavity and throat of the animal. After microbiological analysis including isolation, identi-fication, and 16s rRNA sequencing; Ch. indologenes was detected as the main cause of the oral abscess in this case. Phylogenetic relatedness analysis showed a close relationship between this isolate and other strains isolated from human infections. Antimicrobial susceptibility testing re-vealed that the isolate was multi-drug resistant. However, it was very sensitive to minocycline, ceftazidime, and tetracycline. The patient was treated by antibiotic therapy and completely re-covered after two weeks. To our best knowledge, this is the first incidence of Ch. indologenes in an oral abscess in a ball python. As result we would consider this organism as an opportunistic animal pathogen with zoonotic potentiality.

A high step-up DC-to-DC converter that integrates an isolated transformer and a switched-clamp capacitor is presented in this study. The voltage stress of the main power switch should be clamped to 1/4 V by using the turn ratio and switched-clamp capacitor of an isolated transformer to achieve a high voltage gain. In addition, a passive clamp circuit is employed reduce voltage stress on the main power switch. The energy of the leakage inductor can be recycled by the clamp capacitor because of the passive clamp circuit, thereby improving the power converter efficiency. The converter consists of one isolated transformer, one main switch, three capacitors, and four diodes. Operating principle and steady-state analyses are also discussed. Finally, a 24-V-input voltage to 200-V-output voltage and a 150 W output power prototype converter are fabricated in the laboratory. The maximum efficiency of the converter is 95.1 at 60 W.

Unsteady flow is the most common and complicated form of fluid motion in nature. This paper takes the beaches of key waterways in the middle and lower reaches of the Yangtze River as the research object, analyzes the water surface distribution and average flow velocity distribution near the beach, and analyzes the differences. The relationship between the flow pattern of the steep slope and the water flow pattern of the entire survey area and the steep slope of the beach and its nearby water flow structure. The water flow near the beach is divided into four major blocks: the swelling water area, the falling water area, the backwater area and the maximum flow rate area. The water flow structure characteristics and the cause of each block are analyzed.

In this paper, new types of M-fractional wave solutions of mathematical physics model named as truncated M-fractional (1+1)-dimensional non-linear simplified Modified Camassa-Holm model are achieved by applying the modified simplest equation (MSE), Sardar sub-equation and generalized Kudryashov techniques. The gained solutions containing dark, bright, periodic and mixed wave solitons. Effect of fractional order derivative is also discussed. Achieved wave solitons are verified by Mathematica tool. Few of the gained wave solitons are also described through 2-dimensional, 3-dimensional and contour graphs through Mathematica tool. The gained solutions are helpful for the further development of concerned model. Finally, these techniques are simple, fruitful and effective to deal with non-linear FPDEs.

Satellite-based Normalized Difference Vegetation Index (NDVI) time-series data are useful for monitoring the changes of vegetation ecosystems in the context of global climate change. However, there are currently no ideal NDVI datasets that reconcile long-term series with high spatial resolution. Here, we have developed a simple and novel data downscaling algorithm based on the coefficient of variation (CV) statistics, which combines the detailed spatial features of MODIS data with the long-term temporal information of AVHRR data. The proposed data fusion method helps generate a global monthly NDVI database that has a 250 m-resolution and covers the long period of 1982−2018. We evaluated the accuracy of the fused data against MODIS NDVI using the metrics of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Pearson’s correlation coefficients (R). Validation suggests a high performance of the downscaling algorithm and a high accuracy of the new NDVI database. We further applied the downscaled data to monitor NDVI changes of various vegetation types and in areas having high vegetation heterogeneity, and we obtained stable results similar to MODIS data. The whole data downscaling and validation processes were completed on the Google Earth Engine platform, and here we provide a code for users to easily get the data for any part of the world. The downscaled global-scale NDVI time series has high potential in monitoring the temporal and spatial dynamics of the terrestrial ecosystems under changing environments.

SOC stock (SOCS) changes caused by land use changes are still unclear, and understanding this response is essential for many environmental policies and land management practices. In this study, we investigated the temporal-spatial and vertical distribution characteristics of SOCS on the Western Sichuan Plateau (WSP) using the sparrow search algorithm-random forest regression (SSA-RFR) models with excellent model applicability and accuracy. The temporal-spatial varia-tions in the SOCS were modeled using 1080 soil samples and a set of nine environmental covariates. We analyzed the effect of land use changes on the SOCS on the WSP. The total SOCS increased by 18.03 Tg C from 1990 to 2020. The results of this study confirmed that the SOCS in the study area has increased significantly since 2010, with an increase of 27.88 Tg C compared to the total SOCS in 2010. We found that the spatial distribution of the SOCS increased from southeast to northwest, and the vertical distribution of the SOCS in the study area decreased with increasing soil depth. Forest and grassland are the main sources of SOCS, the total SOCS in the forest and grassland accounted for 37.53 and 59.39% of the total SOC pool in 2020, respectively. Expansion of the wetlands, forest, and grassland areas could increase SOCS in the study area.

We present a MATLAB-based, open source reconstruction software for Hybrid Imaging problem arising from Current Density Impedance Imaging (CDII) and Acoustic-Electrical Impedance Tomography (AEIT), utilizing mixed finite element method solver of Darcy toolbox {\tt DarcyLite}~. For a formulation of hybrid inverse problems in impedance tomography the successive substitution iterative schemes are adapted and an reconstruction algorithm is developed. The problem formulation is a class of hybrid imaging modality which is current density impedance imaging or Acoustic-Electrical Impedance Tomography. The proposed algorithm is implemented numerically in two dimensions.

The wetlands in northeastern Bangladesh are home to some of the most prolific and valuable ecosystems on the planet, offering a wide range of benefits to both the local and wider communities. A limited number of research on this topic have been carried out in various wetlands in coastal hilsa, but none have been located in the northeastern region of Bangladesh. Thus, the goal of this study was to better understand the most commercially relevant species (anadromous hilsa) in northeastern Bangladesh as well as to assess the wetland ecosystem services more thoroughly with the contribution of their fish genetic resources. In addition to laboratory studies, fieldwork was done in the fishing settlements along the Surma River, Hakaluki Haor, and Tanguar Haor. The study classified 77 fish species into 21 subcategories of ecosystem services, of which 56% were not threatened, 17% were vulnerable, and 10% were highly endangered. The species Hilsa was found to be the most economically significant, accounting for 1% of the GDP overall. The study determined if the hilsa stock of the examined wetlands maintains a single or several subpopulations in comparison to other parts of the country when they migrate from saline water to northeastern Bangladesh (freshwater). But when compared to other groups, the FST score was between 0.000 to 0.017, indicating that the populations are identical and that there is genetic material sharing between them. Additionally, these statistics imply that the Sylhet haor and adjacent river systems contain a single stock of Hilsa fish. Nevertheless, a number of stresses and risks, including overexploitation, illicit and destructive fishing, siltation, and natural disasters like flash floods, have been linked to changes in the supply of hilsa and other ecosystem services. This study also emphasizes the necessity of policy attention and research to address the concerns.

Abstract: As a low-input crop, Miscanthus offers numerous advantages that, in addition to agricultural applications, permits its exploitation for energy, fuel, and material production. Depending on the Miscanthus genotype, season, and harvest time as well as plant component (leaf versus stem), correlations between structure and properties of the corresponding isolated lignins differ. Here, a comparative study is presented between lignins isolated from M. x giganteus, M. sinensis, M. robustus and M. nagara using a catalyst-free organosolv pulping process. The lignins from different plant constituents are also compared regarding their similarities and differences regarding monolignol ratio and important linkages. Results showed that the plant genotype has the weakest influence on monolignol content and interunit linkages. In contrast, structural differences are more significant among lignins of different harvest time and/or season. Analyses were performed using fast and simple methods such as nuclear magnetic resonance (NMR) spectroscopy. Data was assigned to four different linkages (A: b-O-4 linkage, B: phenylcoumaran, C: resinol, D: b-unsaturated ester). In conclusion, A content is particularly high in leaf-derived lignins at just under 70 % and significantly lower in stem and mixture lignins at around 60 % and almost 65 %. The second most common linkage pattern is D in all isolated lignins, the proportion of which is also strongly dependent on the crop portion. Both stem and mixture lignins, have a relatively high share of approximately 20 % or more (maximum is M. sinensis Sin2 with over 30 %). In the leaf-derived lignins, the proportions are significantly lower on average. Stem samples should be chosen if the highest possible lignin content is desired, specifically from the M. x giganteus genotype, which revealed lignin contents up to 27 %. Due to the better frost resistance and higher stem stability, M. nagara offers some advantages compared to M. x giganteus. Miscanthus crops are shown to be very attractive lignocellulose feedstock (LCF) for second generation biorefineries and lignin generation in Europe.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

Implementing computer algorithms and virtual reality tools into all areas of the educational process is vital for the new generation engineers, scientists and researchers. With regards to chemistry education, various software that allow dynamic molecular building and viewing are currently available. The present short communication provides a summary of these applications and a detailed description of the NarupaXR program, which has the desired mix of functionality and simple design, necessary for an educational tool. Since NarupaXR can only be used with a companion application “Narupa Builder” which has a different file format, we have developed a converter that allows a simple transition between the two extensions of the aforementioned programs and sufficiently increases the efficiency of the educational process. The automatic converter is freely available on GitLab (https://gitlab.com/teamSCAMT/converter_mol2_to_xml). We have provided this short communication with detailed written instructions that can simplify the installation process of the converter and facilitate the use of both the software and the hardware of the VR set.

Accurate forecasts of ocean waves energy can not only reduce costs for investment but it is also essential for management and operation of electrical power. This paper presents an innovative approach based on the Long Short Term Memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analyzing is provided by collecting the experimental data from another study and the exerted data from numerical simulation of searaser. The simulation is done with Flow-3D software which has high capability in analyzing the fluid solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study the wind speed and output power are related with a LSTM method. Moreover, it can be inferred that the LSTM Network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement and the root mean square is 0.49 in the mean value related to the accuracy of LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of LSTM method.

Women's empowerment represents a process in which they are recognized as subjects of rights. Despite advances in women's rights, inequalities persist in various areas. This study aims to validate a psychometric scale of 31 items in seven factors: Participatory Empowerment, Temerity, External Influences, Independence, Social Satisfaction and Equality. The objective is to measure the empowerment of women in the university context. The research was carried out in a transversal and non-experimental manner, with the participants' voluntary collaboration, including the participation of 1,478 university students from four Higher Education Institutions in Zone 4 of Ecuador. The data was analyzed through inferential statistics, including exploratory and confirmatory factor analysis. The results indicated that the instrument for measuring empowerment can be made up of 24 items grouped into six factors, and this model showed a good fit.

We consider the financial planning problem of a retiree wishing to enter a retirement village at a future uncertain date. The date of entry is determined by the retiree's utility and bequest maximisation problem within the context of uncertain future health states. In addition, the retiree must choose optimal consumption, investment, bequest and purchase of insurance products prior to her full annuitisation on entry to the retirement village. A hyperbolic absolute risk-aversion (HARA) utility function is used to allow necessary consumption for basic living and medical costs. The retirement village will typically require an initial deposit upon entry. This threshold wealth requirement leads to exercising the replication of an American put option at the uncertain stopping time. From our numerical results, active insurance and annuity markets are shown to be a critical aspect in retirement planning.

An exposition is undertaken to reveal the significant impact of the time-dependent root parameter, on the stability of Pointwise Stationary Fluid Flow Approximation (PSFFA) model of the non-stationary queueing system. This opens new grounds to stability analysis of non-stationary queueing systems. Potential PSFFA applications of IoT are highlighted.

The discovery by the Large Area Telescope on board Fermi of variable gamma-ray emission from radio-loud narrow-line Seyfert 1 (NLSy1) galaxies revealed the presence of a possible third class of AGN with relativistic jets in addition to blazars and radio galaxies. Considering that NLSy1 are usually hosted in spiral galaxies, this finding poses intriguing questions about the nature of these objects and the formation of relativistic jets. We report on a systematic investigation of the gamma-ray properties of a sample of radio-loud NLSy1, including the detection of new objects, using 7 years of Fermi-LAT data with the new Pass 8 event-level analysis. In addition we discuss the radio-to-very-high-energy properties of the gamma-ray emitting NLSy1, their host galaxy, and black hole mass in the context of the blazar scenario and the unification of relativistic jets at different scales.

The preparation of transition metal salt ammine complexes having oxidizing anions is a great challenge due to the reaction between the ammonia and oxidizing anions during the synthesis of these materials. However, they have an important role in the development of new oxidants in organic chemistry and especially as precursors in the preparation of mixed metal oxides and alloys in controlled temperature thermal decomposition reactions. Therefore, the synthetic procedures to prepare the complexes of transition metal permanganate, pertechnetate, and perrhenate (VIIB group tetraoxometallates) salts with ammonia as ligand or co-ligand have been comprehensively reviewed. The available data about the structure and spectroscopic properties of these compounds, including the hydrogen bonds that act as redox reaction centers under their thermal decomposition are given and evaluated. Their thermal decomposition processes and products have been summarized. The available pieces of information about their role in organic oxidation reactions like the oxidation of benzyl alcohols, and regeneration of oxo-compounds from oximes and phenylhydrazones including the kinetics of these processes have also been collected.

Previous studies generally reckon that the full converter-base wind power generation (FCWG) is a ’decoupled’ power source from the grid, which hardly participates in electromechanical oscillations. However, it is found recently that strong interaction could be induced which might incur severe resonance incidents in electromechanical dynamic timescale. In this paper, the participation of FCWG in electromechanical dynamics is extensively investigated, and particularly, an unusual transition of electromechanical oscillation mode (EOM) is uncovered for the first time. The detailed mathematical models of open-loop and closed-loop power systems are firstly established, and modal analysis is employed to quantify the FCWG participation in electromechanical dynamics, with two new mode identification criteria, i.e., FCWG dynamics correlation ratio (FDCR) and quasi-electromechanical loop correlation ratio (QELCR). On this basis, the impact of different wind penetration levels and controller parameter settings on the participation of FCWG is investigated. It is revealed that if an FOM has a similar oscillation frequency to the system EOMs, there is a high possibility to induce strong interactions between FCWG dynamics and system electromechanical dynamics of the external power systems. In this circumstance, an interesting phenomenon may occur that an EOM may be dominated by FCWG dynamics, and hence is transformed into a quasi-EOM, which actively involves the participation of FCWG quasi-electromechanical state variables.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

Day by day, the share of renewable energy-based power plants such as offshore wind farms and photovoltaic power plants, in the present-day power systems is escalating. Nevertheless, most of these power-producing facilities have been located quite far away from the load centers. At this stage, HVDC power systems provide satisfactory options due to qualities such as reduced line losses, substantial controllability, and environmental advantages. Nevertheless, unique converter designs are required to combine asynchronous HVDC networks with each other and with already existing high voltage alternating current (HVAC) networks. In this study, based on the above special cases, a unique DC-DC modular multilevel converter (MMC) architecture is presented that provides an excellent solution for both integrating modern asynchronous HVDC networks and integrating these modern HVDC power systems with existing asynchronous HVAC power systems. The mathematical analysis of the recommended DC-DC MMC design has been examined in detail and the power transfer between two asynchronous power grids has been realized in a simulation environment. Efficient bilateral power transmission has been made achievable by the control system. The recommended DC-DC MMC topology has shown to be a highly successful approach, in the interconnection of HVDC power systems, according to the simulation results.

The wave energy converter (WEC) technology constantly develops new designs and models. To encourage its development within the energy sector, research is required to improve current designs and create durable and effective devices. However, it is also crucial to investigate the suitability of a WEC to a site-specific sea condition as this can impact its optimal power absorption and constructability. This study evaluates and compares the efficiency of a heaving buoy with varied geometries in one of the most energetic places along the Mexican coast. A statistical analysis of the wave climate on the coast of Ensenada during the last 42 years is performed to define the conditions to which the device is subjected. The location of the WEC in shallow waters is chosen using a computational model which solves the modified mild slope equation in its elliptic shape. The heaving buoy floater is studied using three distinct geometries: a semi-sphere, a cylinder, and a proposed rounded semi-rectangle. The hydrodynamic response of the three geometries is then analysed in the frequency and time domain using ANSYS AQWA. The hydrodynamic study involves the assessment of the floating body dynamics, exerted forces, the power absorbed as well as the suitability of the proposed power take-off (PTO) system. Findings reveal that the proposed geometry absorbs the most energy, with an annual power of 135.11 MW, and that the transmission PTO design is appropriate for this type of technology.

This communication explores the transition from Cognitive Behavioural Therapy (CBT) to a more holistic model, named Complex Therapy, developed by Tullio Scrimali for explaining and treating mental disorders. The Author delves into the limitations of traditional CBT and introduces an innovative framework that integrates multiple therapeutic modalities. While CBT is based on the study and modification of behaviour and cognition, Complex Therapy includes, in its focus, the theory of human relationships, the attachment theory, neuroscience, applied neuroscience, complex systems theory and ecology.Complex Therapy addresses the multifaceted nature of mental health issues by combining evidence-based techniques with personalized care strategies. The goal is to enhance patient outcomes through a tailored, comprehensive treatment plan that adapts to the evolving needs of individuals facing mental health challenges.Two innovative and original items which Scrimali has developed, which are a device for Applied Neuroscience MindLAB Set and a new botanical drug, NegEnt, are presented and discussed.The topic also describes, an original and integrated protocol, named Negative Entropy, the author has developed for treating a challenging disorder, such as schizophrenia, he renamed Entropy of Mind.

The effect of neutron irradiation of Si-Ge/Si film structures obtained by gas-phase epitaxy on the processes of charge carrier generation and the magnitude of the dark short-circuit current and open-circuit voltage caused by it is studied, it is shown that their magnitude practically does not change with time. The results obtained indicate that all types of structural defects, both associated with dopant impurities and radiation exposure, contribute to the generation of charge carriers in heated silicon structures with the participation of deep energy levels, which indicates the applicability of neutron processing of silicon materials to create thermal energy converters, operating at high temperatures.

This article argues that philosophy and its influential narrative should take and think "food" - the gastro-world - in order to radically renew itself and develop a gastrocentric view of the world and the future of humanity. It discusses first some of the basic ideas of a gastrocentric understanding of the world, second the adventure of becoming a Terran, and third the daily practice of what I will call "terra madre forming". It tells us this: If there is to be a future of human survival and well-being on Earth, if we are to achieve the goals of sustainable development, then we have a clear agenda for tomorrow with the re-humidification of the soil - madre terra - that nourishes us. What needs to be done is quite simple and down-to-earth: We, each of us, if possible, must participate in the regeneration of the earth. In conclusion, it is time for the Slow Food movement to blossom into a geo-gastro-activism. The future task of this organization is to make itself electable as a political party to support the much-needed transformation of humans into Terrans.

This paper provides a new discovery that adds to the contemporary theory of modelling of non-stationary queues, by capturing analytically the behavior of the root parameter, for the G1/M/1 PSFFA model of non-stationary Ek/M/1 queue. Additionally, it has been also revealed that the resulting root parameter reduces to the PSFFA model of non-stationary queue. Potential applications of PSFFA to Computer engineering are provided. Some challenging open problems are addressed combined with closing remarks and future research directions.

To reduce the redundant counting of TDC in high-speed applications and thus reduce power consumption, a TDC architecture is proposed to quantify the difference between two adjacent rows of pixel signals. This structure can remove the identical part between two rows of pixel signals by adjusting the start and stop signal of the TDC, which will reduce the number of flipping of D flip-flops in the TDC. In the 110nm CMOS process, simulation results show that this design achieves an effective number of bits (ENOB) of 4.72 bits and a Figure of Merit (FoM) of 104.7 fJ/step - 162.3 fJ/step with a power consumption ranging from 60 µW to 93 µW. Compared with traditional counting methods, the proposed TDC can reduce counting power consumption by 30%.

The article presents a method of generating Key Performance Indicators related to electric motor energy efficiency on the basis of Big Data gathered and processed in frequency converter. The authors proved that using the proposed solution it is possible to specify the relation between the control mode of an electric drive and the control quality-energy consumption ratio in the start-up phase as well as in the steady operation with various mechanical loads. The tests were carried out on a stand equipped with two electric motors (one driving, the other used to apply the load by adjusting the parameters of the built-in brake). The measurements were made in two load cases, for motor control modes available in industrially applied frequency converters (scalar V/f, vector Voltage Flux Control without encoder, vector Voltage Flux Control with encoder, vector Current Flux Control and Vector Current Flux Control with torque control). During the experiments values of current intensities (active and output), the actual frequency value, IxT utilization factor, relative torque and the current rotational speed were measured and processed. Based on the data the level of the energy efficiency was determined for various control modes.

Wave energy converter (WEC) arrays deployed in coastal regions may create physical disturbances potentially resulting in environmental stresses. Presently, limited information is available on the nature of these physical disturbance or the resultant effects. A quantitative Spatial Environmental Assessment Tool (SEAT) for evaluating potential effects of wave energy converter (WEC) arrays on nearshore hydrodynamics and sediment transport is presented for the central Oregon coast (USA) through coupled numerical model simulations of an array of WECs. Derived climatological wave conditions were used as inputs to the model to allow for the calculation of risk metrics associated with various hydrodynamic and sediment transport variables such as maximum shear stress, bottom velocity, and change in bed elevation. The risk maps provided simple, quantitative, and spatially-resolved means of evaluating physical changes in the vicinity of a hypothetical WEC array in response to varying wave conditions. Near-field risk of sediment mobility was determined to be moderate in the lee of the densely spaced array, where the potential for increased sediment deposition could result in benthic habitat alteration. Modifications to the nearshore sediment deposition and erosion patterns were observed near headlands and topographic features, which could have implications for littoral sediment transport. The results illustrate the benefits of a risk evaluation tool for facilitating coastal resource management at early market marine renewable energy sites.

Particle Image Velocimetry (PIV) is a widely used experimental technique for measuring flow. In recent years, open-source PIV software has become more popular as it offers researchers and practitioners enhanced computational capabilities. Software development for graphical processing unit (GPU) architectures requires careful algorithm design and data structure selection for optimal performance. PIV software, optimized for central processing units (CPUs), offer an alternative to specialized GPU software. In the present work, an improved algorithm for the OpenPIV-Python software is presented and implemented under a traditional CPU framework. The Python language was selected due to its versatility and widespread adoption. The algorithm was also tested on a supercomputing cluster, a workstation, and Google Colaboratory during the development phase. Using a known velocity field, the algorithm precisely captured the time-average flow, monetary velocity fields, and vortices.

In this paper, we succeed to discover the new exact wave solutions to the truncated M-fractional complex three coupled Maccari’s system by utilizing the Sardar sub-equation scheme. The obtained solutions are in the form of trigonometric and hyperbolic forms. These solutions have many applications in nonlinear optics, fiber optics, deep water-waves, plasma physics, mathematical physics, fluid mechanics, hydrodynamics and engineering where the propagation of nonlinear waves is important. Achieved solutions are verified with the use of Mathematica software. Some of the achieved solutions are also described graphically by 2-dimensional, 3-dimensional and contour plots with the help of Maple software. The gained solutions are helpful for the further development of concerned model. Finally, this technique is simple, fruitful and reliable to handle the nonlinear FPDEs.

From a differential geometric perspective, information geometry (IG) aims to characterise the structure of statistical geodesic models. The research done for this paper offers a novel method for modelling the information geometry of a queuing system. From the perspective of IG, the manifold of the transient M/M/infinity queue is described in this context. The Fisher Information matrix (FIM) as well as the inverse of (FIM), (IFIM) of transient M/M/infinity QM are devised. In addition to that, new results that uncovered the significant impact of stability of QM on the existence of (IFIM) are obtained. Moreover, the Geodesic equations of motion of the coordinates of the underlying transient are obtained. Also, it is revealed that stable QM is developable (i.e., has a zero Gaussian curvature) and has a non-zero Ricci Curvature Tensor (RCT). Novel stability dynamics of transient M/M/iInfinity queue manifold is revealed by discovering the mutual dual impact between the behaviour of (RCT) and the stability(instability) phases of M/M/Infinity QM. Furthermore, a new discovery that presents the significant impact of stability of queue manifold and the continuity of the unique representation between M/G/1 queue manifold and Ricci Curvature Tensor (RCT). The information matrix exponential (IME) is devised. Also, it is shown that stability of the devised (IME) enforces the instability of M/M/ Infinity QM. More interestingly, novel relativistic info-geometric queueing theoretic links are revealed .

The inverse system identification toolbox named INVSID 1.0 for MATLAB, which is used to identify the inversion of single-input single-output systems, is developed. The complete process of developing the toolbox is demonstrated. Afterwards, a numerical example is illustrated to describe how the toolbox can be used to solve inverse identification problems. Simulation results demonstrate the effectiveness of the toolbox.

The fetal heart rate (FHR) is a screening signal for preventing fetal hypoxia during labor. When experts analyze this signal, they have to position a baseline and then identify decelerations and accelerations. These steps can potentially be automated and made more objective by signal processing analysis. Various methods have been described in the literature but there are no open-source programs for performing these steps. The MATLAB toolbox presented here comprises a standard signal pre-processing function, 11 re-coded literature methods for fetal heart rate analysis, a signal viewer (enabling annotation by an expert) and an evaluation procedure with various criteria measuring intrarater agreement.

The article presents an induction generator connected to the power grid using the AC / DC / AC converter and LCL coupling filter. In the converter, both from the generator side and from the power grid side, three-level inverters were used. The algorithm realizing Pulse Width Modulation (PWM) in inverters has been simplified to the maximum. Control of the induction generator was based on the Direct Field Oriented Control (DFOC) method. At the same time, voltage control has been used for this solution. The MPPT algorithm has been extended to include the variable pitch range of the wind turbine blades. The active voltage balancing circuit has been used in the inverter DC voltage circuit. In the control system of the grid converter with an LCL filter, the number of measurements was limited to the measurement of power grid currents and voltages. Synchronization of control from the power grid side is ensured by the use of a PLL loop with the system of preliminary suppression of undesired harmonics (CDSC).

Nowadays, the identification and characterization of grapevine cultivars resilient to climate and water stress while preserving quality traits is crucial for the wine industry. Therefore, the objective of this work was to characterize according to their aromatic potential 9 white and 6 red minority cultivars recently recovered from Castilla-La Mancha region (Spain), subjected at two different water deficit regimes: rainfed, with subsistence irrigation, and irrigated. For this, the varietal aromatic potential index (IPAv) and the detailed aromatic composition were analyzed by HS-SBSE-GC/MS, in extracts of two different ph. For IPAv values, red varieties did not show a clear trend with respect to irrigation. However, in white minority varieties, higher values were obtained under irrigation conditions. Thus, a clear differentiation of the minority varieties in comparison to the references was observed, primarily attributed to the content of esters and acids, in both white and red varieties. A notable contrast was observed at different pH, indicating a greater extractability of certain compounds like linalool, under more acidic conditions. This suggests that some recovered minority cultivars could be promising for cultivation in semi-arid regions with limited water, contributing to the wine sector sustainability in the future.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

The valorization of the large amount of crude glycerol formed from biodiesel industry, is of primordial necessity. One possible direction with high interest to the biorefiniery sector is the production of fuel additives as solketal, though the acetalization of glycerol with acetone. This is a chemical process that conciliates high sustainability and economic interest, since solketal contributes to the fullfilment of a Circular Economy Model through its use in biodiesel blends. The key to guarantee high efficiency and high sustainability for solketal production is the use of a recovery and recyclable heterogeneous catalyst. Some examples are already reported in the literature (first example in 2012), and this review intends to conciliate the information spreaded in this topic and established the most important reacional parameters and the main catalytic characteristics strategies to achieve high solketal production in short reaction time.

Background: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. Methods: Deep convolutional neural networks (DCNN) were trained as one-vs-all classifiers to differentiate between six classes, T1 weighted (w), contrast-enhanced T1w, T2w, T2w-FLAIR, ADC, and SWI. Each classifier yields a probability, allowing threshold-based and relative probability assignment while excluding images with low probability (label: unknown, open-set recognition problem). Data from three high-grade glioma (HGG) cohorts was assessed; C1 (320 patients, 20101 MRI images) was used for training, while C2 (197, 11333) and C3 (256, 3522) were for testing. Two raters manually checked images through an interactive labeling tool. Finally, MR-Class' added value was evaluated via radiomics models' performance for progression-free survival (PFS) prediction in C2, utilizing the concordance index (C-I). Results: Approximately 10% of annotation errors were observed in each cohort between the DICOM series descriptions and the derived labels. MR-Class accuracy was 96.7% [95%-Cl: 95.8, 97.3] for C2 and 94.4% [93.6, 96.1] for C3. 620 images were misclassified; Manual assessment of those frequently showed motion artifacts or alterations of anatomy by large tumors. Implementation of MR-Class increased on average the PFS model C-I by 14.6% compared to a model trained without MR-Class. Conclusions: We provide a DCNN-based method for sequence classification of brain MR images and demonstrate its usability in two independent HGG datasets.

Lignocellulosic biomass, an abundant and renewable resource, serves as a crucial raw material for the production of biofuels, biochemicals, and various value-added products. This paper aims to develop and optimize a mild alkaline treatment of sawdust assisted by ultrasound, along with enzymatic hydrolysis of the pretreated material. The sonochemical pretreatment with alkali emerged as the optimal approach to enhance the susceptibility of cellulose to subsequent enzymatic hydrolysis, thereby increasing the yield of reducing sugars. A comparative study was performed using various ultrasonic applicators (horn and bath) and conventional assisted alkaline pretreatment. The ultrasonic assisted pretreatment revealed a higher delignification as 68% (horn) and 57% (bath) comparing with conventional pretreatment. Processes were optimized using a statistical analysis based on a 23 factorial design. The ratio between sawdust and alkanine solution (RSL = 0.5–1.5 g/100 mL), US amplitude (A = 20–60%), and working temperature (t = 30–50 °C) were selected as process factors. The optimal operating conditions to maximize the reducing sugar yield (138.15 mg GE /gsubstrate) were found as following: solid/liquid ratio RSL,opt = 1.25 g/100 mL, US amplitude Aopt = 60%, and and pretreatment temperature topt = 50 °C. The overall outcomes clearly confirmed the intensification of delignification by ultrasound assisted alkaline pretreatment

A new set of basis functions is presented. The foundation in mathematics is established. To reconstruct a signal, it is compared to the Fourier basis functions. The M-basis functions' potential applications are also presented.

Occupational accidents, despite continuous safety updates, are still a scourge in the occupational and forensic spheres, constituting, among other things, the subject of a large share of litigation. Demographic data can help to understand the areas where the application of health surveillance is lacking. This meta-analysis sets out to analyze data from studies on accidents at work, focusing on the correlation between the areas in which accidents occur and whether or not personal safety equipment is used, about the different regulations in force. For the selection of the data, a systematic review was carried out according to the PRISMA guidelines, with the primary objective of identifying the trend of occupational accidents in specific geographical areas, which differ in terms of the attention paid to preventive aspects. The data we highlighted showed, regarding the type of accident, substantial differences between low-income countries and industrialized countries (stratified according to the Human Development Index) and, an overall indifference as to whether or not individual safety devices were used, revealing that, despite the continuous normative evolution in the field of safety at work, even today, the investigative data on the actual application of the regulations, during accidents at work, is underestimated and little researched.

This article delves deeply into digital forensics, covering computer forensics, network 1 forensics, and mobile device forensics. It analyzes the techniques and methodologies used by forensic 2 investigators in various disciplines. It underlines the diffculties investigators encounter and the 3 importance of thorough investigations to combat ever-increasing cyber risks. The paper emphasizes 4 the necessity of leveraging digital forensic tools to improve cybersecurity and provides a thorough 5 list of widely used Python libraries suitable for each investigation strategy, allowing for effective 6 comparison. Furthermore, it emphasizes the availability and suitability of these Python libraries in 7 computer device investigations (PyTSK3, Volatility, Pyregf, and Pyevtx), mobile device investigations 8 (Pytsk3, Volatility, Pyewf, dfVFS, Androguard, and pyMobileDevice), and network forensics (Scapy, 9 Bro/Zeek, Dpkt, pypcap, and NetworkX). The creation of these libraries recognizes the complexities 10 of digital crimes and the importance of applying modern techniques in forensic investigations. 11 Particularly, digital forensics plays an important role for healthcare providers because modern 12 medical devices produce, store, and transmit large amounts of patient and therapy information, 13 which could provide a forensic investigator with a treasure trove of potential digital evidence.

We present a simulation library in Python 3 called Lumos. Different optimization routines areimplemented using multiple instruments. Automated measurements are implemented in this librarywhich lead to faster measurements as compared to manual sweeps. We elaborate on the experimentalmethods and also example usage is mentioned.

Particulate matter (PM) is one of the problems faced in environmental science. It has health effects on man and animals in both developed and developing countries. Research and efforts have been on it several years back. Policy statements and efforts have been published. This review paper is an added information on air pollution. In it, efforts were made in discussing these: classification, effects, methodology, case studies and source apportionment. It is hoped that this paper would contribute to existing knowledge on PM.

Reconstructing the Tree of Life remains a central goal in biology. Early methods, relying on small numbers of morphological or genetic characters, often yielded conflicting evolutionary histories, undermining confidence in the results. Investigations based on phylogenomics, which use hundreds to thousands of loci for phylogenetic inquiry, have provided a clearer picture of life's history, but certain branches remain problematic. To resolve difficult nodes on the Tree of Life, two recent studies tested the utility of synteny, the conserved collinearity of orthologous genetic loci in two or more organisms, for phylogenetics. Synteny exhibits compelling phylogenomic potential while also raising new challenges. We identify and discuss specific opportunities and challenges that bear on the value of synteny data and other rare genomic changes for phylogenomic studies. Synteny-based analyses of highly contiguous genome assemblies mark a new chapter in the phylogenomic era and the quest to reconstruct the Tree of Life.

In this paper, at first, a color image of a car is taken. Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3. The blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via Matlab programming. Many methods can restore the noisy and motion blurred image; particularly in this paper Inverse filtering as well as Wiener filtering are implemented for the restoration purpose. Consequently, both motion blurred and noisy motion blurred images are restored via Inverse filtering as well as Wiener filtering techniques and the comparison is made among them.

This study addresses the implementation of the International Financial Reporting Standard 9 (IFRS 9) in the European Union as of January 1, 2018, replacing the International Accounting Standard 39 (IAS 39) to introduce a new model for recognizing Loan Loss Provisions (LLP) based on Expected Credit Loss (ECL). This model responds to criticisms of the former Incurred Credit Loss (ICL) system for its inability to reflect credit losses in a timely manner, potentially exacerbating the effects of financial crises (Bushman &amp; Williams, 2015; Gebhardt &amp; Novotny-Farkas, 2011). The study focuses on the effects of adopting the ECL model on the level of Loan Loss Allowances (LLA) in loans, own equity, and the Common Equity Tier 1 (CET1) ratio across 13 Portuguese commercial banks. A mean comparison test is used to evaluate scenarios before and after the application of the ECL model, highlighting the importance of regulator actions and the adequacy of loss recognition policies, including the effects of Regulation (EU) 2017/2395 of December 12. The results obtained demonstrate significant negative impacts on the net values of loans, own equity, and the CET1 ratio upon adopting the IFRS 9 ECL model, due to the widespread increase in LLAs.

The analysis and simulation of water quality in distribution networks is a complex issue of great concern today. The analysis of the evolution of water age, as a simple indicator of water quality in the network, is of great interest in both design and operation phases. Understanding the factors that have the strongest influence on water quality is key to developing adequate strategies aimed at preserving it. This paper firstly analyses the factors with biggest influence in the tanks water age, to support the selection of the most appropriate configuration from the point of view of water quality during design phases. Then, the main considerations when modelling tanks following the different mixing models considered in Epanet have been presented. Also, a real tank behaviour has been characterised through field measurements and Epanet simulations in order to determine the best fitted mixing model.

In this era of technology, programming has become more significant than ever before. Python and C++ are both widely used programming languages. Python, the most popular programming language in today’s world, is a high-level object-oriented language whereas C++, the language behind most operating systems, is a low-level object-oriented language. In this paper, we present a comparative study of Python and C++. This paper discusses the introduction to these languages, their memory management techniques, and the reasons behind their program execution speed. Furthermore, we analyzed the execution time and memory used by multiple algorithms in both the languages with best, average, and worst cases. They are also compared with respect to the benefits and issues related to them. Results indicate that C++ is faster than Python in execution speed but Python serves as a better language for beginners due to its simplicity. Moreover, for the best results, the language should be selected according to the type of project.

There are several well-described molecular mechanisms that influence cell growth and are relat-ed to the development of cancer. Chemokines constitute a fundamental element which are not only involved in local growth but also affect angiogenesis, tumor spread and metastatic disease. Among them, the C-X-C motif chemokine ligand 12 (CXCL12) and its specific receptor the chemo-kine C-X-C motif receptor 4 (CXCR4) have been widely studied. The overexpression in cell mem-branes of CXCR4 has been shown to be associated with the development of different kinds of his-tological malignancies, such as adenocarcinomas, epidermoid carcinomas, mesenchymal tumors or neuroendocrine neoplasms (NENs). The molecular synapsis between CXCL12 and CXCR4 leads to the interaction of G proteins and activation of different intracellular signaling pathways in both gastroenteropancreatic (GEP) and bronchopulmonary (BP) NENs conferring greater ca-pacity for locoregional aggressiveness, epithelial-mesenchymal transition (EMT) and the ap-pearance of metastases. Therefore, it has been hypothesized how to design tools that target this receptor. The aim of this review is to focus on current knowledge of the relationship between CXCR4 and NENs, with a special emphasis on diagnostic and therapeutic molecular targets.