Protection suit is vital for fireman’ safety. Traditional protection suit just physically protects fireman from empyrosis, but can not locate the body injury position from the impact debris. Herein, we present a wearable impact debris positioning system for fireman protection suit alarming based on accelerometer array. The wearable piezoelectric accelerometers with distribute regularly on the suit to detect the vibration on different body parts, which is conducive to positioning injured body parts. In addition, the injured parts can be displayed on the dummy body model on the upper computer with a higher localization accuracy of 4 cm. The positioning alarm system has a rapid response time of 0.11ms attributing to smart signal processing method. This work provides a reliable and smart method for locating and assessing the body injury position from the impact debris, which is significant for fire disaster commander to rescue injured fireman in time.

This study explores the current landscape of social value (SV) research in the construction industry, analyzing global trends and identifying future research directions. Using bibliometric analysis, research published in Elsevier Scopus database from 2013 to 2023 was extracted using the keywords "social value" AND "construction industry." Results indicate a focus on social value indicators, socioeconomic impacts, project management, innovative practices, and cross-sector collaborations. The study also reveals a lack of attention to SV in African countries' construction sectors. By highlighting research areas and identifying gaps, this study contributes to academic literature, suggesting the need for guidelines, particularly in developing countries, and advocating for further research on SV in the built environment and strategies for global social value creation.

The study findings revealed significant variability in various wood properties among different genetic families, although the static modulus of elasticity did not exhibit significant differences between the chosen genetic families. All measured wood properties decreased from the bottom to the top of the model trees. Wood hardness displayed a moderately negative correlation with wood density and weak correlations with MOE and MOR. Given the weak correlations between wood hardness and other wood mechanical properties, it is suggested that the MOEdyn would be a more suitable trait for genetic studies.

A dioxomolybdenum catalyst [(MoO2Cl2-O-MoO2Cl2)(L)] (1) (with L (1S,2S)-N,N´-bis(2-pyridinecarboxamide)-1,2-cyclohexane) was prepared and used as catalyst for the desulfurization of a multicomponent model fuel containing the most refractory sulfur compounds in real fuels. This complex showed to have a high efficiency to oxidize the aromatic benzothiophene derivative compounds present in fuels, mainly using a biphasic 1:1 model fuel/MeOH system. This process conciliates catalytic oxidative and extractive desulfurization, occurring the oxidation of the sulfur compounds in the polar organic solvent. The oxidative catalytic performance of (1) showed to be influenced by the presence of water in the system. Using 50% aq. H2O2 it was possible to reuse the catalyst and the extraction solvent MeOH during ten consecutive cycles without loss of desulfurization efficiency.

Elevated serum uric acid (sUA) is associated with risk of developing cardiovascular disease (CVD). Here, we examined the prognostic significance of sUA and exercise capacity in 411 Japanese adults (age, ≥65; mean, 81 years) hospitalized for worsening CVD. Mean sUA was significantly higher in the 23% of patients on an sUA-lowering agent than in those not. When the patients were stratified by sUA into three groups (7.0 mg/dL), the high sUA group had significantly worse peak VO2 and composite endpoint (rehospitalization due to worsening CVD and all-cause mortality) compared with low and moderate sUA groups (P < 0.001). When the patients were stratified by sUA into five groups (sUA 10.0 mg/dL), the incidence of the composite endpoint was significantly higher in the highest sUA group compared with that in the reference group, but only in women. Univariate Cox regression analysis, but not a multivariate analysis, indicated that sUA was significantly associated with the composite endpoint. Although sUA and exercise capacity may have some degree of prognostic significance in older patients with CVD, this significance may differ between men and women.

Aflatoxin M1 (AFM1), a carcinogenic toxin, mainly contaminates cow milk and poses significant challenges to the dairy industry and, thereby, public health in most tropical countries, including Ethiopia. This study aimed to investigate the prevalence and level of AFM1 in raw cow milk collected from Specialized Dairy Farms and Milk Vendors in three Urban Centers: Chiro town, Dire Dawa, and Harar cities in Eastern Ethiopia. 180 milk samples were collected from two major Milk Sources: Specialized Dairy Farms (N=90) and Milk Vendors (N=90) using a simple random sampling technique. AFM1 was analyzed using high-performance liquid chromatography. The study revealed that, 63.9% (115/810) prevalence and 0.179±0.48µg/L mean of AFM1 level in milk, which 39.13% and 26.08% exceed the tolerable limits of EU and FDA respectively. A 40.0% prevalence and 0.344±0.72µg/L average of AFM1 in the milk from Dire Dawa city was significantly higher than the samples from other Urban Centers. However, 30.43% and 29.57% prevalence as well as 0.055±±0.13µg/L and 0.140±0.33µg/L average of AFM1 in milk from Chiro town and Harar city, respectively were not significant. Furthermore, milk samples from Dairy Farms revealed a higher prevalence (57.39%) and level (0.252±0.64µg/L) of AFM1 than Milk Vendors, with a prevalence of 42.61% and an average of 0.107±0.21µg/L. Therefore, this study concludes that substantial milk samples were contaminated by AFM1 and urges the need to enhance farmers' awareness on mitigation.

The influence of growth temperature on the crystalline quality and optical properties of AlGaN at 325 nm, grown using plasma-assisted molecular beam epitaxy (MBE), was investigated. The growth temperature exhibited a significant impact on the growth mode and surface morphology. Specifically, at a growth temperature of 750 degrees Celsius, when appropriate Al and Ga flow rates were employed, the AlGaN layer exhibited exceptional surface morphology and favorable optical properties. The optical properties of the AlGaN material were explored, revealing the presence of two distinct non-radiative recombination centers characterized by different activation energies. The minority carrier lifetimes were measured as 560 ps at 10 K and 64 ps at room temperature. The underlying mechanism was analyzed from the perspective of carrier dynamics, shedding light on the observed phenomena.

Herein, we present the design technique of a resonant rectifier for piezoelectric (PE) energy harvesting. We propose two diode equivalents to reduce the voltage drop in the rectifier operation, a minuscule-drop-diode equivalent (MDDE) and a low-drop-diode equivalent (LDDE). The diode equivalents are embedded in resonant rectifier integrated circuits (ICs), which use symmetric bias-flip to reduce the power wasted for charging and discharging the internal capacitance of a PE transducer. The self-startup function is supported by synchronously generating control pulses for the bias-flip from the PE transducer. Two resonant rectifier ICs, using both MDDE and LDDE, are fabricated in a 0.18 μm CMOS process and their performances are characterized under external and self-power conditions. Under the external-power condition, the rectifier using LDDE delivers an output power POUT of 564 μW and a rectifier output voltage VRECT of 3.36 V with a power conversion efficiency (PCE) of 90.1%. Under self-power conditions, the rectifier using MDDE delivers a POUT of 288 μW and a VRECT of 2.4 V with a corresponding PCE of 74.6%. The result shows that the power extraction capability of the proposed rectifier is 5.9 and 3.0 times higher than that of a conventional full-bridge rectifier.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

The growing expansion of the economy and population has resulted in an increased inclination towards the utilization of groundwater resources. Conducting a geophysical survey is a widely employed method for subsurface mapping and the detection of groundwater. A geophysical study was conducted in Nanshan township, Huarong County, located in the Hunan province of the South-Central region of China. The investigation involved the utilization of a 2D Electrical Resistivity Imaging technique employing forward and inverse pole-dipole electrode array. A total of six survey lines were established, each with an electrode distance from the nearest measuring point to exceeding 800 m. The maximum current electrode separation was utilized in this setup. The spacing between the electrical resistivity sounding points was established at regular intervals. The findings from the exploration indicate the existence of multiple faults within the surveyed region. The study additionally identified two regions of structural failure, which occurred due to the convergence of faults oriented in different directions. This convergence led to the fracturing of rocks, an increase in water content, and a decrease in resistivity. The findings from the exploration were utilized in the formulation of five verification target boreholes. The results of this study offer significant insights that can inform future investigations into groundwater exploration endeavors within the region.

Keywords: olfactory rehabilitation; chronic pain; fMRI;

Diacylglycerol kinase (DGK) is a lipid kinase that phosphorylates diacylglycerol (DAG) to generate phosphatidic acid (PA). Based on converting one important signaling molecule (DAG) to another (PA), DGK plays an important role in plant responses to abiotic stress including waterlogging stress. However, no studies have been reported on the characterization of the DGK gene family in the waterlogging-tolerant kiwifruit germplasm, Actinidia valvata Dunn. Here, we identified 18 AvDGK genes in the A. valvata genome. The phylogenetic analysis showed that AvDGKs can be classified into three clusters, and members within the same cluster have similar domain distribution, exon-intron structures and conserved motif compositions. Chromosome localization analysis revealed that all the AvDGK genes are located across 18 different chromosomes. There were 29 duplicated gene pairs in kiwifruit and all had undergone purifying selection during evolution. Promoter cis-element analysis revealed that the cis-elements within AvDGK genes are associated with multiple functions, including phytohormone signal transduction, stress response, and plant growth and development. The expression pattern analyses indicated that AvDGK play an important role in the fruit development and plant response to waterlogging stress. AvDGKs gene family in the tetraploid A. valvata genome might promote PA synthesis and subsequent signal transduction both under short-term and long-term waterlogging stress. These results provide information regarding the structural characteristics and potential function of DGK genes within kiwifruit and lay a fundamental basis for further research into breeding for enhancing the kiwifruit’s tolerance to waterlogging stress.

The concept of Digital Twins has been in the field for a long time, constantly challenging the specification, modeling, design, implementation, and exploitation of complex cyber-physical systems. Despite the various foundations, standards, and platforms in Systems Engineering, there are ongoing challenges with Verification and Validation methodology. This study aims to establish a generic framework that addresses the various aspects of Digital Twinning. The multifaceted nature of the problem requires raising the abstraction level in both the Real (Actual) and Virtual domains, effective dissemination of information resources, and a design inspired by verification and validation. The proposed framework combines the Quintuple Helix model with the Problem and Operational Domains of a Real (Actual) Twin, the Solution and Implementation Domains of a Virtual Twin, and the Execution Domain as the bridge that links them. Verification and Validation dimensions follow the Meta Object Facility abstraction layers (Instance, Model, Meta-model, and Meta-meta-model) mapping over five Helices. Embedding the complexity reduction mechanisms in the proposed framework builds a suite for extendible and verifiable digital twinning in simulation and real-time scenarios.

Fever control is essential in patients with severe traumatic brain injury (TBI). The efficacy of therapeutic hypothermia (TH) in severe TBI has been investigated over the last few decades; however, in contrast to experimental studies showing benefits, no evidence of efficacy has been demonstrated in clinical practice. In this review, the mechanisms and history of hypothermia were briefly outlined, while the results of major randomized controlled trials (RCTs) and meta-analyses investigating TH for adult TBI are introduced and discussed. The retrieved meta-analyses showed conflicting results, with a limited number of studies indicating benefits of TH. Some studies have shown benefits of long-term TH compared with short-term TH. Although TH is effective at lowering elevated intracranial pressure (ICP), reduced ICP does not lead to favorable outcomes. Low-quality RCTs overestimated the benefits of TH, while high-quality RCTs showed no difference or worse outcomes with TH. RCTs assessing standardized TH quality demonstrated the benefits of TH. As TBI has heterogeneous and complicated pathologies, applying a uniform treatment may not be ideal. A meta-analysis of young patients who underwent early cooling and hematoma removal showed better TH results. TH should not be abandoned, and its optimal should be advocated on an individual basis.

Immune checkpoint inhibitors (ICI) are a promising form of immunotherapy that has significantly changed the therapeutic landscape for many advanced cancers. They have shown unique clinical benefit against a broad range of tumour types and a strong overall impact on survival in studied patient populations. However, there are still many limitations holding back this immunotherapy from reaching its full potential as a possible curative option for advanced cancer patients. A great deal of research is being undertaken in the hope of driving advancements in this area, building a better understanding of the mechanisms behind immune checkpoint inhibition and ultimately developing more effective, safer, and wider-reaching agents. Taking into account the current literature on this topic, this review aims to explore in depth the basis of the use of ICIs in the treatment of advanced cancers, evaluate its efficacy and safety, consider its current limitations, and finally reflect on what the future holds for this very promising form of cancer immunotherapy.

Patients with inflammatory bowel diseases (IBD), including both ulcerative colitis (UC) and Crohn's disease (CD), are at a higher risk of developing colorectal cancer (CRC). However, advancements in endoscopic imaging techniques, integrated surveillance programs, and improved medical therapies have led to a decrease in the incidence of CRC among IBD patients. Currently, the management of patients with IBD who have a history of or ongoing active malignancy is an unmet need. This involves balancing the risk of cancer recurrence/progression with the potential exacerbation of IBD if the medications are discontinued. The objective of this review is to provide an updated summary of the epidemiology, causes, risk factors, and surveillance approaches for CRC in individuals with IBD, and to offer practical guidance on managing IBD patients with history of previous or active cancer.

Early childhood may be a stressful period for families with a significant impact on parents’ mental health and the child’s healthy development. The goal of the SKKIPPI cohort study was to assess the occurrence and determinants of psychosocial stress and mental health disorders in parents as well as in their offspring in early childhood in three German regions. Based on random samples from three residents' registration offices, parents with infants up to 12 months of age at the time of sampling, were invited to participate in the study. A screening online questionnaire was newly developed, available in four languages, to assess common psy-chosocial stressors and mental health problems of parents with small children. The study enrolled 4,984 mothers and 962 fathers. The most common potential psychosocial stressors at baseline were professional problems (mothers 22%, fathers 33%), lack of social sup-port (20%, 14%), and severe, negative experiences in childhood (22%, 16%). Obses-sive-compulsive thoughts (21%, 16%), depressive (9%, 9%) and anxiety symptoms (11%, 7%) were the most frequently reported mental health problems by both parents. Regulatory prob-lems of the child were reported by between 1.5% and 5.1% of parents. The SKKIPPI study showed that a substantial proportion of parents are burdened by psychoso-cial problems and suffer from mental health problems in the first years after birth. Early pre-ventive and low-threshold support measures should be available in the health and social care system. The newly developed questionnaire can be used to screen in the relevant populations in order to avoid disturbances in the mother-child attachment.

Background: Endovascular treatment of popliteal aneurysms (PA) has increased in the last few years, quickly becoming the main treatment in many vascular centers, based on the acceptable and promising outcomes reported in the literature. However, endograft infections are the most dangerous complication as a serious local compromise and usually require open surgical conversion and device explantation to preserve the affected extremity. Case report: We reported two patients admitted to the emergency room for pain and edema in the lower leg. Both patients have undergone exclusion of ruptured PA a few years before by endovascular graft. CTA showed a significant volume of fluid-corpuscular collection related to perianeurysmal abscess collection in both cases. Blood cultures and drained material cultures were positive for Staphylococcus Capitis in the first case and S. Aureus in the second. The antibiotics prophylaxis was performed for 10 days, then, patients underwent an open surgical conversion with a complete explantation of endovascular material and a femoro-popliteal bypass using an autologous vein in the first case and biological bovine pericardium prosthesis in the other case. Furthermore, we have questioned the literature and found eight more cases described in case report article referred to popliteal graft infections by different microorganisms, mostly presenting acute limb ischemia as the first symptom and suggesting endograft explantation with open conversion and autologous vein bypass as the commonest therapeutic choice. Conclusions: Open surgical conversion of popliteal endograft infection is the best strategy to manage peripheral infection of the popliteal stent.

Abstract: Congenital hyperinsulinism (CHI) is a rare disorder of glucose metabolism and is the most common cause of severe and persistent hypoglycemia (hyperinsulinemic hypoglycemia, HH) in the neonatal period and childhood. Most cases are caused by mutations in the ABCC8 and KCNJ11 genes that encode the ATP-sensitive potassium channel (KATP). We present the correlation between genetic heterogeneity and the variable phenotype in patients with early-onset hyperinsulinemic hypoglycemia caused by ABCC8 gene mutations. In the first patients, which presented persistent severe hypoglycemia since the first day of life, molecular genetic testing revealed the presence of a homozygous mutation in the ABCC8 gene [deletion in the ABCC8 gene c.(2390+1_2391-1)_(3329+1_3330- 1)del] correlated with a diffuse form of hyperinsulinism (the parents being healthy heterozygous carriers). In the second patient, the onset was on the third day of life with severe hypoglycemia, and genetic testing identified a heterozygous mutation in the ABCC8 gene c.1792C&gt;T (p.Arg598*) inherited on the paternal line, which led to the diagnosis of focal form of hyperinsulinism. To locate the focal lesions, (18)F-DOPA (3,4-dihydroxy-6-[18F]fluoro-L-phenylalanine) PET/CT was recommended (an investigation that cannot be carried out in the country), but the parents refused to carry out the investigation abroad. In this case, early surgical treatment could have been curative. In addition, the second child also presented secondary adrenal insufficiency requiring replacement therapy. At the same time, she developed early recurrent seizures that required antiepileptic treatment. We emphasize the importance and of molecular genetic testing for diagnosis, management and genetic counseling in patients with HH.

This study investigates stress perceptions, somatization, and coping strategies among 271 nursing and dentistry (n=126 and n=145, respectively), undergraduate and postgraduate students. For the study, an e-questionnaire was designed in a two-step process, uploaded in Google Forms and staying open for submissions during February 2024 in the School of Health Sciences, National and Kapodistrian University of Athens. In our sample, predominantly female (68.60%), the majority were undergraduates (80.10%). Females reported higher stress somatization (M=10.22, SD=5.23) compared to males (M=7.94, SD=6.14), with a moderate effect size (Cohen’s d=0.412, p

The structure and physicochemical properties of polyvinyl alcohol (PVA) and PVA/glycerol films have been investigated by Fourier-transform infrared spectroscopy (FT-IR), X-ray Diffraction (XRD), thermogravimetry/differential thermal analysis (TG/DTA), and advanced scanning probe microscopy (SPM). In the pure PVA films, SPM allowed us to observe ribbon-shaped domains with a different frictional and elastic contrast, which apparently originated from a correlated growth or assembly of PVA crystalline nuclei located within individual PVA clusters. The incorporation of 1.0 wt.% glycerol led to modification in shape of those domains from ribbon-like in pure PVA to rounded in PVA/glycerol 1.0 wt.% films; changes in the relative intensities of the XRD peaks and a decrease of the amorphous halo in the XRD pattern were also detected, while the DTA peak corresponding to the melting point remained at almost the same temperature. For higher glycerol content, FT-IR revealed additional glycerol-characteristic peaks presumably related to the formation of glycerol aggregates, and XRD, FT-IR, and DTA all indicated a reduction in crystallinity. For more than 2.0 wt.% glycerol, the plasticization of the films complicated the acquisition of SPM images without tip-induced surface modification. Our study contributes to the understanding of crystallinity in PVA and how it is altered by a plasticizer such as glycerol.

The surface velocity of the Amery Ice Shelf (AIS) is vital to assessing its stability and mass balance. Previous studies have shown that the AIS basin has a stable multi-year average surface velocity. However, spatiotemporal variations in the surface velocity of the AIS and the underlying physical mechanism remain poorly understood. This study combined offset tracking and DInSAR methods to extract the monthly average surface velocity of the AIS and obtained the inter-annual surface velocity from the ITS_LIVE product. An uneven spatial distribution in inter-annual variation of the surface velocity was observed between 2000–2022, although the magnitude of variation was small at less than 20.5 m/yr. The increase and decrease in surface velocity on the eastern and western-central sides of the AIS, respectively, could be attributed to the change in the thickness of the AIS. There was clear seasonal variation in monthly average surface velocity at the eastern side of the AIS between 2017–2021, which could be attributed to variations in the area and thickness of fast-ice and also to variations in ocean temperature. This study suggested that changes in fast-ice and ocean temperature are the main factors driving spatiotemporal variation in the surface velocity of the AIS.

Studies have demonstrated the critical role that safety plays in preserving favourable working conditions in the construction industry, which is necessary to accomplish goals. The aim of this research was to inform stakeholders in the construction industry in developing nations about the value of safety and possible strategies for influencing their opinions regarding safety protocols. The importance of safety to the construction sector, which is crucial to the advancement of the country, has also been emphasized. However, due to a lack of adequate safety understanding among stakeholders in the construction sector, the construction industry is characterized by a great deal of instability and hazard. To determine what factors, affect productive working conditions in the construction production, this study examined safety. A conceptual framework for safe working conditions in the construction sector was developed by considering several aspects, including the person dimension, environment factor, safety behaviour, organization features, technology, and incentives. It is now imperative to bring in these dimensions to improve the safe working conditions in the construction industry, particularly in the developing countries. By putting these safety factors into practice, the construction industry can reduce safety risks, lower the number of accidents and fatalities, cut expenses related to subpar safety performance, safeguard the reputation of construction companies, boost employee morale and satisfaction with their work, enhanced employee retention, reduced absenteeism, and enhanced sustainability goals. Additionally, the proposed conceptual framework is assured to be new and to be widely accepted in the developing nations. Based on this assumption the conceptual framework is designed.

Diarrhoea is a serious cause of mortality worldwide that can lead to dehydration, gut barrier function impairment, nutrient malabsorption, and alterations of the gut microbiota (dysbiosis). Current solutions for its management, such as oral rehydration salts (ORS), inhibitors of the gut motility, antibiotics, and living probiotics only partially counteract the mechanisms of the disease and do not provide a full coverage of the problem. Potential risks of the use of living probiotic strains, particularly in immunocompromised patients, can be eliminated with the use of tyndallized (heat-killed) postbiotic bacteria and yeast. ABB C22® is a postbiotic combination of three tyndallized yeasts, namely Saccharomyces boulardii, Saccharomyces cerevisiae, and Kluyveromyces marxianus. To assess the action of the postbiotic combination on diarrhoea, immune and gut epithelial cell signaling assays, gut barrier formation assay and rotavirus gene expression assay were performed. ABB C22® showed a strong anti-inflammatory effect, an induction of the build-up of the gut epithelium, and a degree of protection against rotavirus infection. These experimental studies support the use of the postbiotic ABB C22® as a solution for the management of diarrhoea and gastrointestinal conditions, alone or in combination with existing but incomplete treatments.

The sexual reproduction of ascomycetes is controlled by the mating-type(MAT) locus. (Pseudo)homothallic reproduction has been hypothesized based on genetic data from Hirsutella sinensis (Genotype #1 of Ophiocordyceps sinensis). In this study, the differential occurrence of the mating-type genes in the MAT1-1 and MAT1-2 idiomorphs, which constitute the genetic control of O. sinensis reproduction, was analyzed in 237 strains and 5 genomes of H. sinensis. The mating-type genes were differentially transcribed in H. sinensis Strain L0106, and the MAT1-2-1 transcript in H. sinensis Strain 1229 was alternatively spliced, with an unspliced intron I containing stop codons; these findings constitute O. sinensis reproduction controls at the transcriptional and coupled transcriptional-translational levels. The occurrence of the mating-type genes and a-/α-pheromone receptor genes differed among the 5 H. sinensis genomes. The differential occurrence and transcription of these genes are inconsistent with self-fertilization under (pseudo)homothallism but suggest self-sterility of H. sinensis, which therefore needs a mating partner to achieve sexual outcrossing under heterothallism or hybridization. Consistent occurrence and transcription of the mating-type genes of both the MAT1-1 and MAT1-2 idiomorphs have been reported in natural and cultivated Cordyceps sinensis, but these findings are insufficient to determine the reproduction mode(s) because of the co-occurrence of >90 fungal species and multiple genomically independent genotypes of O. sinensis. The mutant MAT1-1-1 and α-pheromone receptor transcripts in natural C. sinensis result in the N-terminus or middle-truncated proteins, with changes in hydrophobicity and secondary structures of the proteins, suggesting heterogeneous fungal source(s) of the proteins and hybridization reproduction.

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.

This is a single tertiary center in southwest Romania, population-based, study. We retrospectively compared data obtained in two periods: January 2008 - December 2013 and January 2018 - De-cember 2023, respectively. The global incidence of transposition of great arteries (TGA) - the ter-minated cases added to the ones resulting in live-born pregnancies, remained almost constant. The live-born incidence of TGA decreased (0.34% to 0.013%). The prenatal detection rate increased slightly (71.42% to 87.5%). The median gestational age at the diagnosis decreased, from 29.3 ges-tational weeks (mean 25.4) to 13.4 weeks (mean 17.2). The proportion of terminated pregnancies significantly increased (14.28% to 75%, p = 0.019).

The rapid evolution of digital payment systems has significantly transformed the retail industry, reshaping how transactions are processed and enhancing the overall shopping experience. This research explores the adoption of digital payment technologies in retail settings, focusing on the motivations, challenges, benefits, and impacts associated with their implementation. Retailers are increasingly driven by the need to meet consumer demands for convenience and speed, as well as to gain a competitive edge in a saturated market. Digital payment systems, including mobile wallets, contactless payments, and online payment gateways, have become essential tools for retailers seeking to streamline operations and improve customer satisfaction. The study highlights several key findings, including the challenges related to technical integration, security concerns, and cost implications. Technical difficulties in integrating new payment systems with existing point-of-sale infrastructure, coupled with the need for robust security measures, present significant obstacles. Despite these challenges, the long-term benefits of digital payments, such as increased transaction volumes, higher average transaction values, and valuable data insights, often outweigh the initial investment and ongoing costs. The research also underscores the positive impact of digital payments on customer behavior, noting improvements in transaction speed, convenience, and overall satisfaction. Furthermore, the findings reveal variations in the impact of digital payment systems across different retail settings, including large department stores, independent retailers, and e-commerce businesses. The ability to tailor digital payment solutions to the specific needs of each retail environment is crucial for maximizing benefits and ensuring successful implementation. Overall, the study provides a comprehensive understanding of the role of digital payment systems in modern retail and offers insights into the future trajectory of these technologies.

This research explores the profound impact of digital transformation on marketing strategies within the retail sector. As the retail landscape evolves, digital technologies have become central to understanding and engaging with consumers. The study examines how data-driven decision-making, enabled by advanced analytics, has replaced traditional intuition-based methods, leading to more targeted and efficient marketing strategies. Personalization has emerged as a key trend, with retailers leveraging customer data to offer tailored experiences, fostering greater customer satisfaction and loyalty. The adoption of omnichannel marketing strategies is highlighted as crucial for providing a seamless and consistent customer experience across various touchpoints, integrating both online and offline channels. The role of social media as a powerful platform for brand engagement and community building is discussed, emphasizing its importance in modern marketing. Mobile technologies are identified as transformative, offering convenience and enhancing the shopping experience through mobile apps and payment solutions. The research also explores the emergence of new business models, such as subscription services and direct-to-consumer approaches, which have been enabled by digital technologies. Challenges associated with digital transformation, including the need for continuous technological investment, skill development, and data privacy concerns, are also addressed. The study concludes by highlighting the acceleration of digital adoption due to the COVID-19 pandemic and the potential of emerging technologies like artificial intelligence, virtual reality, and blockchain to shape the future of retail marketing. The findings underscore the necessity for retailers to embrace digital transformation to remain competitive and responsive to changing consumer behaviors.

This qualitative study explores Supplier Relationship Management (SRM) in the context of digital transformation within e-commerce businesses, focusing on the implications of emerging digital technologies. Through semi-structured interviews and documentary analysis, the study examines how organizations leverage technologies like artificial intelligence (AI), blockchain, and Internet of Things (IoT) to enhance SRM practices. Findings reveal that digitalization enables improved operational efficiencies, real-time data analytics, and expanded supplier networks through platforms such as Amazon Business and Alibaba. Strategic shifts are noted towards collaborative supplier relationships, emphasizing joint innovation, technology co-investment, and shared risk management strategies. However, the implementation of digital SRM strategies presents challenges, including data privacy concerns, integration complexities, cybersecurity threats, and regulatory compliance issues. Organizations must navigate these challenges by developing robust governance frameworks and cybersecurity protocols to protect sensitive information and ensure regulatory adherence. Key performance indicators (KPIs) such as supplier performance, cost savings, innovation impact, risk management effectiveness, and sustainability integration are critical for evaluating the success of digital SRM initiatives. Strategically, businesses are advised to prioritize leadership commitment, cross-functional collaboration, and continuous learning to optimize digital SRM practices effectively. Future research should explore emerging trends in digital SRM, investigate technological impacts on supplier dynamics and organizational performance, and examine ethical considerations in digitalization. By integrating these insights into strategic decision-making, businesses can enhance supplier relationships, mitigate operational risks, and achieve sustainable growth in the global marketplace.

This qualitative research explores the role of digital transformation in procurement through insights gathered from interviews with industry leaders. Digital technologies such as Robotic Process Automation (RPA), Big Data Analytics, Artificial Intelligence (AI), and Blockchain are examined for their impact on operational efficiency, strategic procurement management, supplier relationships, and sustainability practices. Findings reveal that these technologies streamline processes, enhance decision-making capabilities, and improve transparency, thereby enabling organizations to achieve greater agility and competitive advantage. Strategic factors including leadership commitment, strategic investments, and digital skills development emerge as crucial for navigating challenges such as organizational resistance, data security, and integration complexities. The study underscores the transformative potential of digital transformation in procurement, emphasizing its role in driving innovation, fostering sustainability, and aligning procurement practices with broader organizational goals. Looking forward, the research suggests that organizations must continue to innovate and adapt to technological advancements to maintain resilience and leadership in a digital economy. By integrating digital strategies effectively, organizations can enhance operational efficiencies, strengthen supplier relationships, and promote ethical business practices, ultimately positioning themselves for sustained success.

This qualitative investigation delves into the intricate interplay of cultural factors within supply chain integration, aiming to elucidate how diverse cultural dimensions shape communication dynamics, decision-making processes, and collaborative efforts among global supply chain practitioners. Employing a phenomenological approach, the study explores the lived experiences and subjective perceptions of supply chain managers and professionals, providing nuanced insights into the profound impact of national culture, organizational culture, digital technologies, and sustainability considerations on supply chain operations. Key findings highlight the significant influence of national culture, as conceptualized by Hofstede's cultural dimensions, on supply chain interactions. These dimensions—such as power distance, individualism versus collectivism, uncertainty avoidance, and masculinity versus femininity—play crucial roles in shaping how individuals perceive authority, manage risks, negotiate agreements, and engage in collaborative practices across diverse cultural contexts. Cultural norms and values deeply influence both intra-organizational dynamics and inter-organizational relationships within global supply chains. Organizational culture emerges as a pivotal determinant of supply chain integration success. Cultures characterized by transparency, trust, inclusivity, and a commitment to continuous improvement create environments conducive to effective cross-border collaboration. Conversely, organizations with conflicting or ambiguous cultural norms face challenges in aligning goals, fostering mutual understanding, and maintaining open communication channels with international partners. The study underscores the transformative role of digital technologies in enhancing supply chain management practices. Technologies such as blockchain, artificial intelligence, and the Internet of Things improve supply chain visibility, optimize operations, and enhance decision-making processes, strengthening resilience against disruptions. The adoption and integration of these technologies depend on organizational readiness, technological capabilities, and cultural acceptance within supply chain networks. Cultural factors influence the adoption pace, implementation strategies, and utilization patterns of digital technologies, emphasizing the need for strategic alignment between technological investments and cultural norms to maximize their potential benefits. Furthermore, sustainability considerations are critical in shaping cultural values and priorities within supply chain networks. Organizations increasingly recognize the importance of environmental sustainability, ethical sourcing practices, and corporate social responsibility in shaping their reputations, stakeholder relationships, and long-term business goals. Cultural factors significantly influence organizations' approaches to sustainability, guiding their strategic decisions and operational practices.

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

Solar radiation impacts diverse aspects of city life, such as harvesting energy with PV panels, passive heating of buildings in winter, cooling loads of air-conditioning systems in summer, and urban microclimate. Urban digital twins and 3D city models can support solar studies in the process of urban planning and provide valuable insights for data-driven decision support. This study examines the calculation of solar incident radiation at the city scale in Sofia using remote sensing data for the large shading context in a mountainous region and 3D vector building data. The building footprints and the terrain are preprocessed using GIS. The solar calculation uses a raytracing algorithm in 3D CAD at the city scale. To evaluate the performance, two cases were considered at the city and district scale, respectively. The total face count of meshes for the simulations constituted approximately 2 000 000 faces. 64379 roofs for the whole city and 4796 buildings for one district were selected. All calculations were performed in one batch and visualized in a 3D web platform. Using a 3D CAD environment establishes a seamless process of updating 3D models and simulations while preprocessing in GIS ensures working with large-scale datasets. The proposed method showed moderate computation time for both cases and could be extended to include reflected radiation and dense photogrammetric meshes in the future.

The adoption of Industry 4.0 technology is essential for achieving energy efficiency, a critical aspect of Sustainable Development Goal (SDG) no. 12 aimed at addressing climate change. Understanding the impact of Sustainable Attitudes, Digital Trust, and Uncertainty Avoidance is crucial in the decision-making process regarding the implementation of Industry 4.0 technology. The slow adoption of Industry 4.0 technology is attributed to non-technological factors or people, while the need to achieve energy efficiency targets necessitates examining the influence of Sustainable Attitudes and Digital Trust as supporting variables, along with Uncertainty Avoidance, on technology adoption. The study employed a quantitative explanatory research approach to investigate the relationships and impacts of variables. It centered on employees of Schneider Electric Indonesia, a global Industry 4.0 technology provider, with a sample of 189 employees selected through random sampling, incorporating a 5% margin error from Slovin formula. The analysis involved the use of Structural Equation Modeling (SEM) with AMOS version 26. Industry 4.0 technology is essential for fostering innovation and business expansion, leading to a sustainable environment by offering principles, guidance, and energy-efficient technologies for companies. The involvement of Sustainable Attitude, moderated by Uncertainty Avoidance, is vital in reaching energy efficiency goals through Industry 4.0 implementation.

The COVID-19 pandemic has led to a substantial revolution in the incorporation of digital solutions in healthcare. This systematic review investigates the enduring physical and psychological consequences individuals experience up to two years post-recovery. Additionally, it focuses on examining the influence of mHealth interventions on these effects. Significantly, 41.7% of survivors experience lingering symptoms that have not been addressed, while 14.1% encounter difficulties in returning to work. The presence of anxiety, compromised respiratory functioning, and persistent symptoms highlight the immediate requirement for specific therapies. Telehealth, particularly telerehabilitation, presents itself as a possible way to address these difficulties. The study thoroughly examines 10 studies encompassing 749 COVID-19 patients, investigating the efficacy of telerehabilitation therapies in addressing various health markers. Telerehabilitation-based breathing exercises yield substantial enhancements in functional performance, dyspnea, and overall well-being. The results emphasize the potential of telerehabilitation to have a favorable effect on patient outcomes, however more research is needed to strengthen the existing evidence basis as one of the most important limitation is the limited number of trials and the evaluation of varied therapies. The analysis highlights the significance of digital solutions in post-COVID care and calls for ongoing research to improve comprehension and implementation of telehealth interventions in a swiftly changing healthcare environment.

This cross-sectional study investigates the impact of Virtual Private Networks (VPNs) on Nigerians during the 2021 Twitter ban, prompted by the Nigerian government's suspension of Twitter operations following the deletion of a tweet by the President. The study aims to analyze the frequency and awareness of VPN and Twitter usage among Nigerians during the ban, addressing specific objectives. Employing a survey research method, data was collected from 400 respondents through online and offline questionnaires. Descriptive statistical tools were used for data analysis, revealing significant findings. Results indicate a substantial decrease in Twitter usage during the ban, with IPVanish emerging as the preferred VPN brand. The study also highlights an increase in VPN awareness post-ban, emphasizing the need for educational initiatives. The demographic analysis reveals a dominant age group of 21 to 25 among social media users. Overall, the research underscores the complex interplay between social media dynamics, VPN adoption, and demographic factors, emphasizing the importance of nuanced regulatory approaches and ongoing user education in Nigeria's online landscape.

Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Conventional human-based approaches, however, are prone to challenges in defect detection accuracy and efficiency, primarily due to the high inspection demand from manufacturing industries with high production throughput. To solve this challenge, numerous computer-based alternatives have been developed, including Auto-mated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these algorithms demand large volumes of data that should be representative of real-world cases. However, the availability of digital X-ray radiography data for open research is limited by non-disclosure contractual terms in the industry. This study presents a pipeline that is capable of modeling synthetic images based on statistical information acquired from X-ray intensity distribution from real digital X-ray radiography images. Through meticulous analysis of the intensity distribution in digital X-ray images, the unique statistical patterns associated with the exposure conditions used during image acquisition, type of component, thickness variations, beam divergence, anode heel effect, etc., are extracted. The realized synthetic images were utilized to train deep learning models, yielding an impressive model performance with mean intersection over union (IoU) of 0.93, and mean dice coefficient of 0.96, on real unseen digital X-ray radiography images. This methodology is scalable and adaptable, making it suitable for diverse industrial applications.

The fourth industrial revolution has resulted in the digitalization of projects and operations across all sectors. Accordingly, efforts are being made to utilize advanced technologies to improve the energy efficiency of the building sector. This study has reviewed the current application and limitations of cutting-edge technologies in this regard. An overview of the use of the Internet of Things (IoT), artificial intelligence (AI), digital twin (DT), and building information modeling (BIM) for energy efficiency of buildings has been provided. It has been found that the use of Industry 4.0 technologies, during the construction and operational phase of buildings, has a great potential to reduce energy consumption and emissions of the sector. This study may help stakeholders of the built environment to understand the role of industry 4.0 tools for energy efficiency of the sector.

This paper presents a new method for the design of separable denominators 2-D IIR filters with nearly linear phase in the passband. The design method is based on a balanced-realization model reduction technique. The nearly linear-phase 2-D IIR filter is designed using 2-D model reduction from a linear-phase 2-D FIR filter, which serves as the initial filter. The structured controllability and observability Gramians $P^s$ and $Q^s$ serve as the foundation for this technique. These Gramians are block diagonal positive-definite matrices that satisfy 2-D Lyapunov equations. An efficient method is used to compute these Gramians by minimizing the traces of $P^s$ and $Q^s$ under linear matrix inequalities (LMI) constraints. The use of these Gramians ensures that the resulting 2-D IIR filter preserves stability and can be implemented using a separable denominator 2-D filter with fewer coefficients than the original 2-D FIR filter. Numerical examples show that the proposed method compares favorably with existing techniques.

In light of the recent pandemic, several COVID-19 vaccines were developed, tested and approved in a very short time, a process that otherwise takes many years. Above all, these efforts have also unmistakably revealed the capacity limits and potential for improvement in vaccine production. This review aims to emphasize recent approaches for targeted rapid adaptation and production of vaccines from an interdisciplinary multifaceted perspective. Using literature research, stakeholder analysis and a value proposition canvas, we reviewed technological innovations on the pharmacological level, formulation, validation and resilient vaccine production to supply bottlenecks and logistic networks. We identified four main drivers to accelerate the vaccine product life cycle: computerized candidate screening, modular production, digitized quality management and a resilient business model with corresponding transparent supply chains. In summary, the results presented here can serve as a guide and implementation tool for flexible, scalable vaccine production to respond swiftly to pandemic situations in the future.

In response to the growing inspection demand exerted by process automation in component manufacturing, Non-destructive testing (NDT) continues to explore automated approaches that utilize deep learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep learning models. This study investigates the influence of two image quality parameters, namely Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), on the performance of U-net deep learning segmentation model. Input images were acquired with varying combinations of exposure factors such as kilovoltage, milli-ampere, and exposure time, which altered the resultant quality. The data was sorted into 5 different datasets according to their measured SNR and CNR values. The deep learning model was trained 5 distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection over Union (IoU) metric of 0.9594 on test data of the same category but drops to 0.5875 when tested on lower CNR test data. The result in this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters, to enhance the performance of deep learning segmentation models in NDT radiography applications.

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS.

Every month teachers face the dilemma of what exercises should their students practice, and what their consequences are on long-term learning. Since teachers prefer to pose their own exercises, this generates a large number of questions, each one attempted by a small number of students. Thus, we couldn’t use models based on big data such as deep learning. Instead, we developed a simple to understand state-space model that predicts end-of-year national test scores. We used 2,386 online fourth-grade mathematic questions designed by teachers and each attempted by some of the 500 students in 24 low socioeconomic schools. We found that the state-space model predictions improved month-by-month and that in most months it outperformed linear regression models. Moreover, the state-space estimator provides for each month a direct mechanism to simulate different practice strategies and compute their impact on the end-of-year standardized national test. We built iso-impact curves based on two critical variables: the number of questions solved correctly in the first attempt and the total number of exercises attempted. This allows the teacher to visualize the trade-off between asking students to do exercises more carefully or doing more exercises. To the best of our knowledge, this model is the first of its kind in education. It is a novel tool that supports teachers drive whole classes to achieve long-term learning targets.

The role of Social Media Marketing (SMM) in marketing strategies is rapidly growing. Because the use of social media is growing, the industry of SMM will grow bigger in the coming years; the pace of this growth is faster than ever. To survive in the modern competitive world, effective use of SMM for a firm is a must; for that, every SMM channel needs to be used to its full potential. For a marketing campaign to be effective, there is a need for some metrics to measure the success of the SMM campaign. These metrics measure if the campaign is successfully implemented or not. This would help firms understand the market, gain a competitive advantage, and ultimately get a positive impact on the overall business. This study categorizes SMM strategy into 4 dimensions and associates 10 broad categories of SMM metrics to these dimensions. The proposed model of this study suggests the application of Social Media Analytics (SMA) ineffective use of metrics to measure SMM campaigns. There are so many SMA Tools available for free and time-efficient data analysis that can lead to faster and better results than manual analysis. Following this model, the importance of SMA tools in devising an effective SMM strategy is highlighted. The implication of this research is towards a better understanding of the application of SMA for any firm to have a solid SMM Strategy, especially small and medium-sized enterprises that have limited resources.

This paper considered the issue of agricultural fields boundary recognition in satellite images. A novel algorithm based on the aggregated history of vegetation index data obtained via open satellite data, Sentinel-2, was proposed. The proposed algorithm included several basic steps, namely the detection of parcel regions on aggregated index data; the calculation of aggregated edge maps; the segmentation of parcel regions using the edges obtained; the computation of connected components and their contour extraction. In this paper, we showed that the use of aggregated vegetation index data and boundary maps allow for much more accurate agricultural field segmentation compared to the instant vegetation index approach. The quality of segmentation within regions of Russia and the Ukraine was estimated. The dataset that was used and Python implementation of the proposed algorithm were provided.

Aim: The main purpose of this feasibility study was to estimate compliance and acceptability rate of a 9-Steps mobile app preparation program. Subsequently, to assess its impact regarding attitudes towards smoking cessation. Methods: The program was only available on versions above iOS 13.3. The ecological study collected user’s data via the app from 04/07/2021 to 28/07/2021. Results: A total of 2331 ecological users completed onboarding; 1926 users (73.9%) completed the step 0 and only 265 users continued in step 1. On average, participants actively engaged with intra-steps activities (85,1%). Most users were highly satisfied at the end of each step. On average 86% found the content of each step comprehensive and relevant. Mann-Whitney tests demonstrated positive changes in attitudes toward smoking cessation on the less confident users: willing (3 to 5, p <.05); confidence (3 to 5, p <.001) and readiness to quit (3 to 5, p <.001). Conclusions: This ecological study corroborates the expected dropout between first use and regular use of health mobile apps. However, engagement and satisfaction rates support the feasibility and acceptability of a 9-step preparation program. Additional research is needed to better understand determinants of regular use of mobile apps and its impact on the target behavior.

Background: The reference test for SARS-CoV-2 detection is the reverse transcriptase real time PCR (real time RT-PCR). However, evidences reported that real time RT-PCR has a lower sensitivity compared with the droplet digital PCR (ddPCR) leading to possible false negative in low viral load cases. Methods: We used ddPCR for viral genes N1 and N2 on 20 negative (no detection) samples from symptomatic hospitalized COVID-patients presenting fluctuating real time RT-PCR results and 10 suspected samples (Ct value>35) from asymptomatic not hospitalized subjects. Results: ddPCR performed on RNA revealed 65% of positivity for at least one viral target in the hospitalized patients group of samples (35% for N1 and N2, 10% only for N1 and 20% only for N2) and 50% in the suspected cases (30% for N1 and N2, while 20% only for N2). On hospitalized patients’ samples, we applied also a direct ddPCR approach on the swab material, achieving an overall positivity of 83%. Conclusion: ddPCR, in particular the direct quantitation on swabs, shows a sensitivity advantage for the SARS-CoV-2 identification and may be useful to reduce the false negative diagnosis, especially for low viral load suspected samples.

Current acceleration in digital innovations, the unexpected challenges in our social interactions, open access to virtualization, huge limitation in our physical spaces, and unpredictable changes in our old lifestyles - as originated from the COVID-19 global pandemic in 2020 - continue to provide us with a framework, rapidly updating under our eyes, of the modifications our world is undergoing by pursuing into the “new digital age”. Or, as many scholars say nowadays, into the new normal! These are shared and deep changes that, regardless of their permanence or temporariness in the time, concretely stress, ever more greatly, their “own” effects on how ideally a city should function. Forcing us to reflect on the real ability to achieve choices and visions for the future by taking vantage from the new digital platforms. In the pages of this article authors, through different eyes but sharing an early response to the matter of new Governance, explore the theme of a radical change of those already consolidated paradigms and, therefore, of the innovations that are transforming the way we understand our society and its technologic advancements, economics, and culture, as defined through dimensions of time and space. This article identifies a methodologic vision for acquiring a more democratic and participatory (inclusive) dimension in the newest configuration of contemporary cities, the new smart city, and in the possible innovations in reading the common sentiments and wishes through the new digitalized world. The analysis investigates how ICT is altering the meanings/ideas of “urban planning”, driving us toward a more effective “governance” through a citizen-centred digital approach. Indeed, city governance's success must be measured based on the “listening capacity” of the inhabitants and the facilities that we are capable to provide to citizens. “Sentiment Analysis” tool is tested as a useful tool to achieve these aims.

Methods for cost calculation of Laser-based Additive Manufacturing (LAM) have evolved over the last two decades. Starting from a rapid prototyping manufacturing method, cost calculation over time included more than single part production for small series or mixed batches. New machine developments nowadays aim at large scale production and introduce modular machine designs that give LAM factory operators more decision freedom on machine configurations and expansibility. To leverage this new possibilities, LAM costing methods must be adapted to calculate and visualize the economic consequences of different module configurations.In this work a modelling architecture of L-PBF machines using virtual modules is presented. Using principles of the industry 4.0 communication standard OPC UA, a virtual machine is set up, parametrized and connected to the AM digital process chain. Two different views on the cost information are used, optimized for different use cases.First, the product view allows detailed analysis of cost allocation to parts in mixed batches and the layered geometry of the part. Second, the machine view tracks utilization of all machine modules, divided into the states idle, active, off and maintenance. The theoretical Overall Equipment Effectiveness (OEE) is calculated on a module basis, enabling economic analysis and optimization of module composition as well as module improvements and new modular machine constellations. This allows focusing research and development on most valuable improvements. By calculating different scenarios like spare part production, mass production or rapid prototyping, the part portfolio to be manufactured can be considered.

The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resulting psychological and economic sequela. As countries start to ease lock-down measures and reopen industries, ensuring a healthy workplace for employees has become imperative. Thus, this paper presents a mobile app-based intelligent portable healthcare (pHealth) tool, called iWorkSafe, to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care. Developed mainly for low-end Android devices, the iWorkSafe app hosts a fuzzy neural network model that integrates data of employees' health status from the industry's database, proximity and contact tracing data from the mobile devices, and user-reported COVID-19 self-test data. Using the built-in Bluetooth low energy sensing technology and K Nearest Neighbor and K-means techniques, the app is capable of tracking users’ proximity and trace contact with other employees. Additionally, it uses a logistic regression model to calculate the COVID-19 self-test score and a Bayesian Decision Tree model for checking real-time health condition from intelligent e-health platform for further clinical attention of the employees. Rolled out in an apparel factory on 12 employees as a test case, the pHealth tool generates an alert to maintain social distancing among employees inside the industry. In addition, the app helps employee to estimate risk with possible COVID-19 infection based on the collected data and found that the score is effective in estimating personal health condition of the app user.

Educating for Sustainability involves promoting sustainable competences in students. Not in vain, wider societal changes that ensure a balance between economic growth, respect for the environment and social justice must start with individual actions, implying knowledge, capacity and willingness to act. However, and although there is wide consensus that education should promote the development of competences for life, putting this theoretical tenet into may entail more problems. Competence is most often expressed in general terms without a specific definition of the intervening elements (knowledge, skills, values, attitudes), which may collide with the necessity of teachers – as learning planners - concrete entities on which to base their process of design. So that, in this work we propose a series of indicators that serve to characterize the four dimensions of scientific competence – contents of science, contents about science, value of science and utility of science-. Although they are primarily intended to be used to filter multimedia resources in an educational platform, this proposal of indicators can be extrapolated to the management and selection of a variety of resources and activities, and for sharing the objectives and evidences for the acquisition of competencies.

Space-based Interferometric Synthetic Aperture Radar (InSAR) applications have been widely used to monitor the cryosphere over past decades. Because of temporal decorrelation, interferometric coherence often severely degrades on fast moving glaciers. In addition, higher sensitivity ambiguity occurs in large baseline configurations, which are needed for extracting topographic information over low relief areas such as the surface of a glacier. TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) observations, which overcome the temporal decorrelation because of their simultaneous measurements by two satellite constellations, have used a short baseline sufficient for generation of excellent digital elevation models in most locations around the world. However, it remains difficult to estimate detailed topographic characteristics over low slope glacier surfaces because of the relatively less sensitive height ambiguity from the small baselines. In this study, we used the TanDEM-X pursuit monostatic mode with large baseline formation following a scientific phase timeline to develop highly sensitive topographic elevation models of the Petermann Glacier of Northwest Greenland. As expected, coherent interferometric phases (> 0.8) were well maintained over the glaciers despite their fast movement thanks to the nearly simultaneous TanDEM‐X measurements. The height ambiguity, which defined as the altitude difference correspondent to phase change of flattened interferogram, of the dataset was 10.63 m, which is favorable for extracting topography in a low relief region. We validated the TanDEM‐X derived glacial topography by comparing it to the SAR/Interferometric radar altimeter observations acquired by CryoSat‐2 and the IceBridge Airborne Topographic Mapper laser altimeter measurements. Both observations showed very good correlation within a few meters of the offsets (‐12.5 – ‐3.1 m) with respect to the derived glacial topography. Because of highly sensitive ambiguity, we could successfully extract detailed geomorphological features on the glaciers. Routine TanDEM-X observations will be very useful to better understand the dynamics of glacial movements and topographic change.

The latest developments in digital humanities have increasingly enabled the construction of large data sets which can easily be accessed and used. These data sets often contain indirect localisation information, such as historical addresses. Historical geocoding is the process of transforming the indirect localisation information to direct localisation that can be placed on a map, which enables spatial analysis and cross-referencing. Many efficient geocoders exist for current addresses, but they do not deal with temporal information and are usually based on a strict hierarchy (country, city, street, house number, etc.) that is hard, if not impossible, to use with historical data. Indeed, historical data are full of uncertainties (temporal, textual, positional accuracy, confidence in historical sources) that can not be ignored or entirely resolved. We propose an open source, open data, extensible solution for geocoding that is based on gazetteers composed of geohistorical objects extracted from historical topographical maps. Once the gazetteers are available, geocoding an historical address is a matter of finding the geohistorical object in the gazetteers that is the best match to the historical address searched by the user. The matching criteria are customisable and include several dimensions (fuzzy string, fuzzy temporal, level of detail, positional accuracy). As the goal is to facilitate historical work, we also propose web-based user interfaces that help geocode (one address or batch mode) and display over current or historical topographical maps, so that geocoding results can be checked and collaboratively edited. The system has been tested on the city of Paris, France, for the 19th and the 20th centuries. It shows high response rates and is fast enough to be used interactively.

The stability assessment of aged tunnel linings were mainly evaluated based on the visual inspection, and the Tunnel-lining Crack Index (TCI) is one of the most widely used tunnel lining health assessment indexes in Japan. However, the intersection and distribution of cracks, which can influence the stability of tunnel lining greatly, were not considered in the TCI. A new method was proposed for the health assessment of tunnel lining, which evaluate the lining states according to the fractal dimension of cracks. Based on the machine vision-based method, the crack image can be extracted efficiently. The fractal dimension of lining cracks in one span can be obtained in a few minutes. A series of comparative tests and field tests were conducted to evaluate the validity of this new method. The comparative tests confirmed that fractal dimension can characterize the density, width, and distribution of cracks. The results also certificated that the influence of crack width is larger than the crack density. The intersection of cracks, which will increase the risk of lining collapse, can also increase the fractal dimension, whereas the TCI keep constant. The fractal dimensions of tunnel lining cracks were obtained according to the digital inspection test of Hidake Tunnel in Japan for all the 65 spans. Moreover, the TCI was obtained through statistical methods. The correlation between fractal dimension and TCI of tunnel lining was studied. The significance of the new evaluation index is that it can identify some unusual spans of tunnel lining and provide a basis for further internal testing. As a complement to the conventional visual inspection method, the fractal dimension of the cracks is a promising health assessment index.

Abstract: In this paper we describe how the administration of drone brood homoge-nate (DBH) influenced intestinal secretion of IgA, MUC-2, morphometric parameters, peripheral blood values, and expression of cytokines. A total of 12 pigs were divided into three groups, two experimental, first fed with 100 mg DBH.kg-1 (E1), second with 200 mg DBH.kg-1 (E2) of body weight, and a control (C) group without DBH in the di-et. Animals didn't show significant changes in average weight gains and feed conver-sion among evaluated groups. Feeding a diet containing 200 mg DBH.kg-1 of body weight in the E2 group produced an increase (p < 0.05) in the red cell distribution width, the percentage of metabolic activity of blood phagocytes in the peripheral blood, as well as splenic cell subpopulations CD4+ and CD21+. The villi height and their cutting surface were increased in the E2 group (p < 0.01), together with a gradual decrease in the concentration of MUC-2 and sIgA (p < 0.001). Upregulation of IL-8 (p < 0.0001), IL-10 (p < 0.05), and IL-18 (p < 0.001) was found mainly in E2 group. Results suggest, that supplementation of DBH in the feed of pigs formed optimal intestinal homeostasis.drone

Lynch syndrome (LS) is an inherited cancer predisposition disorder associated with an elevated risk of various epithelial cancers. Despite sharing the same pathogenic variant (PV), Lynch syn-drome variant heterozygotes (LSVH) exhibit considerable phenotypic variability in cancer risk. The role of Human Leukocyte Antigen (HLA) in modifying cancer risk prompted our investiga-tion into whether HLA variations act as genetic modifiers influencing age at cancer diagnosis in a unique cohort of LSVH carrying a PV in the hMLH1 gene in South Africa. Within our extensive LS cohort, 426 individuals carried the same hMLH1 PV (MLH1:c.1528C>T). We selected 100 indi-viduals with the greatest diversity in age at cancer diagnosis and the oldest unaffected individu-als for high-throughput HLA genotyping of 12 HLA class I and II loci using next-generation se-quencing. Statistical analyses employed Kaplan-Meier survival analyses with Logrank tests and Cox proportional hazards. Following the robust application of statistical correction methods, six HLA alleles (3.2%) were significantly associated with a young age at cancer diagnosis. Notably, HLA-B*15:17 and HLA-DPB1*55:01 correlated significantly with very young colorectal cancer (CRC) diagnosis (Mean age: 21y [17-25]; HR = 71.59; q

Water quality assessment in pastoral of Tibetan Plateau, which is water sources for about 40% of world's population and the runoff-yield area of Yellow rivers, is very important. In this paper, Xiahe county which belongs to Tibetan Plateau pastoral was investigated. Six parameters(via, chloride, COD, ammonia nitrogen, nitrate, fluoride, sulfate) were selected to assess the water quality and health degree by using fuzzy comprehensive evaluation methods. The hydrochemical type in surface water was of HCO3--Mg2+-Ca2+ type. The cations and anions in surface water were mainly from weathering and dissolution of carbonate rock. Results showed that the water quality in all 69 sampling sites was all of class Ⅰ. The integrated health degree reached more than 0.85 and the health rate were 100%. Although ammonia nitrogen was regarded as the main contamination factor, but it had a little effect on the entire body of water. Overall, the surface water qualities of most samples in Xiahe County was good condition.

Hyaluronic acid, in the form of a gel or viscoelastic colloidal solution, is currently used for viscosupplementation of joints affected by osteoarthritis, but its effectiveness is under debate in relation to newer alternatives. Based on meta-analytical arguments, the present article reinforces the opinion that there are still no decisive arguments for its complete replacement, but for its use adapted to the peculiarities of the disease manifestation and of the patients. A “broad” comparison is first made with almost all alternatives studied in the last decade, and then a meta-regression study is performed to compare and predict the effect size induced by viscosupplementation and platelet-rich plasma therapies. If they are computerized, the developed models can represent tools for clinicians in determining the appropriateness of the option or not for viscosupplementation, in a manner adapted to the pain felt by the patients, to their age, or to other clinical circumstances. The models were generated using the latest algorithms for meta-analysis and meta-regression implemented in the R language and assembled in different R packages. All primary data and necessary R scripts are provided in accordance with the philosophy of reproducible research. Finally, HA-based products, currently under circumspection, are still clinically useful.

D-dimer testing in combination with clinical assessment is commonly used to diagnose suspected DVT and pulmonary embolism. However, as D-dimer levels are physiologically higher in elderly patients, this test loses clinical specificity in the setting of geriatric patients. The aim of our study was to investigate the changes in D-dimer levels with age and the specificity of the D-dimer test in the diagnosis of pulmonary embolism. 1617 elderly patients consecutively admitted to the Geriatric Emergency Care Department of the INRCA-IRCCS hospital between January 2013 and February 2014 and for whom D-dimer results were available were included. The D-dimer immunoturbidimetric assay was performed on a cs-2100i Sysmex Siemens using a conventional cut-off level of 500 µg/L. The median D-dimer level was 1200 [550-2990] µg/L FEU, and the percentage of patients with D-dimer levels below the conventional cut-off was only 22.1%. As expected, there was a significant age-related increase in D-dimer, even when sex was considered, and a significant decrease in the specificity of D-dimer assay with age. The most established use of D-dimer testing is in the diagnosis of deep vein thrombosis or pulmonary embolism. Our study confirms the need for an age-adjusted cut-off for D-dimer testing in geriatric patients.

Periodontitis is a chronic inflammatory disease which results in the destruction of the tooth’ s supporting tissues and the alveolar bone resorption. The complement becomes a major link between infection and inflammatory pathology including periodontitis. Gingipians as important virulence factors of P. gingivalis have the activity of C5 convertase, could cleave C5 into fully functional C5a to activate C5aR. The above process could be blocked by the C5aR antagonist (PMX-53) to suppress local periodontal inflammation, and then achieves the purpose of treatment of periodontitis. Nanoparticles incorporated within gelatin are promising carrier system for drug delivery in recent years. This study aimed to investigate whether gelatin nanoparticles loaded with PMX-53 prevents alveolar bone resorption in miniature swines model of periodontitis. Four miniature swines were placed ligatures around the maxillary and mandibular fourth premolar and first molar on both sides for seven weeks to induce periodontitis. Then, animals were assigned randomly to four groups: minocycline-treated group, gelatin with PMX53-treated group, gelatin-treated group and a sham control group. They were treated with 1ml related drugs respectively, into gingival sulcusl for 4 times at one-week intervals. We showed that local treatments with gelatin nanoparticles loaded with PMX-53 could inhibit alveolar bone loss of periodontitis. Our study revealed that gelatin nanoparticles loaded with PMX-53 prevented alveolar bone resorption miniature swines model of periodontitis. In addition, provided proof-of-concept for local targeting of gelatin nanoparticles loaded with PMX-53 as a powerful candidate for the treatment of periodontitis.

Air pollution, climate change and global warming show direct linkage. Introduction - Background: Research in the field has proposed and demonstrated their link to increased allergenicity in plants, leading to increased allergic diseases. Allergen patterns are changing in response to climate change. Across Europe, up to 12% of the population suffers from allergic diseases (bronchial asthma and allergic rhinitis) caused by the pollen grains. The medical costs of these types of allergies are enormous and are increasing with the spread of common ragweed - Ambrosia artemisiifolia* in Europe. In practice, more and more patients are turning to allergists. Methods: Based on these data, the research aims to identify safety measures and specific legislation to control and eradicate common ragweed in the countries most affected by the spread of this plant in Europe, increase in population exposure in countries affected by the presence of common ragweed and the socio-economic impact of allergic rhinitis and bronchial asthma at European level.[1][2] Our database search did not reveal any other research that has addressed this topic to date. Results: This research identifies specific legislation to the countries most affected by the spread of this plant pollen grains at European level, changes in allergen patterns and monitoring/safety eradication measures decided by local authorities, and data on the spread of common ragweed over the last two decades, costs per treatment, increased allergic rhinitis and bronchial asthma morbidity in the most affected areas. Conclusions: At European level, there are no universally valid models for eradicating this weed. Various methods can be used to limit the spread of common ragweed and its pollen production (e.g. mowing once or twice a year of overgrown land, ploughing, chemical control, public information, etc.). Local and regional cooperation should be considered essential to promote phytosanitary &amp; safety measures and exchange of information on methods to identify and manage the spread of common ragweed. Unified legislation at European level would help all affected countries; reduce morbidity from respiratory allergic diseases and the costs of treating these diseases.

Studying models of healthy aging and exceptional longevity is crucial to understand a possible longevity signature as most of them show resistance to age-related diseases. In particular, semi- and supercentenarians are a highly selected group, having survived significant adversities, including the Spanish Flu and COVID-19 pandemics, indicating distinctive immune system characteristics. This paper analyse the inflammatory scores (INFLA-score, Systemic Inflammation Response Index (SIRI)) and Aging-Related Immune Phenotype (ARIP) indicators calculated from the dataset of the DESIGN project, including 249 participants aged 19-111 years, trying to understand the immune-inflammatory (IMFLAM) role to achieve longevity. Statistical analyses, including linear regression and one-way ANOVA, were performed to explore the correlations between these parameters and age. Both INFLA-score and SIRI showed a significant increase with age. However, no statistical difference is observed when analysing the values for semi- and supercentenarians with those for other age groups, whose values are similar to that of adults and less than younger centenariansRegarding ARIP values, it is noteworthy that when comparing the CD8 Naive/Effector scores between groups, no significant differences were observed between the semi- and supercentenarian group and the other groups. These results support the idea that the control of IMFLAM response can promote extreme longevity.

Although ovarian cystic teratoma is the most common ovarian tumor, complications are quite rare. However, it is important to be recognized by the radiologist in order to avoid inaccurately diagnosing them as malignant lesions. This case report describes a 61-year-old postmenopausal woman, who presented to the emergency room with abdominal pain following a minor blunt abdominal trauma. In this context, a CT scan was performed, which showed the presence of round, hypodense masses randomly distributed in the peritoneum, with coexisting ascites in moderate amount; ovarian carcinoma with peritoneal carcinomatosis was suspected. The patient was hospitalized and an MRI of the abdomen and pelvis was recommended for a more detailed lesion characterization. Following this examination, the patient was diagnosed with mature cystic ovarian teratoma complicated by rupture. Surgery was performed, and the outcome was favorable. The cases of ruptured cystic teratomas are rare, and to our knowledge, this is the first occurrence described in literature. Special attention must be paid when confronting with such a case in medical practice, since it can easily misdiagnosed as peritoneal carcinomatosis

Background: The incidence of metabolic dysfunction-associated steatohepatitis (MASH)-related hepatocellular carcinoma (HCC) is increasing annually with a rise in the incidence of obesity and metabolic syndrome. Based on preliminary reports regarding the potential association of HCC and periodontitis, this study aimed to analyze the involvement of periodontal bacteria as well as the oral and intestinal bacterial flora in MASH-related HCC (MASH-HCC). Methods: Forty-one patients with MASH and 19 with MASH-HCC who completed survey questionnaires, underwent periodontal examinations, and subjected to sample collection (saliva, mouth-rinsed water, feces, and peripheral blood) participated in the study. Bayesian network analysis was used to analyze the causation between various factors, including MASH-HCC, examinations, and bacteria. Results: The occupancy rate of the genus Fusobacterium in the intestinal bacterial flora was significantly higher in in the MASH-HCC group than in the MASH group (p = 0.002), whereas that of Butyricicoccus (p = 0.022) and Roseburia (p &lt; 0.05) was significantly lower. Bayesian network analysis revealed the absence of periodontal pathogenic bacteria and enteric bacteria affecting HCC. However, HCC directly affected the periodontal bacterial species Porphyromonas gingivalis, Tannerella forsythia, Fusobacterium nucleatum, and Prevotella intermedia in the saliva, as well as the genera Lactobacillus, Roseburia, Fusobacterium, Prevotella, Clostridium, Ruminococcus, Trabulsiella, and SMB53 in the intestine. Furthermore, P. gingivalis in the oral cavity directly affected the genera Lactobacillus and Streptococcus in the intestine. Conclusion: MASH-HCC directly affects periodontal pathogenic and intestinal bacteria, and P. gingivalis may affect the intestinal bacteria associated with gastrointestinal cancer.

More than 20 years after the discovery of the first anelloviruses (AVs), they still remain a mysterious group of viruses. To date, there is no clear understanding of their impact on the host organism, although information is emerging about their participation in various pathologies. However, it is well known that AVs are an important component of the human virome. In the current study we assessed the abundance of AVs in viromes from four nasal and pharyngeal samples from children with respiratory symptoms using a metagenomic approach. It is worth noting that three patients had known respiratory viruses and one did not have any. Regarding AVs, they were detected in all samples, namely Torque teno mini virus (TTMV). The greatest abundance and diversity of AVs (Torque teno virus (TTV), TTMV and Torque teno midi virus (TTMDV)) were shown in a diseased patient with an absence of respiratory viruses, which may indicate the potential involvement of AVs in the maintenance of respiratory symptoms. An activated immune system to combat the main pathogens, against the background of which the number of AVs decreases, is discussed as a possible reason for the reduced diversity of anellome in other patients. We also compared the results of the metagenomic approach and qPCR for major respiratory viruses, which were in agreement. In addition, metagenomic sequencing made it possible to obtain additional data on viral genomes in order to establish their taxonomic identification and characterize individual viral profiles in patients from the Siberian part of Russia.

Safe water is a global public health concern amid increasing scarcity and pollution. Bottled water production and consumption contribute to these problems. This study examines tap water consumption in Italy, assessing associated sociodemographic factors and related health outcomes such as obesity and self-perceived health status. Data from the Italian National Statistics Institute's "Aspects of daily life" survey (N=45,597) were analyzed. Covariates included education, age, gender, economic status, region, concerns about waste and climate change, consumption of carbonated drinks excluding water, alcohol consumption, consumption of vegetables, consumption of snacks, body mass index, and self-perceived health status. Bivariate analyses and mixed-effect logistic regression models explored the associations. People who drink tap water were 19,674 with a higher prevalence in people aged 45 to 59 old, people with a graduate/post-graduate degree diploma, with optimal economic resources, and concerned about waste production and climate change, coming from the North-East regions of Italy. Underweight people showed a higher prevalence of TW assumption as well as those who drink less than occasionally carbonated drinks, drink alcohol, consume vegetables more than once a day, and snacks less than once a week with a satisfying level perceived health status. Regressions showed that all age classes are less likely to drink tap water than people younger than 20 years old. The category with “inadequate” economic resources is more likely to consume tap water. Low educational classes show a low likelihood of consuming tap water as well as Islands. A concern about waste production and climate change is associated with an increased likelihood of assuming tap water. Tap water consumption was negatively associate with obesity but not with a satisfactory Self-perceived health status. Insights from this study can inform public health strategies.

Diabetes mellitus (DM) has been defined as a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. This study includes (84) subjects, their age ranged from (40 to 54) years. (20) subjects were healthy chosen as control group and (64) patients with type 2 diabetes mellitus were divided into three groups according to their type of anti diabetic therapy: (23) newly diagnosed group without therapy (Group1), (20) with metformin therapy (Group2) and (21) with metformin plus glibenclamide therapies (Group3). In the study lipid profile level were quantitatively determine by enzymatic methods, in addition to that fasting plasma glucose (FPG), Glycated hemoglobin (HbA1c%) and body mass index (BMI) were identified in the patients. There is significant increase in the level of lipid profile in patients group. Metformin alone produce a non-significant favorable effect on all lipids profile parameters while metformin plus glibenclamide showed a significant reduction in TC and LDL-C.

Aiming at the problems of high cost of manual pulling, low reliability of existing pulling devices, high breaking rate and high leakage rate in the process of cotton stalk reuse after leaving the field in Xinjiang cotton area, a loosening soil and root-cutting cotton stalk pulling the setting machine has been researched and designed, established the root-cutting force model, calculated and optimized the key parameters of the V-shaped root-cutting knife, and determined the range of slide cutting angle, cutting edge angle and soil entry angle. The shoveling process simulation of V-shaped root-cutting knife and root-soil complex was constructed, and the working mechanism of V-shaped root-cutting knife was clarified. In order to verify the reliability and operation performance of the V-shaped root-cutting knife, the slide cutting angle, cutting edge angle and soil entry angle were used as the test factors, the response surface test of three-factors and three-levels was carried out with root-breaking force and mean value of the cutting resistance as the test index. The test results were analyzed by variance analysis, the significant of influencing factors on the root-breaking force are in a descending order as: slide cutting angle, cutting edge angle, soil entry angle; the degree of influence on the mean value of the cutting resistance are as follows: slide cutting angle, soil entry angle, cutting edge angle. In order to make the V-shaped root-cutting knife achieve the optimal working state, the parameters of the test index were optimized, and the optimal design parameters of the V-shaped root-cutting knife were set as follows: the slide cutting angle was 48.3°, cutting edge angle was 43.4°, and soil entry angle was 26.2°. The field uprooting test showed that the average pass rate of root breakage was 94.8% and the average pull-out rate of the cotton stalk was 93.2%. This study provides theoretical guidance for the development of root breaking mechanism of cotton straw harvester.

The Indian meal moth, Plodia interpunctella, poses a significant threat to global agricultural products. Although pheromone-based technologies show promise for P. interpunctella management, limitations like single-sex targeting necessitate exploring complementary strategies. Vegetable-based oils represent a potential alternative, but their efficacy, sex-specificity, and interaction with sex pheromones remain unclear. To answer these questions, we first examined attraction of P. interpunctella female and male adults to ten different vegetable oils, i.e., camellia, corn, olive, peanut, rapeseed, sesame, soybean, sunflower, walnut, and a blended oil (mixture of sunflower seed, rapeseed, soybean, peanut, and sesame oils), in the semi-field conditions. Sesame, olive, and blended oils demonstrated most attractive, capturing significantly more adults compared to other oils. We then evaluated effectiveness of these three attractive oils and their combinations with P. interpunctella sex pheromone in a grain warehouse. Traps baited with these oils captured significantly more females and males compared to control traps without attractants. However, sex pheromone addition did not improve male capture and significantly reduced female capture, suggesting an inhibitory effect. Our findings highlight the potential of blended, olive, or sesame oil trap alone as alternative monitoring and trapping tools for P. interpunctella, delivering novel insights into the development of efficient integrated pest management strategies for stored products.

Long COVID-19 affects a significant percentage of survivors and is characterized by a wide range of symptoms, including weariness and mental fog as well as emotional symptoms like worry and sadness. COVID-19 is closely linked to the autoimmune disorders that are becoming more prevalent worldwide and are linked to the immune system hyperactivation, neutrophil extracellular trap (NET) development, and molecular mimicry pathways. Long-term COVID-related autoimmune responses include a watchful immune system, altered innate and adaptive immune cells, autoantigens secreted by living or dead neutrophils, and high concentrations of autoantibodies directed against different proteins. The microbiome, which consists of billions of bacteria living in the human body, is essential for controlling immune responses and supporting overall health. The microbiome can affect the course of long-term COVID-associated autoimmunity, including the degree of illness, the rate of recovery, and the onset of autoimmune reactions. Although the precise role of the microbiome in long COVID autoimmunity is still being investigated, new studies indicate that probiotics, prebiotics, and dietary changes—interventions that target the microbiome—may be able to reduce autoimmune reactions and enhance long-term outcomes for COVID-19 survivors. It is clear that more research is required to precisely understand how the microbiome affects COVID-19-related autoimmunity and to create tailored treatment plans.

Summary Genome assembly projects have grown exponentially due to breakthroughs in sequencing technologies and assembly algorithms. Evaluating the quality of genome assemblies is critical to ensure the reliability of downstream analysis and interpretation. To fulfil this task, we have developed the AssemblyQC pipeline that performs file-format validation, contaminant checking, contiguity measurement, gene- and repeat-space completeness quantification, telomere inspection, taxonomic assignment, synteny alignment, scaffold examination through Hi-C contact-map visualisation, and assessments of completeness, consensus quality and phasing through K-mer analysis. It produces a comprehensive HTML report with method descriptions, tables, and visualisations. Availability and Implementation The pipeline uses NextFlow for workflow orchestration and adheres to the best-practice established by the nf-core community. This pipeline offers a reproducible, scalable, and portable method to assess the quality of genome assemblies – the code is available online.GitHub: https://github.com/Plant-Food-Research-Open/assemblyqc Supplementary information Pipeline usage documentation, parameter descriptions and example outputs are available on GitHub: https://github.com/Plant-Food-Research-Open/assemblyqc/tree/main/docs. A preview report is also hosted online: https://plant-food-research-open.github.io/assemblyqc

With more companies entering the realm of electric vertical take-off and landing (eVTOL), and governments enacting policies to support the development of low-altitude economies, the commercial potential of eVTOL is being recognized by the public. However, true commercialization is still a long way off. This article analyzes the technologies, product features, potential markets, and government policies related to eVTOL, and constructs a four-stage, four-layer Policy-Technology Roadmap (P-TRM) model to guide the R&D process of eVTOL. Then transform it into a system structural model, and the Decision-making Trial and Evaluation Laboratory (DEMATEL) method is used to identify several key nodes in the R&D process. Utilizing the Technology Adoption Life Cycle (TALC) theory for interpretation and analysis, the article concludes by proposing strategies in product, technology, and policy support for how eVTOL can successfully cross the chasm. This preliminary analysis of the development path, key nodes, and necessary measures for crossing the chasm provides insights for the R&D and commercialization of eVTOL.

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

Photovoltaic (PV) systems, which are renewable energy sources, are increasingly being utilized in distributed generation. To maintain a stable energy balance, energy storage systems such as batteries (BAT) play a vital role. Electric vehicles (EVs) offer a promising solution with rechargeable battery systems for operation. The BATs must maintain their state of charge within design boundaries, despite intermittent PV and load power fluctuations, and advanced power control and management techniques are essential for their effective operation. The article evaluates an intelligent controller that uses a Sliding Mode Control adaptive deep learning algorithm Convolutional Neural Networks (SMC-CNN) and a unique Strategy for managing supervisory power. (SPMS) for photovoltaic systems with battery energy storage for better regulation of DC bus voltage. SMC-CNN offers exceptional resilience and seamless functionality, minimizing fluctuations in DC bus voltage in the control strategy design requirements. The primary goals are maintaining a consistent power supply and ensuring continuous service by preventing system components from exceeding capacity. This study aims to enhance the control of DC bus voltage in the PV and battery system, with the primary significance focusing on the following aspects. The advanced SPMS has been created, incorporating control system constraints to enhance SOC balancing speed and reduce fluctuations in DC bus voltage. Power flow management involves optimizing energy flow between the PV system, battery system, and load while minimizing battery capacity requirements. The proposed SMC-CNN and SPMS are demonstrated through real-time simulation using Matlab/Simulink, as demonstrated in comprehensive case studies.

To enhance the safety of power grid operations, this study proposes a high-precision short-term photovoltaic power prediction method that integrates information from surrounding pho-tovoltaic stations and the Conv-LSTM-ATT model. In the deep learning prediction model, not only is numerical weather prediction (NWP) data from the target photovoltaic station used as input features, but also highly correlated features from nearby photovoltaic stations are incor-porated. The research begins by analyzing the correlation between irradiance and power se-quences, along with distance factors, to calculate a composite similarity index between the target and other regional photovoltaic stations. Stations with high similarity indices are then selected as data sources. Subsequently, Bayesian optimization techniques are employed to find the optimal data fusion ratios. Ultimately, using the selected data, power prediction mod-eling is conducted via the Conv-LSTM-ATT deep neural network. Experimental results con-firm the superiority of the proposed model, which demonstrates higher predictive accuracy compared to three other classical models. The data fusion strategy determined by Bayesian optimization significantly enhances prediction accuracy, reducing the root mean square error (RMSE) of the test set by 20.04%, 28.24%, and 30.94% for three weather types, respectively.

Plantar fasciitis is one of the most common causes of foot pain; in a significant percentage it is also associated with bone edema of the heel. A prospective randomized clinical trial was designed, in which 48 patients suffering from plantar fasciitis, with or without bone edema, were treated with temperature controlled high energy adjustable multi-mode emission laser and exercises (laser group) or with exercises only (control group). Patients were evaluated at recruitment (T0), at 2 (T1) and 6 months (T2), monitoring pain (with Visual Analogue Scale), functionality (with Foot Function Index) and perception of improvement (with Roles and Maudsley Score) and fascia thickness (with ultrasound examination). In both groups there was a significant improvement in pain, functional recovery, perception of remission and a reduction in plantar fascia thickness at T1 and T2. The laser group presented statistically better values at T2 for the Roles and Maudsley Score (z: 2.21; 0.027). Regression analysis showed that a greater reduction in fascia thickness occurred in the laser group (p-value: 0.047). In conclusion, the two conservative treatments were effective in patients suffering from plantar fasciitis, even in the presence of bone edema, but with lesser results.

Many studies utilize sensors to measure foot plantar pressure and analyze gait patterns, often focusing on assessing weight-bearing symmetry or asymmetry compared to control groups. However, a critical question arises: does the selection of the control group significantly impact study outcomes? Does chronic low back pain affect foot plantar pressure measurements? This experimental study involved 23 participants, including healthy individuals and those without foot disorders but with chronic low back pain. Dynamic foot pressure measurements were conducted using the Ortopiezometr, and data were analyzed using the "Steps" program. Participants were divided into two groups: Group 1 comprised completely healthy individuals, while Group 2 consisted of individuals without foot problems but with moderate chronic low back pain. The findings highlight a prevalence of energy release in the forefoot sensors compared to the hindfoot for both the right and left legs in most cases. Even among healthy volunteers, achieving perfect symmetry in the maximum values on the sensors for the right and left feet was a challenge. Results showed no statistically significant differences in energy released on specific sensors or duration of gait phases between the groups. Essentially, this suggests that foot pressure patterns are comparable between healthy individuals and those with low back pain. The use of sensor-equipped inserts for pedobarographic examinations indicates that, in cases of degenerative changes in the lumbar and sacral spine without accompanying acute pain, gait patterns resemble those of the healthy population.

Obesity has increasingly become a worldwide epidemic, as demonstrated by epidemiological and clinical studies. Obesity may lead to development of a broad spectrum of cardiovascular diseases (CVDs), such as coronary heart disease (CHD), hypertension, heart failure (HF), cerebrovascular disease, atrial fibrillation (AF), ventricular arrhythmias, and sudden cardiac death (SCD). In addition to hypertension, there are other cardiometabolic risk factors (CRFs) such as visceral adiposity, dyslipidemia, insulin resistance, diabetes, elevated levels of fibrinogen, and C-reactive protein and others, all of which increase the risk of CVD events. Mechanisms involved between obesity and CVD mainly include insulin resistance, oxidative stress, inflammation, and adipokines dysregulation, which cause maladaptive structural and functional alterations of the heart, particularly left ventricular (LV) remodeling and diastolic dysfunction. Natural products of plants, provide a diversity of nutrients and different bioactive compounds, including phenolics, flavonoids, terpenoids, carotenoids, anthocyanins, vitamins, minerals, fibers, and others, which possess a wide range of biological activities including antihypertensive, antilipidemic, antidiabetic, and other activities, thus conferring cardiometabolic benefits. In this review, we discussed the main therapeutic interventions using extracts from herbs and plants in preclinical and clinical trials with protective properties targeting CRFs. Molecular mechanisms and therapeutic targets of herbs and plants extracts for the prevention and treatment of CRFs are also reviewed.

Food waste is a common issue arising from grinding of food by experimental animals, leading to excessive food scraps falling into cages. In the wild, animals grind food by gnawing vegetation and seeds, potentially damaging the ecological environment. However, limited ecology studies have focused on food grinding behavior since the last century, with even fewer on rodent food grinding, particularly recently. Although food grinding function is partially understood, biological purposes remain under-investigated and driving factors unclear. This review aims to explain potential causes of animal food grinding, identify influencing factors, and discuss contexts and limitations. Specifically, we emphasize recent progress on gut microbiota significance for food grinding. Moreover, we show abnormal food grinding is determined by degree of excess normal behavior, emphasizing food grinding is not meaningless. Findings from this review promote comprehensive research on the myriad factors, multifaceted roles, and intricate evolution underlying food grinding behavior, benefiting laboratory animal husbandry and ecological environment protection, and identifying potential physiological benefits yet undiscovered.

Pulmonary complications are frequently observed in patients with ulcerative colitis (UC), with large airway disease being the most prevalent manifestation. In this case study, we explore the potential for Immunosuppressant, a monoclonal antibody, to induce pulmonary hemorrhage—a side effect not previously documented with its use. The subject, a young male in his twenties with a six-year history of UC, tragically died suddenly. Found in a pool of blood without external injuries, extensive post-mortem examinations were required to determine the cause of death. The patient had been on biologic therapy, receiving Immunosuppressant intermittently over the past two years, including an infusion five days prior to death. External examination revealed bloody, pinkish foamy material expelling from his nose and mouth. An autopsy supported an atypical presentation of UC, while histopathology confirmed diffuse pulmonary hemorrhage, marked by edema and emphysematous changes. Additionally, there was a notable lymphomonocytic infiltration around the arterial walls, and signs of bronchiolitis and focal pleural fibrosis were evident. We concluded that the cause of death was Diffuse Alveolar Hemorrhage (DAH), directly associated with the recent administration of Immunosuppressant. This finding underscores the need for vigilant pulmonary monitoring in patients treated with monoclonal antibodies to promptly identify and manage potential fatal adverse reactions.

For the last two decades, an increasing demand for low-cost adsorbents for drinking water treatment has been challenged in the rural areas of developing countries that have problems with arsenic (As) contamination. In this work, spent bleaching earth (SBE), a residual material that is formed during the process of refining vegetable oil, has been utilized to prepare the new magnetic adsorbent Fe-SBE/C by modifying its surface area through calcination and co-precipitation with iron. The experimental results demonstrate that Fe-SBE/C exhibits a substantial adsorption capacity towards both arsenite (As(III)) and arsenate (As(V)). Optimal pH conditions were found to be pH 10 for As(III) and pH 3 for As(V), resulting in adsorption percentages of 76.7% and 94.1%, respectively. Other experimental parameters such as calcination conditions, adsorbent dosage, iron-loading ratio, and the presence of co-existing ions were thoroughly investigated. The kinetic study revealed that the adsorption processes follow the Elovich and intraparticle diffusion models, suggesting a combined adsorption mechanism of boundary layer control and chemisorption. Furthermore, the isotherm followed the Langmuir model with a maximum adsorption capacity of 202.61 μg g-1 for As(III) and 187.61 μg g-1 for As(V). Through thermodynamic analysis and reinforcement by activation energy data, this research validates that the observed adsorption process is indicative through chemisorption. The regeneration study revealed the potential for multiple cycles of arsenic removal, highlighting the practical applicability of Fe-SBE/C in environmental contexts. In conclusion, the findings of this study underscore the efficacy of Fe-SBE/C in arsenic removal and present it as a promising and environmentally friendly solution for mitigating arsenic pollution.

Saudi Arabia is a prominent producers of dates, with 1.6 million tons annually. There is a need to evaluate physical properties and quality of fruits non-destructively and then be modeled and predicted throughout the storage period. The aim of the current study was to generate a quality index (Qi) and Near-infrared spectra (NIR) models non-destructively to predict properties of Barhi dates fruits including objective and sensory evaluations. The engineering properties of Barhi dates were measured and modeled with quality index (Qi) based on NIR of fresh Barhi fruits (hardness, color, TSS, pH, and sensory evaluations) and during storage in cold, ambient, and controlled at-mosphere (CA) for up to four months. The prediction of Qi is non- destructively based on NIR utilizing PLSR and ANN data analysis. The results showed that the Qi generated corresponds with high precision to the characteristics of the examined fruits through the duration of the storage period with R2 of 0.96. The NIR spectrum proves to be an efficient method to evaluate the Barhi fruits' quality index. where ANN was found to be more suitable than PLSR analysis. Thus, NIR can be utilized to accurately predict the Qi of fruits quality effectively throughout the handling, processing, transporting, storage, and retail sector supply chain.

The study covered the leaves of 25 varieties of highbush blueberry Vaccinium corymbosum L. Determinations were performed for phenolic acid (chlorogenic, neochlorogenic, cryptochlorogenic, 3,5-dicaffeoylquinic and 4,5-dicaffeoylquinic acids), flavonoid (catechin, rutin, isoquercetin), arbutin and hydroquinone content. The content of phenolic acids and flavonoids in the test material suggests that highbush blueberry leaves can be a potentially good material for dietary supplement production, although the relatively high arbutin content (exceeding 4% in the case of the Bonus, Chantiklee and Herbert varieties) limits this possibility to herbal medicinal products applied in case of health issues for a limited time, similar to other over-the-counter arbutin products (bearberry Arctostaphylos uva-ursi and lingonberry Vaccinium vitis-idaea leaves).

Background/Objectives: The global burden of metabolic dysfunction-associated steatotic liver disease (MASLD) is increasing at an alarming rate. Cardiovascular disease (CVD) is the leading cause of mortality in MASLD patients, therefore, the therapeutic interventions for MASLD should improve coronary risk factors such as obesity, diabetes and dyslipidemia, in addition to improving liver function. The evidence on effect of sodium-glucose cotransporter 2 inhibitors (SGLT2is) which have beneficial pharmacological properties for MASLD, especially the long-term effects, on the progression of MASLD in Asian population is very limited. Methods: The retrospective longitudinal study was performed by using the medical records at the National Center for Global Health and Medicine, Kohnodai Hospital, Japan. We picked up patients with type 2 diabetes who had taken SGLT2is for at least 3 years or longer between April 1, 2014 and March 31, 2018. We collected the data on metabolic parameters, including blood tests and anthropometric parameters, and compared the data before and after the initiation of SGLT2is treatment. Results: During the observation period, 324 patients had taken SGLT2is for 3 years. The 3-year-SGLT2is treatment significantly reduced body weight, hemoglobin A1c, low-density lipoprotein-cholesterol, triglyceride and non-high-density lipoprotein-cholesterol (HDL-C) and significantly increased HDL-C. Furthermore, this treatment significantly improved liver function and the markers for hepatic steatosis and hepatic fibrosis. Conclusions: Considering that the development of CVD determines the prognosis of MASLD patients, the long-term SGLT2is treatment may be an ideal therapy for MASLD patients.

Currently, there is a high interest in smart sensors and integrated composite materials in various industries such as construction, aviation, automobile, medical, information technology, communication, and manufacturing. Here, a new conceptual design for a force and temperature sensor system is developed by integrating fiber optic Bragg grating sensors embedded within composite materials, and a mathematical model is proposed that allows one to estimate strain and temperature based on signals obtained from optical Bragg gratings. This is important for understanding the behaviors of sensors under different conditions and for creating effective monitoring systems. Describing the strain gradient distribution, especially considering different materials with different values of Young's modulus, provides insight into how different materials respond to applied forces and temperature changes. The shape of the strain gradient distribution was obtained, which is a quadratic function with a maximum value of 1500 µ, with a maximum value at the center of the lattice and a symmetrically decreasing strain value with distance from the central part of the fiber Bragg grating. With axial strain at the installation site of the Bragg grating sensor under applied force values ranging from 10 to 11 N, the change in strain was linear. As a result of theoretical research, it was found that the developed system with fiber-optic sensors based on Bragg gratings embedded in composite materials is resistant to external influences and temperature changes.

Objective: To determine the effect of sandblasting before and after sintering on the surface roughness of zirconia and the micro-tensile bond strength of a pressable veneering ceramic to zirconia. Methods: Pre-sintered zirconia blocks (IPS e.max ZirCAD, Ivoclar Vivadent) were divided into a control group (CTR, no surface treatment) and four test groups of three specimens each: Pre-S-30, Pre-S-50, Pre-S-110 were sandblasted with 30µm SiO2, 50µm Al2O3 and 110µm Al2O3 particles respectively, before sintering, Post-S-30 was sandblasted with 30µm SiO2 after sintering. For each treatment, the surface roughness was measured (Ra, Perthometer M4P, Mahr Perthen). After sintering the zirconia blocks, a liner and a pressable ceramic (IPS e.max ZirPress, Ivoclar Vivadent) were fired. Sixteen micro-bars were obtained from each block and submitted to the micro-tensile bond strength (µTBS) test. Data were analyzed with one-way ANOVA. Any correlation between Ra and µTBS was evaluated (Sperman test). Results: Sandblasting before sintering with 110µm Al2O3 (Ra=3.44±0.44µm), 50µm Al2O3 (Ra=2.32±0.46µm), and 30µm SiO2 (Ra=1.22±0.22µm) resulted in significantly higher roughness than sandblasting after sintering with 30µm SiO2 (Ra=0.46±0.11µm). The highest µTBS was measured when the sintered zirconia was sandblasted with 30μm SiO2 (26.79±14.80 MPa), which was significantly different from that of specimens that were sandblasted before sintering (Pre-S-30=20.90±11.70; Pre-S-50=21.27±15.19; Pre-S-110=23.99±16.83) or were not treated (CTR=17.44±14.03). Conclusions: Sandblasting zirconia before sintering enhances the surface roughness proportionally to the particle size of the sand used. Sandblasting with 30µm SiO2 after sintering appeared to improve bonding between the veneering ceramic and zirconia. Clinical Significance: Sandblasting with 30µm SiO2 after sintering may improve bonding between veneering ceramics and zirconia, thus reducing ceramic fractures/chippings.

Design process can be divided into stages of analysis and synthesis. This paper presents the application of the object-oriented technique to the synthesis as well as theoretical considerations concerning to design of gear size ranges. The synthesis consists in composition (aggregation) of single details into units – according to the rule: “from details up to general view”. The basics of the object-oriented synthesis were formulated. Furthermore, the consecutive stages of synthesis were formulated. The interactions between these stages were discussed. Some aspects were described in details e.g.: creation of interfaces for performance of the embodiment phase the designed artefact via CAD system drawing with specification (dimensions are still parametrically set). The final step of the procedure is creation of so called substantial (concrete) form of artifacts i.e. final assignment of dimensions which stored in the special data base. The final stage of the design process is issuing of technical documentation (technical drawings) created automatically via CAD system. The considerations are terminated by the list of advantages connected with the object-oriented approach to design (in comparison to the traditional way).

Dirofilariosis is an infectious disease caused by species of the Dirofilaria genus. It is manifested by the appearance of a subcutaneous swelling, especially in the eye region. We present the case of a 29-year-old patient that accused facial asymmetry in the right genian region. Following clinical and paraclinical evaluations, the diagnosis of parasitic cyst was established in the context of dirofilariosis with Dirofilaria repens (D. repens). Treatment consisted of surgical excision of the formation associated with prophylactic antibiotic medication. Macroscopic analysis of the excision piece revealed a structure that contains a cystic cavity and a filamentous form with a length of approximately 10 mM and a diameter of 1 mM. This is the first case of dirofilariosis located in the genian region reported in Romania. The overview of this pathology is important to raise awareness among physicians about its presence and its clinical variations.

In this paper, a method of charging lead acid battery with solar power by flyback converter is proposed. The basic system consists of a buffer circuit, auxiliary power circuit, control circuit, voltage feedback circuit and current feedback circuit. The system is capable of monitoring battery voltage and the current, charging the coupling inductor and automatic control of output duty cycle, overcharging and over-discharging protection. The experiment proves that the charging efficiency is improved by about 83% at full load.

Bioactive peptides are sequences of amino acids, cut from the parent protein with en-zymes or high pressure processing that can impart a tertiary health benefit to the con-sumer that goes beyond basic human nutrition. This study explores mesopelagic fish in-cluding Maurolicus muelleri and Benthosema glaciale as sources of peptides with an-ti-inflammatory, heart health beneficial, antioxidant and other bioactivities. Bioactive peptides were generated post-catch using different enzymatic hydrolysis processes ap-plied to mesopelagic species caught from experimental cruises targeting these fish. Maurolicus muelleri and Benthosema glaciale were the dominant species processed. Bi-oactive hydrolysates included a Maurolicus muelleri Alcalase hydrolysate and a Maurolicus muelleri autolysis. Hundreds of peptide sequences were identified using mass spectrome-try from active hydrolysates. In silico analysis was used to rank identified peptide se-quences in terms of overall bioactivity using Peptide Ranker, anti-inflammatory activity using PrepAIP, ability to impart Umami flavours with Umami-MRNN and dipeptidyl peptidase IV inhibitory potential using AntiDMPpred . Thirty peptides were selected fol-lowing in silico analysis. Several of the selected peptides had sequence homology with peptides derived from other fish proteins including Atlantic salmon (peptide QCPLHR-PWAL) and thin-lipped mullet (peptide FDAFLPM). Seven peptides predicted to have an-ti-inflammatory, anti-type 2 diabetes or Umami potential using in silico strategies were chemically synthesized and their anti-inflammatory activities confirmed using in vitro bioassays with COX-1 and COX-2 enzymes. The peptide QCPLHRPWAL inhibited COX-1 and COX-2 by 82.90% (+/-0.54) and 53.84% respectively and had a selectivity index greater than 10. This peptide warrant further research as a novel anti-inflammatory/pain relief peptide. Other bioactivities associated with DPP-IV inhibition and Umami flavours de-velopment are discussed. Identified peptides offer potential for use in functional foods or topical agents to prevent pain and inflammation associated with diseases like psoriasis, arthritis and inflammatory bowel disease, however further research in vivo is required.

This critical review illuminates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the dairy livestock export industry, an area facing mounting pressure for heightened efficiency and sustainability. We rigorously scrutinize the uptake of these novel technologies in identifying 'shy feeders,' automating weight monitoring of individual livestock, and refining cattle enumeration procedures. The investigation unravels their capacity to bolster animal welfare standards, minimize supply chain discrepancies, and amplify operational productivity. Moreover, the research delves into how these innovations may enhance market access and competitiveness in a swiftly shifting global dairy landscape. We further highlight the challenges encountered and future trajectories, providing a strategic framework for technology integration within the livestock export sector. Ultimately, this review underlines the importance of adopting AI and sensor technologies, indicating a shift towards precision digital livestock farming that amalgamates efficiency, animal welfare, and profitability.

For the in-house validation of a droplet digital PCR method, a factorial experimental design was implemented. This design serves different purposes. On the one hand, it is an efficient design in relation to the workload involved in achieving a desirable level of reliability of variance estimates. On the other hand, it allows a partitioning of total variance into different components, thus providing information regarding the dominant sources of random variation. The statistical modelling reflects the actual measurement mechanism, establishing relationships between nominal target DNA copies per well, the range of variation of copy numbers per droplet, probability of detection values, and estimated numbers of copies.

The innovation driven Industry 5.0, in agreement with Industry 4.0, leads to consider human in a prominence position as the center of manufacturing field. This pushes towards the hybridization of manufacturing plants promoting a fully collaboration between human and robot. Furthermore, the new paradigm of "human centred design" and "anthropocentric design" allows enabling a synergistic combination of human and robot skills. However, properly collaborative workplaces are currently very few. Industry is still not confident, and systems integrators hesitate to venture into Human-Robot Collaboration (HRC). Despite the effort in collaborative robotics, a general solution to overcome the current limitations in designing of collaborative workplaces still misses. In the current work, a Knowledge-Based Approach (KBA) is adopted to face collaborative workplace designing problem. The framework resulting from the KBA allows developing a modelling paradigm that enable to define a streamlined approach for the layout designing of a collaborative workplace. Finally, a what-if analysis and a ANOVA analysis are performed to generate and evaluate a set of scenarios related to a collaborative workplace for quality inspection of welded parts. Facing the high complexity and multidisciplinary of HRC can be conveyed to develop a general design approach aimed at overcoming the difficulties that limit the spread of HRC in the manufacturing field.

Autonomous unmanned aerial vehicles (UAVs) require highly reliable navigation information. Generally, navigation systems with the inertial navigation system (INS) and global navigation satellite system (GNSS) have been widely used. However, the GNSS is vulnerable to jamming and spoofing. The terrain referenced navigation (TRN) technique can be used to solve this problem. In this study, to obtain reliable navigation information even if a GNSS is not available or the degree of terrain roughness is not determined, we propose a federated filter based INS/GNSS/TRN integrated navigation system. we also introduce a TRN system that combines batch processing and an auxiliary particle filter to ensure stable flight of UAVs even in a long-term GNSS-denied environment. As an altimeter sensor for the TRN system, we use an interferometric radar altimeter (IRA) to obtain reliable navigation accuracy in high altitude flight. In addition, a parallel computing technique with general-purpose computing on graphics processing units (GPGPU) is applied to process a high resolution terrain database and a nonlinear filter in real time on board. Finally, we verify the performance of the proposed system through software-in-the-loop (SIL) tests and captive flight tests in a GNSS unavailable environment.

The sweetpotato whitefly, Bemisia tabaci MEAM1, is a pest known to significantly impact tomato development and yields through direct damage and virus transmission. To manage this pest, the current study compared the effectiveness of various insecticide rotations. Field trials included rotations involving synthetic insecticides, biochemicals, and microbial agents, applied according to their labeled highest concentrations. Results indicated that while standard synthetic insecticides consistently reduced whitefly egg and nymph counts significantly, microbial biopesticide rotations also achieved reductions, although less consistently. This study demonstrated that while traditional chemical treatments remain highly effective, integrating microbial biopesticides containing Beauveria bassiana and Cordyceps javanica present a viable alternative that could mitigate resistance development and reduce environmental impact. The findings suggest the potential for optimizing integrated pest management (IPM) and Insecticide Resistance Management (IRM) strategies by incorporating a variety of modes of action to sustainably manage B. tabaci MEAM1 populations in agricultural settings.

Mangrove forests provide a wide range of goods and services that sustain communities around the world. This paper explores utilization patterns of extractable mangrove resources by communities within the proposed Kenya – Tanzania Transboundary Conservation Area (TBCA). Some 152 household surveys and 12 Nominal Group Technique discussions were carried out. At least 16 direct use products were reported to be extracted from the mangroves, with 90% of households found to use mangrove products. Changing patterns of mangrove use in the areas and accessibility point towards unsustainable utilization and promote illegal activities. Overharvesting of mangrove wood products was reported to be the major threat facing the forests. Patterns of mangrove use in the study site reveal a high level of mangrove-dependence by communities for their livelihood. There is a need to address the problems of open access through capacity building and awareness creation, development and implementation of harvest plans as well as introduction of alternative livelihood options to the communities in the area.

Railway has been used as one of the most crucial means of transportation in public mobility and economic development. For efficiently operating railways, the electrification system in railway infrastructure, which supplies electric power to trains, is essential facilities for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning (MLS) data into ten target classes representing overhead wires, movable brackets and poles, which are recognized key objects in the electrification system. In general, the layout of railway electrification system shows a strong regularity of spatial relations among object classes. The proposed classifier is developed based on Conditional Random Field (CRF), which characterizes not only labeling homogeneity at short range, but also the layout compatibility between different object classes at long range in the probabilistic graphical model. This multi-range CRF model consists of a unary term and three pairwise contextual terms. In order to gain computational efficiency, MLS point clouds is converted into a set of line segments where the labeling process is applied. Support Vector Machine (SVM) is used as a local classifier considering only node features for producing the unary potentials of CRF model. As the short-range pairwise contextual term, Potts model is applied to enforce a local smoothness in short-range graph. While, long-range pairwise potentials are designed to enhance spatial regularities of both horizontal and vertical layouts among railway objects. We formulate two long-range pairwise potentials as the log posterior probability obtained by Naïve Bayes classifier. The directional layout compatibilities are characterized in probability look-up tables which represent co-occurrence rate of spatial relations in horizontal and vertical directions. The likelihood function is formulated by multivariate Gaussian distributions. In the proposed multi-range CRF model, the weight parameters to balance four sub-terms are estimated by applying the Stochastic Gradient Descent (SGD). The results show that the proposed multi-range CRF can effectively classify detailed railway elements, representing the average recall of 97.66% and the average precision of 97.07% for all classes.

Starting from the fact that one of the global control strategies of the Permanent Magnet Synchronous Motor (PMSM), namely the Direct Torque Control (DTC) type control strategy, is characterised by the fact that the inner flux and torque control loop usually uses hysteresis ON-OFF type controllers and the oscillations introduced by them have to be cancelled out by the outer speed loop controller (which is usually a PI speed controller and whose performance is good around the global operating points and for relatively small variations of the external parameters and disturbances caused in particular by the load torque variation), while retaining the advantages of the DTC strategy, this paper presents the improvement of the performance of the PMSM sensorless control system using Proportional Integrator (PI), PI Equilibrium Optimizer Algorithm (EOA), Fractional Order (FO) PI, Tilt Integral Derivative (TID) and FO Lead-Lag for constant flux, and Sliding Mode Control (SMC) and FOSMC for variable flux. The performance indicators of the control system are the usual ones: response time, settling time, overshoot, steady-state error and speed ripple, plus another one given by the fractal dimension (FD) of the PMSM rotor speed signal, and the hypothesis that the FD of the controlled signal is higher when the control system provides better performance is verified. The paper also presents the basic equations of the PMSM, based on which the synthesis of integer and fractional controllers, the synthesis of an observer for estimating the PMSM rotor speed, electromagnetic torque and stator flux are presented. The comparison of the performance of the proposed control systems and the demonstration of the parametric robustness are performed by numerical simulations in Matlab/Simulink using Simscape Electrical and Fractional Order Modelling and Control (FOMCON). Real-time control based on an embedded system using a TMS320F28379D controller demonstrates the good performance of the PMSM sensorless control system based on the DTC strategy in a complete hardware-in-the-loop (HIL) implementation.

The double burden of malnutrition (DBM) is escalating in low- and middle-income countries (LMICs), including in Rwanda, most notably in urbanizing areas. The 2019-2020 Rwanda Demographic Health Survey (DHS) revealed 33% of children under 5 are stunted while 42% of women in urban areas are overweight or obese. This coexistence has contributed to a surge in non-communicable diseases (NCDs), particularly in secondary cities. Using the World Health Organization’s (WHOs) “double-duty action” (DDA) concept, this study aims to identify and evaluate interventions with double-duty potential in Rwanda, focusing on the districts of Rusizi and Rubavu. Guided by Hawkes et al.’s conceptual framework for identifying DDA entry points, a desk review of national policies pinpointed 4 programs with DDA potential: early childhood development (ECD) centers, the school feeding program, farmer field schools (FFS), and the provision of nutrition-sensitive direct support. In-person interviews with key stakeholders assessed their implementation and a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis informed context-specific recommendations for their improvement. Recommendations include targeting beliefs surrounding nutrition, improving trainings for community educators, enhancing parent – particularly father – involvement, and close follow-up. These findings offer actionable strategies for governments and nutrition stakeholders and may benefit similar interventions in other rapidly urbanizing LMICs.