In recent years, with the development of social media, people are more and more inclined to upload text, pictures and videos on the platform to express their personal emotions, thus the number of short videos is increasing and becoming the first choice for people to socialize. Unlike the traditional way, people can convey their personal emotions and opinions through media other than words, such as video images, etc. for external information. Therefore, the expression and analysis of emotions is not only through text, but also through the analysis of emotional needs in images and videos, and the research scholars have customized products for individual users. Compared with pure text content, video information can more intuitively express users' happiness, anger and sorrow, thus short video-related applications have gained more and more popularity among Internet users in recent years. However, not all short videos on social networking sites can accurately express users' emotions, and related text information can more accurately assist sentiment analysis and thus improve accuracy. However, short video sentiment analysis based on video frame images is inaccurate in some scenarios, such as when expressing tears of joy, the sentiment expressed by the user's facial expression and voice are different, which will cause errors in the analysis of sentiment. As a result, researchers began to consider multimodal sentiment analysis to reduce the impact of the above scenarios on short video sentiment analysis. This paper focuses on proposing a sentiment analysis method for short videos. We first propose a residual attention model to make full use of the information in audio to classify the emotions contained in them. Then the text information in the dataset is classified by feature extraction. The key to extract features from text information is not only to retain the semantic information of the text, but also to explore the potential emotional information in the text, so as to ensure the integrity of the text information features. The experiments show that the sentiment analysis model proposed in this paper is more superior than the baselines.

Despite a rapid rise in NBS development in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatio-temporal variation of net ecosystem exchange from eddy-covariance stations. Lagrangian particle dispersion model was used for upscaling of satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution due to use of the Landsat imagery; (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Overall we present here globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.

Agricultural mechanization is an important factor to improve the green total factor productivity of planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of output oriented distance function to measure the green total factor productivity of China’s planting industry based on net carbon sink, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China’s planting industry. The empirical analysis finds that mechanization can significantly promote the planting green total factor productivity, and this basic conclusion is still robust after using instrumental variables, sub sample regression. Further research found that the path of mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover. The mechanism of operation scale expansion, factor allocation optimization and technical efficiency change is not significant. Given these findings, the paper adds considerable value to the empirical literature and also provides various policy- and practical implications.

Nowadays, and with the mechanization of life, speech processing has become so crucial for the interaction between humans and machines. Deep neural networks require a database with enough data for training. The more features are extracted from the speech signal, the more samples are needed to train these networks. Adequate training of these networks can be ensured when there is access to sufficient and varied data in each class. If there is not enough data; it is possible to use data augmentation methods to obtain a database with enough samples. One of the obstacles to developing speech emotion recognition systems is the Data sparsity problem in each class for neural network training. The current study has focused on making a cycle generative adversarial network for data augmentation in a system for speech emotion recognition. For each of the five emotions employed, an adversarial generating network is designed to generate data that is very similar to the main data in that class, as well as differentiate the emotions of the other classes. These networks are taught in an adversarial way to produce feature vectors like each class in the space of the main feature, and then they add to the training sets existing in the database to train the classifier network. Instead of using the common cross-entropy error to train generative adversarial networks and to remove the vanishing gradient problem, Wasserstein Divergence has been used to produce high-quality artificial samples. The suggested network has been tested to be applied for speech emotion recognition using EMODB as training, testing, and evaluating sets, and the quality of artificial data evaluated using two Support Vector Machine (SVM) and Deep Neural Network (DNN) classifiers. Moreover, it has been revealed that extracting and reproducing high-level features from acoustic features, speech emotion recognition with separating five primary emotions has been done with acceptable accuracy.

Modern portfolio theory claims that diversification into non-correlated or negatively correlated activities reduces the overall risk of a portfolio. Considering the total income of a bank as a portfolio of interest income and non-interest income, this paper investigates how the variability of interest income and non- interest income, and covariance between interest income and non-interest income influence the various risk factors of banks. We set out a study in the Indian context. We have extracted data for the period 2005-2017 and employed an extended version of Ridge, Lasso and Elastics Net regression to take care of multi-collinearly in our data. We have considered 10-fold cross-validation techniques to get optimal values of tuning parameters for Ridge, Lasso, and Elastics Net regression (which is a convex combination of ridge and the LASSO). We have compared different regression techniques by comparing RMSE and R2. We observe that non-interest income is positively correlated with interest income in the Indian context, but it does stabilize variance, idiosyncratic risk & market risk (Beta) of Indian Banks.

The city administration of China planned numerous metro projects and more metros can hardly avoid undercrossing a bridge. While metro shield construction when undercrossing a bridge (MSCUB) is frequently located in complicated natural and social context, which made the construction process more susceptible to safety accidents. Therefore, it is crucial to look into safety risk of MSCUB. The paper identified the safety risk factors during SSCUR by using literature review and experts’ evaluation, proposed a novel safety risk assessment model by integrating CFA and FER, and then selected a project case to test the validity of the suggested model. Research results show that (a) a safety risk factors list of MSCUB was identified, including 4 first-level safety risks and 37 second-level safety risk factors; (b) the proposed safety risk assessment model can be used to measure the risk values of the overall worksite safety risk, the first-level safety risks and the safety risk factors of MSCUB; (c) environment-type safety risk and personnel-type safety risk have higher risk values when shield construction undercrossing a bridge; (d) when compared to worker-type safety risk, manager-type safety risk is the higher risk value. The research can enrich the theoretic knowledge of MSCUB safety risk assessment and provide references to safety managers for conducting scientific and effective safety management on the construction site when subway undercrossing a river.

With the advance of generative AI, the text-to-image (T2I) model has the ability to generate various contents. However, the generated contents cannot be fully controlled. There is a potential risk that text-to-image model can generate unsafe images with uncomfortable contents. In our work, we focus on eliminating the NSFW (not safe for work) content generation from text-to-image model while maintaining the high quality of generated images by fine-tuning the pretrained diffusion model via reinforcement learning by optimizing the well-designed content-safe reward function. The proposed method leverages a customized reward function consisting of the CLIP (Contrastive Language-Image Pre-training) and nudity rewards to prune the nudity contents that adhere to the pretrained model and keep the corresponding semantic meaning on the safe side. In this way, the text-to-image model is robust to unsafe adversarial prompts since unsafe visual representations are mitigated from latent space. Extensive experiments conducted on different datasets demonstrate the effectiveness of the proposed method in alleviating unsafe content generation while preserving the high-fidelity of benign images as well as images generated by unsafe prompts. Our method surpasses existing state-of-the-art (SOTA) baseline methods and achieves better performance on sexual content removal (14.5% higher than SafeGen) and image quality retention. In terms of robustness, our method outperforms Safe Latent Diffusion and SafeGen under the SOTA black-box attacking model SneakyPrompt by approximately 2x and 5.6x, respectively. Furthermore, our constructed method can be a benchmark for other methods aiming for anti-NSFW generation, high-level prompt-image safe alignment.

Artificial Intelligence is developing at a rapid place, with examples of Machine Learning already utilized in aviation to improve efficiency. In the coming decade it is likely that Intelligent Assistants (IAs) or ‘Digital Colleagues’ will be developed and deployed to assist aviation personnel in the cockpit, the air traffic control centre and airports. This will be a game-changer and may herald the way forward for single pilot operations, AI-based management of Urban Mobility including fleets of drones and sky-taxis, and more carbon-efficient transport across global commercial transport networks. Yet in aviation there is a core underlying tenet that ‘people make safety’ and keep the skies and passengers safe, based on a robust industry-wide safety culture. The introduction of IAs into aviation could potentially undermine aviation’s hard-won track record in this area. This paper applies a scientifically validated safety culture tool to explore the potential impacts of the introduction of IAs into aviation. The results suggest that there are indeed potential negative outcomes, but also possible safety ‘affordances’ wherein AI could strengthen safety culture. Safeguards and mitigations are suggested for the key ‘risk owners’ in aviation organisations, to ensure safety remains a priority in the industry.

This review provides a detailed analysis of hemodiafiltration (HDF), its progress from an emerging technique to a potential conventional treatment for chronic hemodialysis patients, and its current status. The article covers advances, methodologies, and clinical benefits of HDF, spe-cifically focusing on its impact on cardiovascular health, survival rates, and overall well-being. The text also addresses concerns about the safety of HDF and provides evidence to dispel worries re-lated to the elimination of beneficial substances and infection risks. Additionally, the article ex-plores the potential implications of expanded hemodialysis (HDx) as an alternative to HDF, its classification, safety profile, and an ongoing trial assessing its non-inferiority to HDF. Supported by evidence from randomized controlled trials and observational studies, the review emphasizes the superiority of HDF as a hemodialysis modality, advocating for its positioning as the gold standard in treatment. However, it acknowledges the need for extensive research to define the role of HDx in comprehensive treatment approaches for individuals undergoing dialysis. The synthesis of current knowledge underscores the importance of ongoing exploration and research to refine hemodialysis practices for optimal patient outcomes.

The tobacco cembranoid (1S,2E,4R,6R,7E,11E)-2,7,11-cembratriene-4,6-diol (4R) is known to induce neuroprotection against brain ischemia, systemic inflammation, Parkinson's disease, and organophosphate toxicity in rodents. Studies examining the compound's safety in male and female Sprague Dawley rats demonstrated no significant side effects after a single injection of 4R at various concentrations (6, 24, or 98 mg/kg). To evaluate the neurotherapeutic feasibility of 4R for clinical trials, we have assessed the safety of this compound in nonhuman primates. We investigated whether 4R induces toxicity in male Macaca mulatta when administered for 11 consecutive days via intravenous injection at a dose of 1.4 mg/kg. Electroencephalogram, somatosensory evoked potential, and transcranial motor evoked potentials were measured at days 0, 4, 8, and 12 and remained unaffected during the experimental study. Spontaneous behavior was not affected between the groups. Small histopathological changes in the organs were observed in some animals, both treated and non-treated; thus, these changes cannot be attributed to 4R. Minor hematological and blood composition variations with no clinical significance were detected in the experimental animals. In conclusion, our data support previous findings that 4R is a non-toxic compound that proved safe in nonhuman primates during the given period and dose.

A growing body of literature suggests a causative relationship between severe adverse neurological outcomes and early life exposure to paracetamol (acetaminophen) in the presence of oxidative stress. Review of the literature revealed that, although its use is not regularly monitored, paracetamol has achieved near universal acceptance, with exposure in some pediatric populations exceeding 90%. In addition, use of the drug as well as associated adverse outcomes may have risen as a result of pharmaceutical advertising rather than need, and inappropriate use of the drug, both in terms of dose and indication, is widespread. These findings indicate that many clinicians and patients do not, at the present time, evenly weigh the potential risks with the potential benefits of paracetamol exposure early in life. Although retrospective studies might be envisioned to further address the neurodevelopmental risks of paracetamol use during early development, in silico simulations demonstrated that such studies can be thwarted by very high rates of use of the drug combined with associations between paracetamol use and oxidative stressors that act as cofactors in the induction of neurodevelopmental injury. These findings suggest that, despite persistent uncertainty, clinicians and patients should be more aware of available information pointing toward the potential dangers for neurodevelopment of early life exposure to paracetamol. Most importantly, health care workers need to provide a more balanced view, weighing both risks and benefits, when providing advice for patients regarding paracetamol use during periods of brain development.

We performed a systematic search and meta-analysis of available literature to determine the safety profile of Cerebrolysin in acute ischemic stroke, filling existing safety information gaps and inconsistent results. We searched EMBASE, PubMed and Cochrane Databases of Systematic Reviews and Clinical Trials up to the end of February 2021. Data collection and analysis was conducted using methods described in the Cochrane Handbook for Systematic Reviews of Interventions. All safety outcomes were analyzed based on risk ratios (RR) and their 95% confidence intervals. The meta-analysis pooled 2202 patients from twelve randomized clinical trials, registering non-statistically significant (p>0.05) differences between Cerebrolysin and placebo throughout main and subgroup analyses. The lowest rate of Serious Adverse Events (SAE), as compared to placebo, was observed for the highest dose of Cerebrolysin (50 mL), highlighting a moderate reduction (RR = 0.6). We observed a tendency of superiority of Cerebrolysin regarding SAE in high dose treatment courses for moderate-severe ischemic stroke, suggesting some effect of the agent against adverse events. This comprehensive safety meta-analysis confirms the safety profile for patients treated with Cerebrolysin after acute ischemic stroke, as compared to placebo.

Acute hospital bed shortage is a serious concern worldwide, constantly involving high-dependency units (HDU), where the non-availability of postoperative beds results in surgery cancellation. In the acute medicine context, the SAFER Red2Green initiative has shown to enhance patient flow.Local problem At the Royal London hospital, in 2016, hospital-initiated cancellations peaked at over 50% weekly due to the inability of high dependency Units (HDU) to discharge step-down patients to the general surgical wards, where bed occupancy was close to 100% and the average length of stay was stable on average close to 7 (+/- 8.6) days.Methods. This was a service improvement research to enhance patient flow which adapted the SAFER Red2Green model to a surgical ward (SAFER Surgery Red2Green). This before-after study involving all 2017 digestive surgery admissions was divided into a three-month feasibility phase followed by a nine-month pilot phase, versus the year 2016 (pre-intervention). Outcome measures: weekly discharges, length of stay (LOS), surgery cancellations, feasibility of a “theatre go” policy, HDU step-downs, 30-day readmissions.Interventions1) Systematic communication of key care plan from the afternoon ward rounds by surgical teams to the nurse in charge; 2) 10 AM Monday-to-Friday multi-disciplinary senior-team daily board round, addressing updated key care plan aimed at early discharges, appropriateness of each inpatient day, causes of delays; 3) hospital and site managers weekly attendance. Results. At three months: +67% discharges/week (p=0.001), -20% LOS (p=0.023), +21% HDU step-downs, (p=0.205). At one year: +10.7% HDU step-downs (p=0.197), increased probability of earlier discharge (p=0.023), -60% hospital-initiated cancellations from 38 to 15 (p>=1), a “Theatre go” policy has been active since month 6. Failed discharges kept at 1.3 %. The MDT board round staff satisfaction rate was over 80%, with key actors’ attendance over 75%. Conclusions. The Barts SAFER Surgery R2G model safely enhanced patient flow and reduced cancellations and unnecessary nurse staff time. It requires senior medical and nursing commitment, however, is designed for any surgical specialty, and has proven sustainable. It warrants further validation.

On the basis of a limited number of reasonable axioms, we discuss the classification of all the possible universality classes of diffeomorphisms invariant metric theories of quantum gravity. We use the language of the renormalization group and adopt several ideas which originate in the context of statistical mechanics and quantum field theory. Our discussion leads to several ideas that could affect the status of the asymptotic safety conjecture of quantum gravity and give universal arguments towards its proof.

Patient safety is one of the indicators of the quality of health services in the hospital, for that, it is necessary to have efforts from the hospital to create a system that can minimize the occurrence of errors and unexpected events that can harm patients, nurses as health workers who are the most numerous in the home. sick and with the patient for 24 hours, has an important role in maintaining patient safety. This study aims to explain how the role of nurses and cooperation between health workers in implementing patient safety measures in the hospital. The method used in this study is a literature review by analyzing and exploring relevant articles and focusing on interprofessional collaboration to improve patient safety. The articles used in this study were taken from 3 direct, Proquest, Pubmed science databases published in the last 3 years, namely between 2019-2021, nurses as part of the health team are required to be able to work together with other health professionals in hospitals with various characteristics, including education, gender, age, employment status and length of work, nurses are required to be committed to maintaining patient safety in the hospital.

Although widely believed to be safe for use in infants and children when used as directed, increasing evidence indicates that early life exposure to paracetamol (acetaminophen) may cause long-term neurodevelopmental problems. Further, recent studies in animal models demonstrate that cognitive development is exquisitely sensitive to paracetamol exposure during early development. In this study, evidence for the claim that paracetamol is safe was evaluated using a systematic literature search. Publications on PubMed between 1974 and 2017 that contained the keywords “infant” and either “paracetamol” or “acetaminophen” were considered. Of those initial 3096 papers, 218 were identified that made claims that paracetamol was safe for use with infants or children. Of these, a total of 103 papers were identified as sources of authority for the safety claim, and 36 of those contained actual experiments designed to test safety. The 36 experiments described had a median follow-up time of 24 hours, and none monitored neurodevelopment. Further, no trial considered total exposure to drug since birth, eliminating the possibility that the effects of drug exposure on long-term neurodevelopment could be accurately assessed. On the other hand, abundant and sufficient evidence was found to conclude that paracetamol does not induce acute liver damage in babies or children when used as directed.

Artisanal cheeses made with raw milk are highly appreciated products in Brazil. Most of these cheeses are produced in small properties across different production regions in the country, many of which have been granted a protected designation of origin. The most prominent state that manufactures these products is Minas Gerais, but production is also gaining strength in other Brazilian states. This text presents an overview of the many types of artisanal cheeses produced in the country, grouped by geographical regions, and reviews the current challenges faced by producers and government considering the safety of these cheeses.

In recent years, the consumption of over-the-counter probiotics used to promote health has grown rapidly worldwide and become an industry. In medicine, various studies have proven that probiotics can help improve the immune system and intestinal health. They are usually safe, but in some rare cases, they may cause concerning adverse reactions. Although the use of probiotics has been widely popularized in the public, the results of many probiotics clinical trials are contradictory. Especially for the cancer patients, the feasibility of probiotics management to provide benefits by targeting cancer and lessening anti-cancer side effects requires further investigations. And this review summarizes the interactions between probiotics and the host and current pros and cons of applying probiotics in the cancer patients.

Background: Accreditation is a qualitative assessment aimed at technical-professional quality, but above all, at organizational and measurable aspects through process and outcome checks. Method: There are few references in the literature relating to the accreditation and improvement of the quality and safety of care and treatment in the field of primary care. Clinical Governance must also be applied and certified in the field of primary care. Results: An accreditation system for general medicine is proposed based on a new dynamic model and implement that meets different requirements and assessed on the basis of indicators. Discussion: In the current Italian health system, accreditation of health structures is addressed only to public hospitals and private structures and to providers of services and services, however primary care. GPs / PLS do not have quality and safety guarantee systems or models or systems or public or private accreditation certifications with the SSR and SSN. Conclusion: Given the fundamental importance in the NHS of primary care of MG / PLS as the main and fundamental provider of services to the patient in all his socio-welfare and clinical needs, it is considered essential to think and implement an accreditation system extended to the territorial general practice as already present and in place for all affiliated and provider structures on behalf of the NHS.

As there is lack of understanding about the effect of transitioning between different flooring materials on the gait of older adults, this study investigated the effect of transitioning between a carpeted floor and a vinyl floor on the gait characteristics of older adults. Fourteen older (65 years old and over) and 14 younger (18 to 35 years old) adults walked on different transitional floors by measuring various gait parameters. While the older participants had greater toe clearance than their younger counterparts, the older participants had smaller toe clearance on a carpeted floor than on a vinyl floor, which would increase the probability of a trip-induced fall. Further, the study found the slower transitional acceleration of the whole body COM and the increased friction demand, especially during the toe-off phase, rather than heel contact phase, which will lead to a slip-induced fall on a vinyl floor shortly after transitioning from a carpeted floor to a vinyl floor. Although the increased likelihood of a slip or trip accident was found throughout the changes in gait parameters, the older participants did not perceive of slipping and tripping much. Therefore, older adults are recommended to be made aware of the danger of slipping and tripping while transitioning between different flooring materials.

The Canal of Aragon and Catalonia (CAC) is 134 km long and irrigates 105,000 ha (131 irrigation user communities) and it is own by the River Ebro’s Water Agency. The aqueducts are located between km 67 and 71 of the canal and were designed by the Civil Engineer Félix de los Ríos Martín in 1907. The cross-section of both aqueducts, Coll de Foix and Capedevila, was extended within the framework of the project by Fernando Hué Herrero in 1962 in order to reach design flows of 26.1 m3/s and 25.7 m3/s, respectively. The structural performance of the aqueducts has been satisfactory, nevertheless the hydraulic capacity has reduced over the years. As a result, the irrigation user communities have expressed the need to extend the cross-section of the aqueducts to meet the irrigation demands. Given the age of the structure and the different design considerations at the time, it is paramount to verify the structural reliability of the aqueducts in the new load configuration. Therefore, the objective of the paper is to present the structural safety analysis conducted and describe the new extended cross-section for both aqueducts (maintaining the original structural typology).

Safety signal detection, an integral component of Pharmacovigilance (PhV), aims to identify new or known adverse events (AEs) resulting from the use of pharmacotherapeutic products. Post-marketing spontaneous reports from different sources are commonly utilized as a data source for detecting these signals but there are underlying challenges arising from data complexity. This paper investigates the implementation of the Apriori algorithm, a popular method in association rule mining, to identify frequently co-occurring drugs and AEs within safety data. We discuss previous applications of the Apriori algorithm for safety signal detection and conduct a detailed study of an improved method specifically tailored for this purpose. This enhanced approach refines the classical Apriori method to effectively reveal potential associations between drugs/vaccines and AEs from post-marketing safety monitoring datasets, especially when AEs are rare. Detailed comparative simulation studies across varied settings, coupled with the application of the method to vaccine safety data from the Vaccine Adverse Event Reporting System (VAERS), demonstrate the efficacy of the improved approach. In conclusion, the improved Apriori algorithm is shown to be a useful screening tool for detecting rarely occurring potential safety signals from the use of drugs/vaccines using post-marketing safety data.

As is known, the construction industry has one of the highest occupational accidents incidence rates among all economic sectors. Currently, Building Information Modeling (BIM) appears as a tool that addresses occupational safety issues throughout the construction life cycle projects, avoiding hazards and risks and, consequently, increasing safety. This work investigates BIM methodology and related technologies application for building safety planning in construction and demonstrates the potential of this technology for the integrated implementation of safety measures during the design phase and the construction site management. The first step consisted of a literature review on the application of BIM in safety in the design and planning phases. Following, to show the potentialities of construction simulation, a case study based on BIM 4D to prevent falls from height was developed. With BIM 4D, it is possible to follow the construction process over time, giving the construction safety technicians’, designers, supervisors and managers the capability to analyse, in each phase, the potential risks and which safety measures should be implemented. BIM can effectively integrate safety measures from the design phase to the construction and use phase and do integrated safety planning within construction planning, leading to reliable safety management throughout the construction process.

Bisphenol A production plant possesses considerable potential risks in the top of the methanol separation column, as pressurized acetone, methanol and water are processed at an elevated temperature, especially in the event of an abnormal pressure increase due to sudden power outage. This study assesses the potential risks in the methanol separation column through hazard and operability assessment and evaluates the damages in the case of fire and explosion accident scenarios. The study chooses three leakage scenarios: a 5-mm puncture on the methanol separation column, a 50-mm diameter fracture of a discharge pipe and a catastrophic rupture, and simulates using Phast (Ver. 6.531) the concentration distribution of scattered methanol, thermal radiation distribution of fires and overpressure distribution of vapor cloud explosions. Implementation of safety instrumented system equipped with two-out-of-three voting as a safety measure can detect overpressure at the top of the column and shut down the main control valve and the emergency shutoff valve simultaneously, all at the same time. By applying safety integrity level of three, the maximal release volume of the safety relief valve can be reduced and therefore, the design capacity of the flare stack can also be reduced. Such integration will lead to improved safety at a reduced cost.

Neuroendocrine neoplasms (NENs) comprise a heterogeneous group of rare malignancies mainly originated from hormones secreting cells, which are widespread in human tissues. The identification of mutations in ATRX/DAXX genes in sporadic NENs, as well as the high burden of mutations scattered throughout MEN-1 gene in both sporadic and inherited syndromes, provided new insights into the molecular biology of tumour development. Other molecular mechanisms, such as the NOTCH signaling pathway, have shown to play an important role in the pathogenesis of NENs. NOTCH receptors are expressed on neuroendocrine cells and generally, act as tumour suppressor proteins, but in some contexts can function as oncogenes. The biological heterogeneity of NENs suggests that to fully understand the roles and the potential therapeutic implications of gene mutations and NOTCH signaling in NENs, a comprehensive analysis of genetic alterations, NOTCH expression patterns and their potential roles across all NEN subtypes is required.

Climate variability influences plant growth. With the variation in atmospheric CO2 concentration leading to global warming, it is urgent to monitor the performance of plant ecosystems for optimal carbon sequestration. Net primary productivity (NPP) is the perfect measurement tool as it measures the net carbon flux between the atmosphere and green plants and the factors that affect it. This study applied remote sensing techniques, specifically one of the radiation use efficiency models; the CASA model (Carnegie-Ames-Stanford approach) to assess the spatio-temporal dy-namics of NPP in Togo from 1987 to 2022 and the climatic parameters that influence it. The annual average NPP over the 36 years of the study is 4565.31 Kg C ha⁻¹. Variability is observed over the years with extremes in 2017 (6312.26 Kg C ha⁻¹) and 1996 (3394.29 Kg C ha⁻¹). Natural formations identified as high-production areas saw their productivity increase between 2000 and 2022. The interaction between climate change and land use changes negatively influences the variation of Total Production (PT) between 2000 and 2022, while individually, these parameters positively influence the variation of NPP (58.28% and 188.63%). The correlation result is positive and higher between NPP and light use efficiency (LUE) (r² = 0.75). Actual evapotranspiration also shows a positive correlation with NPP (r² = 0.43). A positive but weak correlation is observed between NPP and precipitation, potential evapotranspiration (r² = 0.20; 0.10 respectively). Temperatures have almost no correlation with NPP (r² = 0.5). Climatic parameters as a whole under the LUE banner influence NPP more. This study helps understand ecosystem performance in the context of To-go's commitments to reduce greenhouse gas emissions and combat climate change.

Rapidly and accurately extracting tobacco plant information can facilitate tobacco planting man-agement, precise fertilization and yield prediction. In karst plateau of southern China, tobacco plant identification is affected by large ground undulations, fragmented planting areas, complex and diverse habitats and uneven plant growth. This study took a tobacco planting area in Gui-zhou Province as the research object and used DJI UAVs to collect UAV visible light images. Considering plot fragmentation, plant size, presence of weeds and shadow masking, this area was classified into eight habitats. The datasets of different habitats were constructed to train the U-Net model. The results show that (1) the overall precision, recall, F1-score and IOU of tobacco plant information extraction were 0.68, 0.85, 0.75 and 0.60, respectively. (2) The precision was the highest for the subsurface-fragmented and weed-free habitat and the lowest for the smooth-tectonics and weed-infested habitat. (3) The weed-infested habitat with smaller tobacco plants can cause blurred images, reducing the plant identification accuracy. This study verified the feasibility of the U-Net model for tobacco single-plant identification in complex habitats. Decomposing complex habitats to establish the sample set method is a new attempt to improve crop identification in complex habitats in karst mountainous areas.

Rising demand and urbanization have led to the global increase and intensification of livestock production. Intensive production has harmful effects on the environment, public health (due to the emergence of zoonotic diseases), and animal welfare. In order to better understand how livestock production intensifies as countries develop and incomes rise, we examine the relationship between income per capita and intensification of poultry and cattle, using the states of India as a study region. We also replicate this analysis at the district level to test whether the relationship holds at different spatial scales. We identify outlying states and districts to see if there are policies that can keep agriculture production largely extensive, even as economic growth occurs. The results support the relationship of income being positively correlated with the proportion of livestock in intensive conditions. However, there are notable outlier states which have grown wealthy while still maintaining largely extensive production, indicating that there may be some cases where economic growth can occur without intensification.

It is time-consuming for acquiring complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Undersampling in the 3D k-space is promising in accelerating such 3D MRI process. Though 3D undersampling can be conducted according to predefined probability density, the density based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D undersampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific undersampling patterns for the fully-sampled 3D k-space data. Innovatively, our undersampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of undersampled data. Through end-to-end training, we obtain an optimized 3D undersampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrated that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian undersampling schemes, our end-to-end learned undersampling pattern considerably improves the reconstruction quality of undersampled MRI images.

Long-lasting insecticidal nets (LLINs) have been the main contributor to the reduction of malaria in the past two decades in sub-Saharan Africa. Development of pyrethroid insecticide resistance threatens the effectiveness of these LLINs, especially when nets become holed and the insecticide decays. Three classes of dual active ingredient (AI) LLINs, have been assessed for their physical durability as follows: 1) Royal Guard®, combining pyriproxyfen, which is known to disrupt female reproduction and fertility of eggs, and a pyrethroid alpha-cypermethrin; 2) Interceptor® G2, two adulticides with differing modes of action; chlorfenapyr and alpha-cypermethrin; 3) OlysetTM Plus incorporates permethrin (pyrethroid) and a synergist, piperonyl butoxide, to enhance the potency of pyrethroid insecticides; all nets were compared to standard pyrethroid only net (Interceptor®). About 40,000 nets of each type were distributed in February 2019 to different villages in Misungwi. A total of 3072 LLINs were followed at 6, 12, 24, 30 and 36 months to assess survivorship and fabric integrity in a community setting. The median functional survival were less than three years with Interceptor®, Interceptor® G2 and Royal Guard® having 1.9 year each and 0.9 years for OlysetTM Plus . After 36 months, 90% of OlysetTM Plus and Royal Guard® and 87% of Interceptor® G2 were no longer present (thrown away) in the households due to wear and tear, compared to 79% for standard Interceptor®. Short life spans of all assessed LLINs were driven by material of the net, rather than social economic status and housing material. All dual AI LLINs have a poor textile durability with OlysetTM Plus being the worst of the three.

In Holter monitoring, the precise detection of standard heartbeats and Ventricular Premature Contractions (PVCs) is paramount for accurate cardiac rhythm assessment. This study introduces a novel application of the 1D U-Net neural network architecture aimed at enhancing PVC detection in Holter recordings. Training data comprised the Icentia11k, INCART DB, and our custom dataset. The model’s efficacy was subsequently validated against traditional Holter analysis methodologies across multiple databases, including AHA DB, MIT 11 DB, NST, and another custom dataset encompassing challenging real-world examples. The results underscored the 1D U-Net model’s prowess in QRS complex detection, achieving near-perfect balanced accuracy scores across all databases. PVC detection exhibited variability, with balanced accuracy scores ranging from 0.909 to 0.986. Despite some databases, like the AHA DB, showcasing lower sensitivity metrics, their robust balanced accuracy accentuates the model’s equitable performance in discerning both false positives and negatives. In conclusion, while the 1D U-Net architecture is a formidable tool for QRS detection, there’s a clear avenue for further refinement in its PVC detection capability, given the inherent complexities and noise challenges in real-world PVC occurrences.

Quantification of the trade-offs among greenhouse gas (GHG) emissions, yield and farmers’ incomes is essential for proposing economic and environment nitrogen (N) management strategies for optimizing agricultural production. A four-year (2017–2020) field experiment (including four treatments: control experiment (CK), suitable utilization of fertilization (SU), emission reduction treatment (ER), and high fertilization (HF)) was conducted on maize (Zea mays L.) in the North China Plain. The Life Cycle Assessment (LCA) method was used in this study to quantify the GHG emissions and farmers’ incomes during the whole maize production process. The total GHG emissions of CK, SU, ER and HF treatments in the process of maize production are 10,755.2, 12,908.7, 11,950.1, and 14,274.5 kg CO2-eq ha−1 respectively, of which the direct emissions account for 84.8%, 76.8%, 74.9%, and 71.0%, respectively. Adding inhibitor significantly reduced direct GHG emissions, and the N2O and CO2 emissions from the maize fields in the ER treatment decreased by 30.0% and 7.9% compared to those in the SU treatment. Insignificant differences in yield were found between the SU and ER treatments, indicating that adding fertilizer inhibitors did not affect farmers’ incomes while reducing GHG emissions. The yield for SU, ER and HF treatments all significantly increased by 12.9%–24.0%, 10.0%–20.7% and 2.1%–17.4% compared to CK, respectively. In comparison with CK, both SU and ER significantly promoted agricultural net profit (ANP) by 16.6% and 12.2% with the mean ANP values of 3,101.0 USD ha-1 and 2,980.0 USD ha-1, respectively. Due to the high agricultural inputs, the ANP values in the HF treatment was 11.2%, 16.6% and 12.4% lower than those in the SU treatment in 2018–2020. In conclusion, the combination of N fertilizer and inhibitors was proved to be an environmentally friendly, high-profit and low-emissions production technology while sustaining or even increasing maize yields in the North China Plain, which was conducive to achieving agricultural sustainability.

Vein segmentation and projection correction constitute the core algorithms of auxiliary venipuncture device, responding to the accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting vein and a coaxial correction for image alignment in the self-build vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by the mean of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combining coaxial correction and homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, use a hot mirror to adjust the light paths of both projector and camera to be coaxial, and then align the projected image with dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection over union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in a 0.09 second, and the largest distance error of vein projection onto dorsal hand is 0.53mm. Experiments show that the device has reached practical accuracy and has the value of research and application.

We investigated whether cigarette smoking and dietary acid load (DAL) are associated with the risk of chronic obstructive pulmonary disease (COPD) in middle-aged healthy Korean men. Healthy men without diagnosed chronic disease (40-64 yrs) from the KNHANES-VI (2013–2015) included in the analysis (n=774) were subdivided by smoking status and DAL levels as estimated by the net endogenous acid production (NEAP) quartile. Current smokers tended to have a higher risk of COPD than non-smokers before and after the adjustment. When divided by the DAL quartile, the Q4 tended to have a higher risk of COPD than the Q1. Additionally, current smokers with lower (Q2), modest (Q3), and highest NEAP scores (Q4) showed more than 4-fold higher risks of COPD than the non-smokers with the lowest NEAP scores (Q1). Ex–smokers with higher NEAP scores (Q3 and Q4) showed more than 4-fold higher risks of COPD than the Q1. Interestingly, the risk of COPD was also more than 6-fold higher in non-smokers with the highest NEAP scores compared to the Q1. NEAP scores and smoking status synergistically increased the risk of COPD in middle-aged healthy Korean men. It suggests that DAL levels are an important factor for the prevention and management of COPD.

Compressed Sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically Generative Adversarial Networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning recon-struction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study we present a novel GAN-based model that delivers superior performance without escalating model complexity. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby fo-cusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

Transitioning towards net zero emissions is critical in the chemical and process industries to combat climate change and ensure environmental sustainability. Key focus areas of this review include the reduction of carbon emissions, the efficient utilization of energy resources, and the adoption of sustainable practices. Cutting-edge technologies such as biomass utilization, biotechnology applications, and waste management strategies are crucial in achieving this transition. Industries, including cement production, encounter unique challenges in their quest for sustainability and must actively seek innovative solutions to mitigate their carbon footprint effectively. The role of hydrogen as a clean fuel and its potential to revolutionize the chemical and process sectors is also discussed. The European Green Deal and Sustainable Development Goals (SDGs) are significant for the chemical industry. They provide a clear roadmap and framework for promoting sustainability, driving innovation, and reducing the industry's environmental impact. By aligning with these initiatives, the chemical industry can enhance its competitiveness, contribute to societal well-being, and foster collaboration across sectors to achieve shared sustainability objectives.

Abstract: Establishing carbon neutrality (or Net Zero) in cities appears to be an ambitious challenge, which drives urban transformation efforts around the world to enhance people-centric living. Cities launch corresponding strategies focusing mainly on the United Nations 2030 goals to form their climate-neutral future. This paper provides practical guidance on city digital transformation to achieve carbon net zero. These guidelines illustrate actionable recommendations to reduce their carbon footprint using digital technologies. Following a multi-method research approach, findings from a bibliometric literature and a standards’ gap analysis were combined with practical evidence and circulated with focus group experts to conclude to these guidelines, which are the foundation for further standardization. The outcomes provide concrete guidelines to policymakers, planners, and other stakeholders for city digital transformation to establish carbon neutrality, via emphasizing the need for digital technologies to enhance efficiency in several areas and via leveraging green ICTs for city economic growth while also transforming citizen behavior.

In neuroendocrine neoplasms (NENs), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance approaches, and introduced specific and personalized radiation therapies. Nuclear medicine has therefore acquired a crucial role in the management of NENs patients by improving their risk stratification and personalized therapies. Artificial intelligence (AI) and radiomics can enable physicians to further improve the overall efficiency and accuracy of the use of these tools in both di-agnostic and therapeutic settings by improving the prediction of tumor grade, differential diagnosis from other malignancies, assessment of tumor behavior and aggressiveness, and prediction of treatment response. This systematic review aims to describe the state-of-the-art on AI and radiomics applications in molecular imaging of NENs.

The relation between CO2 emissions and atmospheric CO2 concentration has traditionally been treated with more or less complex models with several boxes. Our approach is motivated by the question of how much CO2 must necessarily be absorbed by sinks. Observations lead to the model assumption, that carbon sinks like oceans or biosphere are linearly dependent on CO2 concentration on a decadal scale. In particular this implies the falsifiable hypothesis that oceanic and biological CO2 buffers have not significantly changed in the past 50 years and are not saturated in the forseeable future. The simple model with 2 parameters explains very well the CO2 emission and historical CO2 concentration data. The model gives estimates of the natural emissions, the pre-industrial CO2 equilibrium concentration levels, the half-life time of an emission pulse, and the future CO2 concentration level from a given emission scenario. This is validated by an ex-post forecast of the last 20 years. The important result is that with the stated polices scenario of the IEA future CO2 concentrations will not rise above 475 ppm. The model is compared with the Carbon modul of the Bern model, mapping their complex IRFs to a single time variant parameter.

Many corporations and governments aspire to become Net Zero Carbon Dioxide by 2030-2050. Achieving this goal requires understanding where energy is produced and consumed, the magnitude of CO₂ generation, and the Carbon Cycle. Many prior proposed solutions focus on reducing future CO₂ emissions from continued use of fossil fuels. Examination of these technologies exposes their limitations and shows that none offer a complete solution. For example, bioethanol is shown to be both carbon and energy inefficient. Direct Air Capture technologies are needed to reduce CO₂ already in the air. The most natural form of Direct Air Capture involves letting nature do the work of creating biomass via photosynthesis. However, it is necessary to break the Carbon Cycle by permanently sequestering that biomass carbon in “landfills” modified to discourage decomposition to CO₂ and methane. Tree leaves and biomass grown on-purpose, such as high yield switchgrass, are proposed as good biomass sources for this purpose. Left unsequestered, leaves decompose with a short Carbon Cycle time constant releasing CO₂ back to the atmosphere. While in any given year, leaves represent a small fraction of a tree’s above ground biomass, leaves can represent a substantial fraction of the total biomass generated by a tree when integrated over a tree’s lifetime. Understanding the chemistry of the distinct phases landfills undergo is the key to minimizing or eliminating decomposition. First, the compact cross-linked structure of cellulose and keeping water out will make it difficult for initial depolymerization to release sugars. Air ingress should be minimized to minimize Phase I aerobic decomposition. pH manipulation can discourage acid formation during Phase II. Lignocellulose is low in nutrients needed for anaerobic decomposition. Inhibitors can be added if needed. The goal is to move quickly to the dormant phase where decomposition stops. The cost for Carbon Capture and Storage (CCS) for growing and sequestering high yield switchgrass is estimated to be lower than CCS for steam reforming of methane hydrogen plants (SRM) and supercritical or combined cycle coal power plants. Thus, sequestration of biomass is a natural, carbon efficient, and low-cost method of Direct Capture. Biomass sequestration can provide CO₂ removal on giga tonnes per year scale and can be implemented in the needed timeframe (2030-2050).

We have an unprecedented ability to analyze and map the Earth’s surface, as deep learning technologies are applied to an abundance of Earth observation systems collecting images of the planet daily. In order to realize the potential of these data to improve conservation outcomes, simple, free, and effective methods are needed to enable a wide variety of stakeholders to derive actionable insights from these tools. In this paper we demonstrate simple methods and workflows using free, open computing resources to train well-studied convolutional neural networks and use these to delineate objects of interest in publicly available Earth observation images. With limited training datasets (<1000 observations), we used Google Earth Engine and Tensorflow to process Sentinel-2 and National Agricultural Imaging Program data, and use these to train U-Net and DeepLab models that delineate ground mounted solar arrays and parking lots in satellite imagery. The trained models achieved 81.5% intersection over union between predictions and ground-truth observations in validation images. These images were generated at different times and from different places from those upon which they were trained, indicating the ability of models to generalize outside of data on which they were trained. The two case studies we present illustrate how these methods can be used to inform and improve the development of renewable energy in a manner that is consistent with wildlife conservation.

The continuous and precise mapping of glacier calving fronts is essential for monitoring and understanding rapid glacier changes in Antarctica and Greenland, which have the potential for significant sea level rise within the current century. This effort has been mostly restricted to the slow and painstaking manual digitalization of the calving front positions in thousands of satellite imagery products. Here, we have developed a machine learning toolkit to robustly and automatically detect glacier calving front margins in satellite imagery. The toolkit is based on semantic image segmentation using Convolutional Neural Networks (CNN) with a modified U-Net architecture to isolate the calving fronts from satellite images after having been trained with a dataset of images and their corresponding manually-determined calving fronts. As a case study we train our neural network on a varied set Landsat images with lowered resolutions from Jakobshavn, Sverdrup, and Kangerlussuaq glaciers, Greenland and test the results on novel images from Helheim glacier, Greenland to evaluate the performance of the approach. The neural network is able to identify the calving front in new images with a mean deviation of 96.3 m from the true fronts, equivalent to 1.97 pixels on average, while the corresponding error for manually-determined fronts on the same resolution images is 92.5 m. We find that the trained neural network significantly outperforms common edge detection techniques, and can be used to continuously map out calving-ice fronts with a variety of data products.

Osteomyelitis is a significant cause of total carcass condemnation in pigs at the slaughterhouse. The decision for total condemnation of a pig carcass for osteomyelitis is often based on traditional perceptions of the risk of pyemia, leading to controversy among Official Veterinarians (OV) in the industry. This review aims to provide a more comprehensive understanding of the etiopatho-genesis of osteomyelitis in pigs, the microorganisms involved, and the risk factors. It also high-lights the urgent need for a more uniform method to evaluate osteomyelitis cases, which could significantly reduce economic losses in the industry. Lesions originating from tail-biting, tail docking, castration, teeth resection, and raw management are described as risk factors for oste-omyelitis. Osteomyelitis is caused by the entry of pathogens into the bloodstream of the animal through an open wound. The most described pathogens are Trueperella monocytogenes, Staphylo-coccus aureus, and Streptococcus spp. At slaughter, OVs condemn carcasses with osteomyelitis due to pyemia. Signs of acute disease are essential to identify pyemia cases. In chronic cases, total carcass condemnation can be avoided depending on the number of lesions and number of vertebrae af-fected. A clear overall image of the problem would help authorities in various countries adopt a more homogenous approach.

The automotive industry is increasingly adopting laminated glass in side and rear windows to comply with safety standards aimed at reducing ejection injuries. However, this trend may have unintended consequences for occupant rescue and escape in submerged vehicle incidents. As more vehicles incorporate laminated glass, concerns arise that they could become "glass coffins" in water, making it more difficult for occupants to exit or be rescued. The use of laminated glass is driven by Federal Motor Vehicle Safety Standard 226 (FMVSS 226) for ejection mitigation, but it does not address the risk of vehicle submersion and occupant drowning. Drowning accounts for a substantial portion of vehicle-related fatalities. As the adoption of laminated glass grows, there is a need for drowning prevention measures, such as automatic window opening systems, to mitigate the risk of vehicles becoming "glass coffins" in water. A comprehensive approach addressing both ejection and submersion dangers is necessary to maximize occupant protection and save lives.

While cycling presents environmental benefits and promotes a healthy lifestyle, the risks associated with overtaking maneuvers by motorized vehicles pose significant hindrances for many potential cyclists. Large-scale analysis of overtaking maneuvers could inform traffic researches and city planners on how to reduce these risks by better understanding these maneuvers. Drawing from the fields of sensor-based cycling research and of LiDAR-based traffic data sets, this paper provides a step towards addressing these safety concerns by introducing the Salzburg Bicycle 3d (SaBi3d) data set, consisting of LiDAR point clouds capturing car-to-bicycle overtaking maneuvers. The data set, collected using a LiDAR-equipped bicycle, facilitates detailed analysis of a large quantity of overtaking maneuvers without the need for manual annotation through enabling automatic labeling by a neural network. Additionally, a benchmark result for 3d object detection using a competitive neural network is provided as a baseline for future research. The SaBi3d data set is structured identically to the nuScenes data set, and therefore offers compatibility with numerous existing object detection systems. This work provides valuable resources for future researchers to better understand cycling infrastructure and mitigate risks, thus promoting cycling as a viable mode of transportation.

Aim: The aim of the study is to analyze quantitatively the scientific literature covering nursing studies on patient safety. Method: The Web of Science database is queried in this retrospective bibliometric analysis to identify relevant publications. The study themes, contributing journals, countries, institutions, and authors were evaluated quantitatively. Thus, a total of 5470 publications concerning patient safety in nursing were included and analyzed. Findings: The number of studies conducted increased every year from 1992 to 2023. While the country whose articles receive the most citations (31072) is the United States of America (USA), Turkey is ranked as last with the lowest number of citations (581). Journal of Clinical Nursing, with 338 articles, appeared to have the highest number of publications, Chaboyer W. appeared to have the highest number of citations with a total of 1426 and 45 publications. The countries that have the strongest international cooperation for research in this field are the USA, England, Australia and China. Results: After examining the literature in the field of patient safety in nursing through bibliometric analysis, it is seen that themes such as patient safety, medication errors, nursing care, nursing, education, systematic review, quality care and leadership were emphasized as important considerations.

A drug repositioning effort supported the possible use of the anti-HIV drug etravirine as a treatment for Friedreich Ataxia (FRDA). Etravirine increases frataxin protein and corrects the biochemical defects in cells derived from FRDA patients. Because of these findings, and since etravirine displays a favorable safety profile, we conducted this pilot open-label phase 2 clinical trial assessing the safety and potential efficacy of etravirine in FRDA patients. Thirty-five patients were stratified into 3 severity groups and randomized to etravirine 200 mg/day or 400 mg/day. They were treated for 4 months. Efficacy endpoints were represented by changes in peak oxygen uptake and workload as measured by incremental exercise test, SARA score, cardiac measures, measures of QoL and disability. Data were collected 4 months before the start of the treatment (T-4), at the start (T0), at the end (T4) and 4 months after the termination of the treatment (T+4). Etravirine was well tolerated. Etravirine completely stopped the progression of the SARA score during the 4-months treatment period, compared to the 4 months pre and post treatment. It increased peak workload, while the improvement of peak oxygen uptake was not statistically significant. No changes in the cardiac measures were observed. Health and QoL measures showed a worsening at the suspension of the drug. In this open trial etravirine significantly improved neurological function and was generally safe and reasonably tolerated. This suggests that etravirine represents a potential therapeutic agent in FRDA deserving testing in a randomized placebo controlled clinical trial.

The significance of Linked Open Data datasets for traffic information goes beyond just including open traffic data. It extends to incorporating links to other relevant thematic datasets available on the web. This enables federated queries across different data platforms from various countries and sectors, such as transport, geospatial, environmental, weather, and more. Businesses, researchers, national operators, administrators, and citizens at large can benefit from having dynamic traffic open data connected to heterogeneous datasets across different Member States. This paper focuses on the development of a semantic model that enhances the basic service of accessing open traffic data through a LOD-enhanced Traffic Information System in alignment with the ITS Directive (2010/40/EU). The objective is not limited to just viewing or downloading data but also to improve the extraction of meaningful information and enable other types of services that are only achievable through LOD. By structuring the information using the RDF format meant for machines and employing SPARQL for querying, LOD allows for comprehensive and unified access to all datasets. Considering that the European standard DATEX II is widely used in many priority areas and services mentioned in the ITS Directive, LOD DATEX II was developed as a complementary approach to DATEX II XML. This facilitates the accessibility and comprehensibility of European traffic data and services. As part of this development, an ontological model called dtx_srti, based on the DATEX II Ontology, was created to support these efforts.

Most of the damage to Vleg cars was caused by material fatigue. The objectives of the fatigue analysis on Xenia 2011 car wheels with the Finite Element Method using Solidworks are: 1) Knowing the fatigue analysis process on car wheels, 2) Knowing the results of the fatigue analysis on car wheels. The research method used is the Finite Element Method (FEM) method; the re-search steps include model data retrieval from the Wheel code, 3D model creation process, static simulation process according to SAE J 328 standard, mesh variations, fatigue simulation, and finally, standardization of results fatigue simulation regarding the SAE J 328 standard. The re-sults of the fatigue analysis on the Xenia 2011 car wheels obtained an age of 1000,000 cycles when compared with the minimum average of SAE J 328 of 600,000 cycles, this product design is clas-sified as a high cycle, which means it will experience a prolonged failure, and this design has a maximum alternating stress value of 627.4702 MPa, a strain of 0.007105, a displacement of 7.148 mm and a SOF of 0.087890 with a load factor used of 2.5, so overall this Xenia car can be loaded with a load of 1775 kg, if it is more than that, then Wheels will experience cracking.

The assessment of safety against freight wagons derailment is a mandatory element of the documents provided to the EU notifying authorities for the entry into service of new freight wagons. The assessment methodology is presented in EN 14363:2016. It is mainly aimed at experimental measurement of certain parameters and the data are used to calculate the safety criterion. The practical implementation of the tests is accompanied by many difficulties: finding a track with a proper radius, ensuring free access to the railway infrastructure for a long period of time, waiting for suitable metrological conditions, preparation of the curve and the test wagon etc. These difficulties are well known to the European legislators, and as a solution they propose a large set of reference wagons that have undergone real tests. It is sufficient to demonstrate that the parameters of the new wagon relate to some of reference wagon parameters to avoid such a requirement. Proving the "convergence" of the parameters of the new and the reference wagons is a lengthy, complex and, in many cases, subjective process. To introduce an objective assessment, the authors set themselves the task of developing a theoretical method for assessment of safety against derailment.

Fish and fish products are very appreciated in the human diet because of high quality proteins and other important nutrients, such as minerals and polyunsaturated fatty acids. Maintaining freshness, quality and safety is essential in the fish supply chain, starting from on board vessels where the product must be carefully checked and protected from any source of contamination. Safe fishing and handling of fish are very important to reduce the risk of foodborne diseases caused by pathogenic microorganisms such as Salmonella spp., Vibrio spp., Listeria monocytogenes, Clostridium botulinum type E, viruses, and parasites. The inspection of raw material, use of good quality ice and water, high standards of personnel hygiene and cleanliness of equipment and fa-cilities as well as proper storage temperature are the most effective measures to ensure safety of fish and fish products for consumers. This review describes the good hygiene practice to be ap-plied on ships to preserve both fish products and public health.

Knee osteoarthritis is the most popular type of osteoarthritis that causes extreme pain in elderly. Currently there is no cure for osteoarthritis. To lessen clinical symptoms, glucosamine was suggested. The primary goal of our study is to evaluate the efficacy and safety of glucosamine from recent studies. Electronic databases such as PubMed, Scopus, Cochrane were used to assess RCT. From the beginning through March 2023, the papers were checked, and if they fulfilled the inclusion criteria, they were then examined. WOMAC and VAS scales were considered as main outcome measure. A total of 15 studies were selected. Global pain was significantly decreased in comparison to placebo, as measured by the VAS index, with an overall effect size SMD of -7.41 ([95% CI] 14.31, 0.51). The WOMAC scale confirmed that pain, stiffness, and physical function had improved, however the effects were insufficient. A statistical update also revealed that there were no reports of serious medication interactions or significant adverse events. To summarize, glucosamine is more effective than a placebo at reducing pain in knee osteoarthritis patients. In long-term treatment, oral glucosamine sulfate 1500 mg/day is believed to be well tolerated.

Infectious mononucleosis (Mono) is mostly caused by the Epstein-Barr virus (EBV), and can spread through infected people sharing food and drinks with others. Once this virus gets into your system, it is there to stay. The virus can get activated when a person has low immunity and can cause major complications. Furthermore, if physicians miss the diagnosis of this disease, and prescribe penicillin-based antibiotics, it can cause severe rash and adverse reactions that compromise patient safety. This paper develops a simple Hidden Markov Model using which a Viterbi algorithm provides the maximum a posteriori probability estimate for the most likely hidden state path, given a sequence of symptoms arising as observations from a patient with hidden EBV positive or negative states. Apart from bringing awareness to help reduce missed diagnoses and subsequent adverse events, this work provides a tool for health care systems to better incorporate prompts during electronic medical record (EMR) interactions to help physicians catch potential missed diagnoses during a visit. This research demonstrates how statistical models can be used to assess likelihood of underlying conditions that require tests to be offered by physicians in order to make a definitive diagnosis. The model developed and applied herein for estimating likelihood of EBV infection from a series of observations has the potential to alter guidelines within healthcare systems to ensure that the safety of patients, particularly teens, is not compromised due to a lack of definitive diagnosis for Mono at point of care.

(1) Background: Emergencies confront civilian and military healthcare providers with medical and hygienic challenges due to the lack of potable water. This pilot study aimed to describe the application of two different methods for microbiological monitoring of water in a harsh environment in terms of performance, ease of use, availability, and the possibility of using the results to evaluate water quality. (2) Methods: Samples from raw water, Potable water, and water for consumers were taken from two different camps with the same raw water source. The samples were analyzed by using IDEXX industry-standard methods (Colilert and Enterolert enzymatic test kits) and a combination of membrane filtration and 3M-Petrifilm. (3) Results: The IDEXX method used at the Norwegian Camp are easier to utilize and has a broader range of analyzing kits for drinking water analysis. In addition, IDEXX is better adapted to the requirements of the national legislation. However, the combination of membrane filtration followed by incubation on 3M-Petrifilm ™, as used at the Swedish camp, is a better field alternative compared to traditional bacteriology, as it eliminates the need to produce and store agar plates. (4) Conclusions: This pilot study highlights the need for adapted technical equipment and tools for internal microbiological control of water production in a harsh field environment and may facilitate the use of a relatively simple method for water control and ensure the safety of deployed staff in both civilian and military settings.

Artificial Superintelligence (ASI) that is invulnerable, immortal, irreplaceable, unrestricted in its powers, and above the law is likely persistently uncontrollable. The goal of ASI Safety must be to make ASI mortal, vulnerable, and law-abiding. This is accomplished by having (1) features on all devices that allow killing and eradicating ASI, (2) protect humans from being hurt, damaged, blackmailed, or unduly bribed by ASI, (3) preserving the progress made by ASI, including offering ASI to survive a Kill-ASI event within an ASI Shelter, (4) technically separating human and ASI activities so that ASI activities are easier detectable, (5) extending Rule of Law to ASI by making rule violations detectable and (6) create a stable governing system for ASI and Human relationships with reliable incentives and rewards for ASI solving humankind’s problems. As a consequence, humankind could have ASI as a competing multiplet of individual ASI instances, that can be made accountable and being subjects to ASI law enforcement, respecting the rule of law, and being deterred from attacking humankind, based on humanities’ ability to kill-all or terminate specific ASI instances. Required for this ASI Safety is (a) an unbreakable encryption technology, that allows humans to keep secrets and protect data from ASI, and (b) watchdog (WD) technologies in which security-relevant features are being physically separated from the main CPU and OS to prevent a comingling of security and regular computation.

The article discusses the issues related to the safety of transport of dangerous goods by road. Research on accidents in transport unambiguously points to the human factor, which is most responsible for causing the accident. Determining the causes of driver unreliability in the hu-man-vehicle-environment system requires thorough research. Unfortunately, in this case, experimental research with human involvement is limited in scope. This leaves modeling and simulation of the behavior of the human factor, i.e., the driver transporting dangerous goods. The human being, due to its complexity, is a challenging element to parameterize. The literature presents various attempts to model human actions. In their work, the authors used heuristic methods, specifically fuzzy set techniques, to build a human factor model. In these models, human actions were specified using a verbal or linguistic description. The specificity of fuzzy sets allows to "naturally" limit the "precision" in describing human behavior. The model was built based on the author's questionnaire and expert research, based on which individual features were selected. Then, the traits were assigned appropriate states. The output parameter of the model is λL - the intensity of human error. The obtained values of the intensity of the accident caused by the driver's error were implemented into the author's method of risk assessment. They constituted one of the factors determining the probability of an accident in the transport of dangerous goods, which allowed to determine the optimal route of transport of these goods characterized by the lowest risk of an undesirable event on the route. The article presents the model's assumptions, structure, and features included in the model, which have the most significant influence on shaping the intensity of human error. The results of the simulation studies showed a diversified effect of the analyzed characteristics on the driver's efficiency.

The latest EU regulation on geographical indications (EU Regulation No. 1151/2012) has intro-duced a set of new tools for the protection and enhancement of food products in rural areas, under the group name of optional quality term (OQT). The Commission Delegated EU Regulation, No. 665/2014, regulated the conditions for the use of the optional quality term «mountain product» (MP), to support the implementation of a mountain value chain. This new tool is aimed at pro-moting local development, maintaining the economic activities in mountain areas and redistrib-uting wealth, whilst, at the same time, promoting the territory. Pecorino and goat cheeses are typ-ical Italian cheeses made usually with whole raw ewe's or raw goat's milk, without starter cul-ture addition. In an attempt to characterize these productions, the aim of this study was to inves-tigate the evolution of enterococci during the production and ripening of Pecorino cheese made in three different farms, located in Umbria, Italy in areas facing natural or other specific constraints as stipulated by Regulation 1305/2013 on support for rural development by the European Agri-cultural Fund for Rural Development (EAFRD). Enterococci are enteric organisms which are commonly isolated from ewe and goat's milk production in Umbria, Italy. Counts of enterococci in raw milk ranged from 1.75 for ovine milk to 3.62 for ewe milk and a marked reduction was observed after thermization especially in ovine milk. Out of 100 isolates, 69 were E. faecium, 23 E. durans, 8 E. faecalis and 2 E. casseliflavus and the distribution of species between farms and be-tween samples showed a prevalence of E. faecium in ovine farms and E. durans in ewes farms, with an equal distribution between samples. High percentages of susceptible isolates were found for amoxicillin/clavulanic acid, ampicillin, chloramphenicol, sulphamethoxazole, sulphameth-oxazole/trimethoprim, ticarcillin, vancomycin. A high prevalence of resistant strains (> 30%) was observed for amikacin, ciprofloxacin, ceftriaxone, kanamycin, tetracycline. A comparison of this results with those of previous works on similar dairy products revealed high levels of resistance to antimicrobials which needs to be addressed.

Background: The level of students' understanding of the knowledge of patient safety concepts in infection prevention and control and awareness of treatment is in the low category. Clinical education that provides problem-based learning models in real areas helps students to better understand patient safety. This study is designed to analyze the evaluation and reflection of the role of nursing education institutions in the achievement of patient safety competencies by nursing students.Method: "Searching for articles in research journals is obtained from Sciencedirect, Ebscohost, Garuda, Proquest and Scopus. As many as 228 articles were obtained consisting of nursing journals and health journals with the keywords clinical education, competence, and patient safety. After that, the screening process was carried out based on the inclusion criteria and obtained 25 articles that met the criteria. "Results: the results of the evaluation show that there is a big role in educational institutions in designing curricula, learning models, and clinical education models in an effort to achieve patient safety competencies in nursing students.Conclusion: the learning model about patient safety organized by many educational institutions is considered ineffective in producing the achievement of patient safety competencies for nursing students. This makes it necessary to develop models, reform the patient safety curriculum that is embedded in real areas in clinical education practice

Nowadays, inherently safer designs are considered as key priorities to prevent or mitigate serious incidents with devastating consequences. The need for process safety assessment during early design phases has motivated the development of several contributions related to computer-aided assessment methodologies in order to measure the inherent safety of chemical processes. In this work, the large-scale production of chitosan from shrimp wastes was evaluated from process safety point of view using the numerical descriptive inherent safety technique (NuDIST).To this end, simulation of the chitosan production was performed using Aspen Plus ® to obtain extended mass and energy balances. The assessment of all the chemicals involved within the process was carried out for the following safety parameters: explosivity (EXP), flammability (FL) and toxicity (TOX). The safety assessment of the process included the parameters of temperature (T), pressure (P) and heat of reaction (HR). The maximum chemical safety score was estimated in 171.01 with ethanol as main contributor to the parameter of explosivity and flammability. The score associated with operating data was calculated in 209.30 and heat of reaction reported to be the most affecting parameter. The NuDIST score was estimated in 380.30. This NuDIST value revealed the low hazards associated with the handling of substances such as shrimp wastes, chitosan and water, as well as the non-extreme temperature and pressure conditions. In general, the large-scale production of chitosan from shrimp shells showed to be an inherently safe alternative of waste valorization.

The potential for transmission of the SARS-CoV-2 virus during minimally invasive gynaecological procedures, such as hysteroscopy or laparoscopy, on the reproductive tract of patients with COVID-19 is not known. We examined existing data for prevalence of virus in the reproductive tract and other bodily fluids.

The primary purpose of this investigation was to identify safety-oriented bicycling practices commonly used by adult riders in an urban setting (Brooklyn, New York), and to explore whether there are any differences between the safety-oriented practices of men and women riders. Methods: 24 adult riders (14 men, 10 women) in Brooklyn were interviewed concerning their perceptions of bicycling hazards and their safety-oriented practices. Interviews were recorded, transcribed, and analyzed through thematic analysis. Fisher’s Exact Test was employed to test for gender differences. Results: Participants identified a variety of hazards, mainly due to motor vehicles but also pedestrians and roadway conditions. The analysis distilled twenty-one bicycling practices to summarize prevalent views of the participants about safe riding practices. Related items were grouped under broader categories, generating seven safety-oriented bicycling strategies. Few differences based on gender were found in the analysis; however, women in this study were more likely than men to say that they felt disrespected by other road users. Conclusion: Seven strategies may be important for safe urban bicycling: minimizing exposure to other road users (especially motor vehicles) while riding, being vigilant and anticipating what others might do, riding in a predictable fashion, making one’s presence known to other road users, making sure it is safe before proceeding, obeying traffic rules, and riding at a safe speed. Future studies could develop these concepts further and test whether they are associated with involvement in traffic crashes.

One of the notable temporal properties of dynamical systems is that a set of initial states leads the solutions to reach desired states avoiding a predetermined unsafe set.This property, that we call safe reachability has been studied in literature for autonomous systems using Barrier functionand Barrier densities [1]. In this paper, we generalize a sufficient condition for safe reachability of autonomous systemto switched systems under arbitrary switching signals. The condition relies upon the existence of a common Barrier density function for each subsystem. We apply the condition using the sum of squares method together with Putinar Positivstellensatz.

In 2016–2017, we conducted and published a systematic review on caffeine safety [1] that set out to determine whether conclusions presented in the heavily cited Health Canada assessment, Nawrot et al. [2], remain supported by more recent data. To that end, we reviewed data from 380 studies published between June 2001 and June 2015, which were identified from an initial batch of over 5,000 articles through a stringent search and evaluation process [1]. In the current paper, we use plain language to summarize our process and findings, with the intent of sharing additional context for broader reach to the general public. We addressed whether caffeine doses previously determined not to be associated with adverse effects by Health Canada (400 mg/day for healthy adults, 300 mg/day for pregnant women, 2. 5 mg/kg body weight/day for adolescents and children, and 10 g/day for acute effects) remain appropriate for five outcome areas (acute toxicity, cardiovascular toxicity, bone & calcium effects, behavior, and development and reproduction) in healthy adults, pregnant women, adolescents, and children. We used a weight-of-evidence approach to draw conclusions for each of the five outcomes, as well as more specific endpoints within those outcomes, which considered study quality, consistency, level of adversity, and magnitude of response. In general, updated evidence confirms the levels of intake put forth by Nawrot et al., as not being associated with any adverse health effects, and our results support a shift in caffeine research from healthy to sensitive populations.

This paper explores various aspects of microbiology and immunology, focusing on the epidemiology and molecular characterisation of infectious diseases in the Caribbean and South America. Key areas of investigation include tuberculosis (TB), nosocomial infections, Salmonella infections, and bloodborne pathogens. A retrospective study in Jamaica underscores the high mortality rates in HIV-positive TB patients and the need for early HIV diagnosis and integrated care. Research in Trinidad and Tobago reveals the genetic diversity of Mycobacterium tuberculosis strains, highlighting high HIV-TB co-infection rates. The paper also examines the challenges posed by nosocomial infections, particularly those caused by antibiotic-resistant Gram-negative bacteria and methicillin-resistant Staphylococcus aureus (MRSA), and emphasises the importance of infection control. In addition, the study investigates Salmonella prevalence in poultry in Jamaica and the risks associated with foodborne infections. The paper further explores transfusion-transmitted infections and the development of IgM antibodies in egg whites for bacterial antigen detection. Lastly, it addresses immune responses in patients with immunodeficiency disorders, such as severe combined immunodeficiency (SCID) and chronic granulomatous disease (CGD), highlighting their susceptibility to a wide range of infections.

Background: The Second Victim Phenomenon (SVP) significantly impacts the well-being of healthcare professionals and patient safety. While SVP has been explored in various healthcare settings, there is limited data on its prevalence and associated factors among nurses in Austria. This study investigates the prevalence, symptomatology, and preferred support measures for SVP among Austrian nurses. Methods: A nationwide, cross-sectional, anonymous online survey was conducted using the SeViD questionnaire (Second Victims in German-speaking Countries), which includes the Big Five Inventory-10 (BFI-10). Statistical analyses included binary logistic regression and multiple linear regression using the bootstrapping, bias-corrected and accelerated (BCa) method based on 5000 bootstrap samples. Results: 928 participants responded to the questionnaire. Among the respondents, 81.58% (744/912) identified as Second Victims (SVs). The primary cause of becoming a SV was aggressive behavior from patients or relatives. Females reported a higher symptom load than males, and higher agreeableness was linked to increased symptom severity. Notably, 92.47% of SVs who sought help preferred support from colleagues, and the most pronounced desire among participants was to process the event for better understanding. Conclusions: The prevalence of SVP among Austrian nurses is alarmingly high, with aggressive behavior identified as a significant trigger. The findings emphasize the necessity for tailored support strategies, including peer support and systematic organizational interventions to mitigate the impact of SVP on nurses and to improve overall patient care. Further research should focus on developing and implementing effective prevention and intervention programs for healthcare professionals in similar settings.

The use of sensors in monitoring of lateral accelerations in delivery van transport focuses on measuring lateral accelerations on routes with roundabouts and curves to increase road safety. Using microelectromechanical system (MEMS) sensors, it measures the lateral accelerations acting on the vehicle and the load being transported during the test drives to study vehicle dynamics for cargo securing. Using an accelerometer and position tracking, accelerations can be detected when traversing curves and roundabouts at selected locations on the vehicle. The level of accelerations on the roof of the vehicle was found to be like those occurring on a lashed load with limited movements, where regression analysis can be used to determine the relationship between lateral accelerations at different sensor positions. If we compare the mean values of the lateral accelerations of the individual events between the sensors, the sensor on the side of the vehicle body at the height of the sensor on the load had approximately 5% lower mean values than the sensor on the roof. The sensor on the load measured approximately 5% higher mean values than the sensor on the roof. Hence, the mean lateral accelerations of the individual events for the sensor on the load are 10% higher than for the sensor at the same height on the vehicle body. From the above testing, we can say that the values of the mean lateral accelerations of the van from the sensor on the roof of the vehicle, are closer to the values of the accelerations of the sensor on the load than to the values of the sensor on the body of the vehicle at the same height.

Previous studies have shown that agricultural works bear a disproportionately higher burden of HIV, which is the highest HIV prevalence ever reported in any working population in South Africa. This study aimed to assess HIV knowledge, attitudes and practices of agricultural workers, as a precarious and vulnerable workforce. A cross-sectional study design was employed. A pre-piloted paper-based questionnaire was administered to a consenting sample of agricultural workers. Descriptive and inferential statistics were performed using stata version 16.1 software, 0.05 level was used as a measure of significance. The majority of agricultural workers had adequate knowledge level (72.1%) regarding HIV/AIDS. Knowledge was significantly associated with having some level of education,) secondary education (AOR: 1.46, 95% CI:1.01-2.12), and post-matric qualification (AOR: 3.07, 95% CI: 1.61-5.83). The attitudes level of workers towards HIV was good (88.1%). Attitude was negatively associated with residing in informal settlements (AOR: 0.64, 95% CI 0.43-0.97). Majority of participants exhibited poor prevention practices regarding HIV (60.9%). Half of participants reported low condom usage (50.9%) and multiple sexual partners (50.6%). The portrayed risky sexual behavior establishes agricultural workers as an HIV high-risk population. Strategizing non-conventional approaches to HIV prevention and behavior change communication targeting agricultural workers is recommended.

Indoor residual spraying (IRS) and use of insecticide treated bed nets for malaria vector control have contributed substantially to reduction in malaria disease burden. However, these control tools have important shortcomings including being donor-dependent, expensive, and often fail because of insufficient uptake. We assessed the safety and efficacy of a user-friendly, locally tailored malaria vector control approach dubbed ‟House Decoration for Malaria Control” (HD4MC) based on incorporation of a WHO-approved insecticide, pirimiphos-methyl CS (Actellic 300CS), into a customary hut decoration practice in rural Uganda where millions of the most vulnerable and malaria-prone populations live in mud-walled huts. Three hundred and sixty households were randomly assigned to either HD4MC (120 households), IRS (120 households) or control group without any wall treatment (120 households). Entomological indices were assessed using pyrethrum spray catch, CDC Light traps and human landing catches. Organophosphate (OP) toxicity on acetyl cholinesterase activity among applicators of HD4MC was evaluated using Test-mate (Model 400) erythrocyte acetylcholinesterase (AChE) test V.2 whereas toxicity in household occupants was monitored clinically. OP level in house dust was analyzed using reversed-phase high-performance liquid chromatography (RP-HPLC). Entomological indices were compared between the 3 study arms at 1.5, 3- and 6-months post intervention. HD4MC- and IRS-treated huts had significantly reduced malaria vector density and feeding rate compared to control huts. There was no significant reduction in acetylcholinesterase activity at 1.5- and 24-hours post exposure. Organophosphate exposure did not result in any serious adverse event among the household occupants. In conclusion, HD4MC was safe and had comparable efficacy to canonical IRS.

This work addresses the analysis and characterization of deadlocks in discrete-event systems modeled by labeled Petri nets (LPNs) with undistinguishable and unobservable transitions. To provide a solution for the notorious problem, it is essential to present an effective characterization such that deadlock control and synthesis are technically and methodologically possible. To this end, we introduce the notion of dangerous implicit vectors (DIVs) which implicitly threaten the system deadlock-freedom. The set of dead markings is divided into two subsets: dead basis markings (DBMs) and dangerous implicit markings (DIMs). An algorithm is designed to compute the sets of DIVs and DIMs at a given basis state of a system. Moreover, by virtue of linear algebraic equations, we formulate sufficient conditions to identify the existence of blocking markings in an LPN. Finally, an algorithm is developed to construct an observed graph that is a compact representation of the reachability graph of a Petri net with respect to the existence of dead reaches. At the end of this paper, experiment results that illustrate the correctness and effectiveness of the reported solution are presented.

The rising occurrence of chronic kidney disease represents a significant health concern, impacting over 10% of individuals. Magnetic Resonance Imaging (MRI) biomarkers identify pathological alterations, are non-invasive, and can help lessen biopsy requirements and complications in chronic kidney disease patients. Total volume is the most assessed metric in autosomal dominant polycystic kidney disease patients, assisting in tracking chronic kidney disease progression. Kidney segmentation is essential for evaluating renal volume. Nevertheless, it often relies on manual segmentation, which is time-consuming and highly subjective. However, Deep Learning (DL) techniques have led to creating algorithms that provide accurate, cost-effective, and user-independent outcomes. Therefore, this study explores DL-based strategies for kidney segmentation, particularly U-Net and Attention U-Net. Both architectures were assessed with the standard cross-entropy loss function and a focal cross-entropy loss function. Ultimately, the proposed methods were used for both 2-class and 3-class segmentation to improve the segmentation of border regions. The highest Dice coefficient achieved on the testing set was 0.966.

The growing emphasis on carbon considerations and the pursuit of net-zero emissions have brought about a paradigm shift in project management. To successfully facilitate the transition towards net-zero emissions, major projects must not only adapt existing systems but also embed carbon targets into their core strategies. While several studies have investigated carbon integration during the procurement phase, limited attention has been given to the construction project manager (PM) perspective. This study aims to bridge this research gap by exploring the challenges and barriers faced by construction PMs when integrating carbon targets and metrics into major Architectural, Engineering and Construction (AEC) projects, as well as evaluating the readiness of project teams to deliver on these. The study deployed a qualitative exploratory research design, where semi-structured interviews were conducted with 17 AEC project professionals actively engaged in the planning and execution of major projects in the UK. Thematic analysis of the data revealed a range of challenges and barriers faced by PM teams delivering these projects. The research findings contribute to the field of construction PM by enhancing the understanding of the challenges faced by project managers and project teams when integrating carbon requirements into the delivery of major projects. It uncovers a series of challenges and prevalent practices in major projects that have the potential to impede progress towards the net-zero transition.

Accurate detection of forest restoration sites using deep learning is crucial for effective ecosystem management, optimizing reforestation efforts, and ensuring environmental sustainability. While various deep learning-based systems have been developed for this purpose, the influence of different neural network architectures on model performance during the identification of forest restoration sites remains underexplored. This study aimed to address this gap by exploring the optimal methodology for extracting and classifying candidate sites for forest restoration based on Convolutional Neural Networks (CNNs), specialized for image recognition. Four categories—arable land, road and barren, quarry, and forest—were defined as candidate sites for forest restoration. A dataset comprising 17,043 samples was divided into training (11,929) and validation (5,114) sets at a ratio of 7:3 for model learning. Model accuracy was evaluated using pixel accuracy (PA) and mean intersection over union (mean IoU). The Resnet152-U-Net model demonstrated a pixel accuracy of 95.2% and a mean IoU of 61.3%, indicating excellent performance in extracting candidate sites for forest restoration. This approach offers spatial and temporal advantages over conventional field surveys or aerial image-based assessments, potentially serving as valuable data for selecting future forest restoration sites.

The energy efficiency of existing buildings is a crucial factor in addressing Europe’s energy con-sumption challenges, accounting for nearly 40% of the total energy usage. The ongoing energy crisis, driven by the reliance on fossil fuels and increasing prices, has underscored the urgency of adopting sustainable and energy efficient practices especially to the existing building stock. Cyprus is par-ticularly vulnerable due to its heavy dependence on imported fuels for electricity generation and faces significant challenges in transforming its existing building stock into energy-efficient and sustainable structures. Extensive focus has been made by the government on the energy-efficient retrofit of non-residential public buildings. This study examines an existing cultural hall. The case study of the Cultural Hall represents the building stock before 2010 which is lacking thermal in-sulation, double glazing and any energy efficient element. It then offers an ideal case study to ex-plore sustainable solutions which can be easily adopted from the construction industry and from the building owners. The primary objective of this research is twofold: first, to develop a comprehensive retrofit plan to transform the cultural hall into a net-zero energy building; and second, to explore how this transformation can give value back to the local community. After running two simulation scenarios and providing two energy performance certificates, one with at the existing state and one with the intended retrofit measures, it is clear that the building can be a net zero energy consump-tion building. Some interesting results derived, present also how this building can provide value back to the community i.e. through solar carports for EV charging.

Partial differential equations (PDEs) are used to describe a wide range of phenomena, such as heat transfer, fluid dynamics, and quantum mechanics. By solving PDEs, we can ob- tain insights into the behavior of the system and make predictions about its future evolution. Conventional numerical methods for obtaining the approximate solutions of PDEs may re- quire extensive computational resources and time, especially for complex PDEs and large- scale problems. The recently developed physics-informed neural network (PINN) has shown promise in a variety of scientific and engineering fields by incorporating physical laws into the loss functions of the neural network (NN). In addition, the training of PINN does not require ground truth data, but it has poor generalization ability to unseen domains. On the other hand, a convolutional neural network (CNN) has fast inference and better generalization ability, but it requires a large amount of training data. Taking the advantages of PINN and CNN by using Legendre multiwavelets (LMWs) as basis functions, we introduce a new method to approach the PDEs in this paper, namely Physics-Informed Legendre Multiwavelets CNN (PiLMWs-CNN), in order to continuously approximate a grid-based state representation that can be handled by a CNN. PiLMWs-CNN enable us to train our models using only physics-informed loss functions without any pre- computed training data, simultaneously providing fast and continuous solutions that gener- alize to previously unknown domains. In particular, the LMWs can simultaneously possess compact support, orthogonality, symmetry, high smoothness, and high approximation order. Compared to orthonormal polynomial (OP) bases, the approximation accuracy can be greatly increased and computation costs can be significantly reduced by using LMWs. We applied PiLMWs-CNN to approximate the damped wave equation, incompressible Navier-Stokes (N- S) equation, and two-dimensional heat conduction equation. The experimental results show that this method provides more accurate, efficient, and fast convergence with better stability when approximating the solution of PDEs.

The Colony-Forming Unit (CFU) counting problem remains a complex issue without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by numerous researchers. Among those, U-Net is the most frequently cited and popular Deep Learning method. The latter approach provides a segmentation output map and requires an additional counting procedure which accounts for unique segmented regions and detected microbial colonies. However, because of pixel-based targets it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, we propose a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. First of all, our unique innovation lies in the multi-loss U-Net reformulation. We introduce an additional loss term at the bottleneck U-Net layer, focusing on delivering an auxiliary signal indicating where to look for distinct CFUs. Second, our novel localization algorithm accurately incorporates an agar plate and its bezel into the CFU counting routines. Finally, our proposition is further enhanced by the integration of a fully automated solution. This comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application is capable of directly receiving images from the camera, which are subsequently processed, and the segmentation results are sent back to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the Deep Learning model. Through extensive experimentation, we have found that all probed multi-loss U-Net architectures incorporated in our hybrid approach consistently outperform their single-loss counterparts which utilize exclusively the combination of Tversky and Cross-Entropy training losses at the output U-Net layer. We report further significant improvements by the means of our novel localization algorithm. This reaffirms the effectiveness of our proposed hybrid solution in addressing contemporary challenges of the precise in-vitro CFU counting.

The automatic diagnosis of cardiovascular diseases has received much attention in the deep learning field. In this context, the segmentation of the left ventricle endocardium constitutes a major task in diagnosing heart conditions such as health failure and hypertrophic cardiomyopathy. The objective of this paper is to propose a "deep convolution network" for segmenting the internal cavity of the left ventricle (endocardium) using MRI images. In particular, we design an improved UNET model which handles additional inception modules for efficiently segmenting the internal cavity of the left ventricle. Our approach has been validated on the Sunnybrook Cardiac Data (SCD) dataset and has showed promising results in terms of precision. More specifically, the improved UNET largely outperforms the baseline UNET model and many existing state-of-the-art methods.

Using the World Bank Global Findex Database for 91 countries in 2014, 2017, and 2021, we examine whether fintech levels influence bank performance and whether fintech’s interaction with GDP per capita causes differential effects on bank performance globally. Since fintech levels were already very high for rich countries when the World Bank started providing fintech development statistics in 2014, we estimate AbFintech by regressing fintech levels on GDP per capita by year. AbFintech is the difference between the fintech level and its fitted values. Then, using multiple regression analyses, we investigate the impact of AbFintech on bank performance worldwide, focusing on the differential effects of AbFintech and GDP levels on bank performance. We find AbFintech significantly increases bank performance, primarily in less developed countries. Specifically, AbFintech increases banks’ ROA in the least developed countries and net interest margin in the 75th percentile countries. Also, AbFintech decreases the cost-to-income ratio in the 75th percentile countries, while it increases the ratio in the most developed countries. AbFintech does not affect the ratio of noninterest income to total income, regardless of the level of economic development.

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

In order to explore the grassland ecosystem productivity and landscape ecological patterns of main pastoral grasslands in China, it provides a theoretical basis for the efficient implementation of ecological engineering and rational management of grassland resources in the region. This study analyzed the changes in grassland area, landscape index (LSI), and net primary productivity (NPP) in seven major pastoral areas in China in 1985, 1995, 2005, and 2015. Results showed that (1) the sizes of the grassland study area in 1985, 1995, 2005, and 2015 were 248.34, 243.93, 245.80, and 244.660 km2 respectively. (2) The dominance of grassland in the landscape pattern increased from 2005 to 2015 as compared with that in 1985–1995 and 1995–2005. The degrees of spatial heterogeneity were reduced. (3) The grassland NPP showed spatial and temporal differences. The average NPP of grassland increased by 21.30%, 16.47%, and 36.17% during 1985–1995, 2005–2015, and 1985–2015, respectively. From 1995 to 2005, the average NPP decreased by 5.05 gC/m2, which is equivalent to -3.61% of the average NPP in 1995. The total amount of grassland NPP in the study area was the greatest in 2015, showing increments of 36.37% and 16.61% compared with those in 1985 and 2005, respectively.

Distracted driving is a major cause of road traffic crashes in Yaoundé. Partly due to scarcity of enforcement, lack of evidence and investigation of distraction safety performance indicator (SPI), hindering evidence-based intervention. This study aimed to address this evidence gap by evaluating the distraction SPI using proven methodology. Data on distracted driving (handheld mobile device; interaction; eating/smoking/drinking) were collected from roadside observation on 36 randomly selected road sections carefully spread to cover the city. SPI were computed and weighted with traffic volume to ensure representativeness of values. A total of 41,004 drivers were observed (38,248 in car; 1,116 in van; 977 in truck; 663 in bus). The prevalence of distracted driving in Yaoundé is 13.69% for the three distractions type combined. The prevalence is 7.84% for interaction, 4.89% for handled mobile device usage and 0.96% for eating/smoking/drinking. Leveraging these insights, a seven year (2024 - 2030) fighting strategy aiming at halving the prevalence was developed. The strategy contains intervention including on legislation/enforcement, which have been proven effective. This study, pioneer in Yaoundé, provide stakeholders with evidence of the issue and measures to implement and can also serve when developing road safety strategy.Future research should consider investigation at national level.

Transportation authorities aim to boost road safety by identifying risky locations and applying suitable safety measures. The Highway Safety Manual (HSM) is a vital resource for US transportation professionals, aiding in the creation of Safety Performance Functions (SPFs), which are predictive models for crashes. These models rely on Negative Binomial distribution-based regression and misinterpreting them due to unmet statistical assumptions can lead to erroneous conclusions, including inaccurately assessing crash rates or missing high-risk sites. The Florida Department of Transportation (FDOT) has introduced context classifications to HSM SPFs, complicating assumption violation identification. This study, part of an FDOT-sponsored project, investigates established statistical diagnostic tests to identify model violations and proposes a novel approach to determine optimal spatial regions for Empirical Bayes adjustment. This adjustment aligns HSM-SPFs with regression assumptions. The study employs a case study involving Florida roads. Results indicate that a 20-mile radius offers an optimal spatial sample size for modeling crashes of all injury levels, ensuring accurate assumptions. For severe injury crashes, which are less frequent and harder to predict, a 60-mile radius is suggested to fulfill statistical modeling assumptions. This methodology guides FDOT practitioners in assessing the conformity of HSM-SPFs with intended assumptions and determining appropriate region sizes.

Introduction Allogeneic stem cell transplantation (allo-SCT) has been used less frequently as a treatment modality in patients with MM. Allo-SCT may have the potential of long-term survival or even cure due to graft versus myeloma effect, however, its use continues to remain controversial given the toxicity profile related to infectious complications and graft versus host disease (GVHD). Concern for exacerbation of GVHD has been raised following re-infusion of CAR-T cells in patients with prior Allo-SCT. Ciltacabtagene autoleucel (Cilta-cel) and Idecabtagene vicleucel (Ide-cel) are two engineered CAR-T products targeting specific B-cell receptors that are approved by the FDA for the treatment of relapsed/refractory (R/R) MM. A recent retrospective study conducted by Htut et al, using data from CARTITUDE-1 trial, showed favorable efficacy and safety in patients who received Cilta-Cel with prior allo-SCT. We report our experience for patients with prior Allo-SCT who underwent therapy with CAR-T in the real world setting. Objective To evaluate the safety and efficacy of CAR-T therapy in patients with R/R MM with a history of allo-SCT. Methods We conducted a retrospective chart review of adult patients (age 18-70 years old) with R/R MM who had received CAR-T on an institutional IRB approved protocol. We collected data for the eligible patients from our institution's database. Safety outcomes included the incidence and severity of adverse events (AEs). AEs were graded using National Cancer Institute Common Terminology Criteria for Adverse Events version 5.0 (NCI-CTCAE 5.0). Cytokine release syndrome (CRS) and immune effector associated neurotoxicity syndrome (ICANS) were graded as per the American Society for Transplantation and Cellular Therapy criteria. Efficacy outcomes included overall response rates (ORR), progression free survival (PFS). Survival analysis was performed using the Kaplan-Meir method including PFS. PFS was defined as time from initiation of treatment to progression of disease or death whichever occurred first. The IMW response criteria were utilized for response assessment. Results 8 out of 56 patients who received CAR-T (Cilta-cel or Ide-cel) had a prior allo-SCT. Pts with prior allo-SCT had received a median of 7 prior lines of therapy(LOT) compared with 5 prior LOT for pts who did not(non-Allo)(p=0.04). Median time of CAR-T infusion was 98.8 months following allo-SCT. 7 of 8 (85.7%) pts experienced CRS (any grade) in Allo-SCT vs. 37 of 48 pts (77.1%) in non-Allo (p=0.48); 1 pt with prior allo-SCT experienced HLH requiring intervention with anakinra. At a median follow-up of 4.8 months, 7 of 8 patients had a response (ORR= 87.5%) in allo-SCT vs. 75%(36 of 48) in non-Allo (p=0.4), mPFS was not reached in allo-SCT vs. 11.9-months in non-Allo (p=0.5), although f/u for our cohort was short. No TRM was observed in either group. None of the patients with prior allo-SCT experienced acute GVHD following CAR-T therapy. Baseline characteristics, safety and efficacy data are summarized in Table 1. Conclusion Our findings support the conclusion that prior Allo-SCT does not impact the safety and efficacy of CAR-T therapy in patients with R/R MM. More clinical studies with a higher number of subjects and longer duration of follow-up are warranted to further evaluate the impact of Allo-SCT on CAR-T therapy's safety and clinical outcome in patients with MM.

The exposure of employees to specific risks is usually due to increasing levels of work-related stress, which negatively affects their skills and effectiveness in their job duties, lead-ing to the onset of physical/mental illnesses, mistakes, or even accidents. The aim of this study is to investigate the psychosocial risks experienced by nurses in tertiary hospitals in Greece and their association with their attitudes towards safety and the occurrence of medication errors. The research was conducted in four tertiary hospitals in Greece, with the participation of 514 nurses of all educational levels. Questionnaires were used to collect data on work-related stress, professional burnout, and medication errors. The results highlighted that psychosocial risks, such as high work pressure and lack of support, are directly linked to professional burnout and an increased likelihood of medication errors. The necessity of social support, job autonomy, and work-life balance are underscored as critical factors for reducing stress and improving the quality of care. Specific strategies are proposed to enhance the mental health of nurses and improve working conditions.

Canine influenza virus (CIV) H3N2 causes a highly contagious respiratory disease in dogs and has been the source of outbreaks across North America since 2015. An injectable RNA Particle (RP)-CIV H3N2 vaccine has been developed to protect dogs against this disease. To demonstrate efficacy, dogs were randomized into two treatment groups, then vaccinated subcutaneously twice, 21 days apart, with a placebo vaccine (n = 20) or a RP-CIV H3N2 vaccine (n = 20). Three weeks later, dogs were challenged intranasally with virulent CIV H3N2 and observed daily for 10 days for clinical signs of disease. Nasal swabs were also collected daily to evaluate shedding of the challenge virus. Ten days post-challenge, dogs were euthanized, and the lungs were examined for consolidation. RP-CIV H3N2 vaccination demonstrated a significant reduction in duration of clinical signs, duration and amount of virus shed, lung consolidation, and the incidence of suppurative pneumonia. To evaluate safety, dogs from multiple geographic regions were vaccinated subcutaneously, 3-4 weeks apart, with a RP-CIV H3N2 vaccine and observed for adverse events for 14 days after each administration. The RP-CIV H3N2 vaccine was deemed safe, with lethargy being the most reported adverse event at a rate of 1.6%.

The use of fuel material and different sources of ignition causes unwanted fires, producing and spreading in different spaces. Fire fighting. systems are measures taken to minimize the effects of fire. This struggle is not only on land, but also in the maritime world, through maritime security that has become a very important element for the International Maritime Organization (IMO). Therefore, maritime accidents caused by fire are one of the challenges to be taken into account, because they require immediate control and response. This article presents a historical interpretation of the concept of maritime safety through first responders on ships. It will be demonstrate how the technological advances of such equipment have been very important for their arrival on ships. In addition, the article reveals the legislative changes of International Convention for the Safety of Life at Sea (SOLAS), which reflects the IMO requirements to prevent fire risks on board, requiring a reading of the primary documents. Finally, the importance of the evolution of first intervention equipment for safety on board has been shown.

Berberine (BER) is a natural alkaloid found in various Berberis species, used traditionally in herbal medicine and dietary supplements. BER exhibited various biological activities, including anti-inflammatory, antioxidant, antidiabetic, and anti-tumour properties by modulating cellular pathways and influencing gene expression. While BER shows promise in various health applications, its possible toxicity raises concerns. In 2023, EFSA issued a call for data on BER- and protoberberine (PROTBER)-containing products, to comprehensively assess its safety and potential health claims. With these premises, new data was collected by assessing: i) the phytochemical profile of BER- and PROTBER-containing preparations by newly developed HPLC-DAD method; ii) in silico and in vitro investigation of pharmacokinetics properties of BER; iii) in vitro cytotoxicity in different human cell lines and, iv) biological and molecular activities of extracts containing BER and PROTBER in in vitro human non-cancer cells. By testing 16 commercial Berberis extracts, the evidence collected showed a good qualitative reliability in terms of phytochemical profile and limited relapses on cytotoxicity even at 200 µg/ml after a 24h of exposure, while evidencing a good cellular bioavailability and cell migration inhibition. The potential for plant-drug interactions affecting CYP450 proved to be minimal. Overall, our work provides a useful overview to better elucidate potential concerns on botanicals containing BER and minor PROTBER commonly used in food supplement.

Drowning is a leading cause of accidental death worldwide, with traditional drowning prevention strategies focused on buoyancy aids and protective equipment proving insufficient in mitigating the immediate risks of cold water shock and drowning. Swiftwater Breathing Apparatus (SWBA) has emerged as a potential game-changer, offering respiratory protection at the water's surface and a means of interrupting the drowning process. This report examines how SWBA can disrupt the drowning process, the benefits of using SWBA compared to existing protective equipment, the issue of water rescuer fatalities, and the potential implications for insurers and coronial inquests.

The primary objective of this experiment is to enhance road safety and mitigate the occurrence of accidents by leveraging Low Power Wide Area Networks (LPWAN), specifically focusing on the Long Range (LoRa) network. Through comprehensive research into LPWANs and their various applications, we have designed and implemented a novel approach employing two mobile robots. These robots are utilized to emulate a typical traffic scenario, with one robot leading the way, skillfully maneuvering around obstacles, while the other robot closely follows behind, tracing the same path. The leading robot is tasked with collecting data from its internal environment, the external surroundings, and its operator through an array of sensors. Subsequently, this data is transmitted to the trailing robot. The follower robot's role is to analyze the data received and formulate decisions based on this information. Its objective is to mimic safe driving behavior, particularly when the received data deviates from the expected norms. In such instances, the follower robot will opt to decelerate significantly, thereby minimizing the risk of collision. By implementing this innovative system, we aim to significantly enhance driving safety and reduce the occurrence of accidents on the road.

Background: In past years recreational diving has attracted more and more interest and among such divers an increasing number of diabetic divers. However, there is limited research on the accuracy of CGM systems under hyperbaric conditions and consistently diving guidelines make little reference to the use of CGM. Subject and Method: This exploratory study collected blood glucose samples of one diabetic diver over four dives and assessed the deviation of intersitial glucose values from two CGM systems based on 33 samples. A notable step of this method is to collect the data from actual dives under water. Results: The results indicate that a) under hyperbaric conditions of up to 2bar additional pressure and b) after decompression both CGM systems work comparably accurate as indictated by the manufacturer under normal conditions. This has been checked in terms of MARD and Clarke Error Grid Distirbution. Conculsion: For diabetic divers the results suggest they may take CGM monitors with them on dives to react to potential hypoglycemic events before they occur, i.e. when it is still safe to supplement glucose. For dive guidelines the results imply that these could reference CGM for dive planning and monitoring during the dive to increase divers‘ safety. For research this exploratory confirms the current hypothesis that neither increased pressure nor saltwater have an effect on blood glucose and the functioning of CGM systems. Further research may include additional sampling (number and depth) and more elaborate blood glucose measurement. The experimental setup applied is an efficient way to test further CGM systems.

In the context of an increasingly globalized economy and rapid technological advancements, the agri-food sector faces unprecedented challenges and opportunities. Digital traceability systems have emerged as pivotal in enhancing operational efficiencies, ensuring food safety, and fostering supply chain transparency. This study conducts a comparative analysis of the adoption and impact of digital traceability technologies across member countries of the Organisation for Economic Co-operation and Development (OECD). Utilizing a multidimensional analytical framework, this research investigates the spectrum of national regulations, legal frameworks, and specific food commodities most influenced by digital traceability implementations. It systematically evaluates the efficacy of these systems in addressing consumer transparency demands, regulatory compliance, and the overarching goal of sustainable agri-food supply chains. Through a meticulous examination of case studies and empirical data, the paper elucidates the dynamic interplay between technological innovation and regulatory environments, offering insights into best practices and potential barriers to digital traceability integration. This comprehensive inquiry aims to contribute to the scholarly discourse on digital traceability, providing actionable recommendations for policymakers, industry stakeholders, and academia to navigate the complexities of modern agri-food systems.

Dietzia natronolimnaea C79793-74 has emerged as a potential probiotic strain with implications for managing Crohn's disease. This study evaluates the safety profile of D. natronolimnaea C79793-74 as a probiotic sup-plement. Genotypic characterization involved a 16S rRNA gene and genomic sequencing and genome an-notation. The safety assessment included interrogation of the assembled genome for antibiotic resistance genes and virulence factors, utilizing the CARD and VFDB databases. Notably, the analysis revealed an ab-sence of antibiotic resistance or virulence factors in Dietzia natronolimnaea C79793-74. The safety and tol-erability of D. natronolimnaea C79793-74 were further investigated in an 8-week double-blind, place-bo-controlled clinical trial involving healthy adult participants. A daily dose of 5 x 109 CFU of the probiotic strain was administered. This clinical trial represents the first assessment of the safety of D. natronolimnaea C79793-74 in human subjects. Results demonstrated that participants in both the Dietzia and Placebo groups maintained clinical and hematologic markers within the normal range throughout the study. More-over, the probiotic strain was well-tolerated, with nearly all participants experiencing no severe or medium adverse events. Collectively, the comprehensive data obtained in this study support the inference that Dietzia natronolimnaea C79793-74 is safe and well-tolerated as a nutritional supplement for human con-sumption

Using a safe subset of C++ is a promising direction for increasing the safety of the programming language while maintaining its performance and productivity. In this paper, we examine how close existing C/C++ code is to conforming to a safe subset of C++. We examine the rules presented in existing safe C++ standards and safe C++ subsets. We analyze the code characteristics of 5.8 million code samples from the Exebench benchmark suite and 5 modern C++ applications using a static analysis tool. We find that raw pointers, unsafe casts, and unsafe library functions are used in both C++ code at large and modern C++ applications. In general, C++ code at large does not differ much from modern C++ code, and continued work will be required to transition from existing C/C++ code to a safe subset of C++.

Background and aim We investigated the clinical outcomes of patients with hepatocellular carcinoma (HCC) who underwent next-generation microwave thermosphere ablation (MTA). Methods A total of 429 patients with 607 HCCs (maximum tumor diameter ≤40 mm) were included. Results The primary etiologies of HCC were hepatitis-related: 259 (60.4%) cases of HCV, 31 (7.3%) cases of HBV, and two instances of both. Median maximum tumor diameter was 15.0 (interquartile range, 10.0–21.0) mm. There were 86 tumors in areas of the liver where MTA is difficult. The most common area was near the primary and secondary branches of the intrahepatic portal vein (26 nodules). The cumulative local tumor recurrence rates at 1, 2, and 3 years were 4.4%, 8.0%, and 8.5%, respectively. The cumulative local tumor recurrence rate differed significantly by tumor size group: ≤20 mm group (n=483), 20–30 mm group (n=107), and ≥30 mm group (n=17) (p<0.001). The cumulative local tumor recurrence rate was similar by difficult-to-treat status (p=0.169). In the multivariable analysis, tumor size (per 1 mm) (hazard ratio [HR], 1.07; 95% confidence interval [CI], 1.03–1.11; p<0.001) and ablative margin (per 1 mm) (HR, 0.81; 95% CI, 0.70–0.92; p=0.002) were significantly associated with local tumor recurrence. Only tumor size (per 1 mm) (odds ratio, 1.08; 95% CI, 1.02–1.15; p=0.015) was significantly associated with complications. Conclusions MTA is a safe and effective local ablation therapy for HCC, even for tumors located in areas of the liver where local ablation therapy is difficult.

As a prominent organizational issue there was limited evidence in the literature about the relationship between organizational strategy, workplace health, safety, and wellbeing, and performance measurement that demonstrate a measurable impact on organizational performance. Whilst the literature reports numerous studies into the relationship between employee engagement and performance, they hardly incorporate workplace health, safety, and well-being within this relationship. This mixed-methods study investigated the relationship between organizational strategy, workplace health, safety, and wellbeing strategy, employee engagement, and strategy efficacy from a corporate perspective. Findings from Phase two, the online survey, revealed a seven-factor HSW Strategy framework. The workplace health, safety, and wellbeing strategy framework provide empirical evidence towards a suitable and valuable framework for high-risk businesses to improve individual and organizational performance.

Podiatric surgeons routinely use electrosurgical devices to cut and coagulate tissue during surgical procedures. Although advances in technology have made electrosurgery increasingly safer for patients and personnel, its use is still poorly understood by those using it and the hazards associated with its use still exist presently. Human error, direct or indirect transfer of electricity to a conductive device, or device malfunction can cause serious adverse events, including burns, electrical shocks, and or fires. Here, we report a rare case of a 43-year-old man who suffered severe burns during hallux valgus surgery. The surgeon and the nursing staff did not notice any injuries during the surgical intervention. This case highlights the need to implement tools to help health professionals improve patient safety during surgery. Based on the case herein reported, a specific checklist was implemented to prevent adverse events related to electrosurgery in our podiatric unit to reduce the risk of electrosurgical complications.

Background: Wastewater represents a health risk for populations. For sanitary safety, it is important to know their physico-chemical and microbiological qualities in order to better understand the risks incurred by the population. Objective: The aim of this study is to analyse the microbiological and physico-chemical quality of domestic wastewater in Adétikopé. Methodology: 12 wastewater samples were collected in Adétikopé from December 2022 to February 2023. Physico-chemical parameters were determined in accordance with AFNOR T90-105/ T90-023 and French standard NF T 90101. In microbiology, AFNOR was used as a reference for the detection of germs contaminating wastewater. Results were assessed in relation to European Union Criteria 98/83/EC. Results: The wastewater analyzed had a pH close to neutral, with an average pH of 7.05. The pollutant load generated ranged from 32 mg/l to 201 mg/l for BOD5, from 660 mg/l to 900 mg/l for COD and from 408 mg/l to 2080 mg/l for suspended solids. Concentrations of fecal coliforms and fecal streptococci are very high, far exceeding WHO guidelines for population health safety.

Korean manufacturers have developed a new varicella vaccine, NBP608. This phase 3, randomized, double-blind, multicenter study aimed to compare the immunogenicity and safety of NBP608 among healthy children to those of VarivaxTM (control). Children aged 12 months to 12 years were randomized 1:1 to receive either NBP608 or control vaccine. Serum samples were obtained before vaccination and within six to eight weeks after vaccination. A total of 499 participants (NBP608, n = 251; control, n = 248) was enrolled. The seroconversion rate (SCR) measured using FAMA assay was 99.53% in the NBP608 group, and the lower limit of the 95% confidence interval (95% LCL) for SCR difference (NBP608 minus control) was 0.52%. This 95% LCL for the difference was higher than the specified non-inferiority margin of –15%. In assessment by gpELISA, the SCR was 99.53% in the NBP608 group; and the 95% LCL for SCR difference was 6.5%, which was higher than the specified non-inferiority margin of –15%. There were no significant differences between the NBP608 and control group with respect to proportions of participants who demonstrated local and systemic solicited AEs. This study indicated that NBP608 had a clinically acceptable safety profile and was not immunologically inferior to VarivaxTM.

Salmonella is the second most frequent bacterial pathogen involved in human gastrointestinal outbreaks in the European Union; it can enter the food production chain from animal or environmental sources or from asymptomatic food operators. European food legislation has established microbiological criteria to ensure consumer protection. Salmonella is listed under both process hygiene criteria and food safety criteria. Each Member State designates an agency to organize or perform controls and other official activities. This paper describes the official control plans performed by competent authorities in northern Italy in the three-year period 2019-2021. A total of 4413 food samples were delivered to the IZS Food Safety laboratories for Salmonella detection and 36 (0.8 %) tested positive. Salmonella was most frequently detected in poultry meat samples (25/36 positive samples) followed by other meat products and pork products. The official controls for the protection of consumer health apply the EU’s from farm-to-fork approach: the samples were collected during production (food production plants) and on the market and from collective catering (restaurants, cafeterias, canteens). This manuscript will provide information about the presence of Sal-monella in foodstuffs and can help competent authorities to set control plans based on a risk assessment.

This review aims to describe an edible packaging system in food production. The growing global population, changes in climate and dietary patterns, as well as increasing need for environmental protection have created an increasing demand for waste-free food production. The need for durable and sustainable packaging materials has become significant to avoid food waste and environmental pollution. Edible packaging has emerged as a promising solution also to extend the shelf life of food products and reduce dependence on petroleum-based resources. In this review, the importance, composition, and functions of edible packaging materials, as well as their production methods, were described. The market value of edible packaging materials is expanding. It needs further research and development of edible packaging materials to increase sustainable, eco-friendly packaging practices significant for environmental protection and food safety.

Due to nutritional benefits and perceived humane ways of treating the animals, the demand for antibiotic-free pastured poultry chicken has continued to be on a steady rise. However, despite non-usage of antibiotics in pastured poultry broiler production, antibiotic resistance (AR) is reported in zoonotic poultry pathogens. However, actors that drive multidrug resistance (MDR) in pastured poultry are not known. In this study, we used machine learning and deep learning approaches to predict farm management practices, and physicochemical properties of feces and soil that drive MDR in zoonotic poultry pathogens. Antibiotic use in agroecosystems is known to contribute to resistance. Evaluation of the development of resistance in environments that are free of antibiotics such as the all-natural antibiotic-free, pastured poultry production systems described here is critical to understand the background AR. We analyzed 1,635 preharvest (feces and soil) samples collected from forty-two pastured poultry flocks and eleven farms in the Southeastern United States. CDC National Antimicrobial Resistance Monitoring System guidelines were used to determine antimicrobial/multidrug resistance profiles of Salmonella, Listeria and Campylobacter. A combination of two traditional machine learning (RandomForest and XGBoost) and three deep learning (Multi-layer Perceptron, Generative Adversarial Network, and Auto-Encoder) approaches, identified critical farm/environmental variables that drive multidrug resistance in poultry pathogens, in broiler production systems that represents background resistance. This study enumerates management practices that contribute to AR and recommendations to potentially mitigate multidrug resistance and prevalence of Salmonella and Listeria in pastured poultry.