Background The metabolic syndrome increases the risk of developing various systemic cancers. The prevalence of the metabolic syndrome in newly diagnosed glioblastoma patients is unknown. Further, there have been contradictory reports about how the metabolic syndrome affects clinical outcomes. Therefore, the purpose of this study is to test the hypothesis that the metabolic syndrome is associ-ated with increased prevalence of glioblastoma and worsened survival outcomes. Methods This retrospective cohort study examines seventy-three patients with isocitrate dehydrogenase (IDH)-wild type glioblastoma as it provides for a relatively homogeneous population to examine. Patient characteristics, vital signs, lab results, tumor molecular markers, and overall survival were analyzed. Patients with the metabolic syndrome and individual risk factors were identified and survival outcomes were examined. Results Our results demonstrate that there is a higher prevalence of the metabolic syndrome in our cohort of patients with glioblastoma than the general population (41% vs 33%). We also demonstrate that after correction for confounding variables, the metabolic syndrome is not significantly asso-ciated with overall survival (p = 0.1). When analyzing individual metabolic risk factors, we demonstrate that there is a significant association between the accumulation of metabolic risk factors and decreased survival (p = 0.03), and hyperglycemia emerges as a significant independent risk factor for decreased survival (p = 0.05). Conclusion These results suggest that metabolic risk factors can affect survival in patients with glioblastoma, which can have significant implications for clinical practice. These findings need to be further ex-plored through further clinical and mechanistic studies.

Structural diversity is a key feature of forest ecosystems that influences ecosystem functions from local to macroscales. The ability to measure structural diversity in forests with varying ecological composition and management history can improve the understanding of linkages between forest structure and ecosystem functioning. Terrestrial LiDAR has often been used to provide a detailed characterization of structural diversity at local scales, but it is largely unknown whether these same structural features are detectable using aerial LiDAR data that are available across larger spatial scales. We used univariate and multivariate analyses to quantify cross-compatibility of structural diversity metrics from terrestrial versus aerial LiDAR in seven National Ecological Observatory Network sites across the eastern USA. We found strong univariate agreement between terrestrial and aerial LiDAR metrics of canopy height, openness, internal heterogeneity, and leaf area, but found marginal agreement between metrics that describe heterogeneity of the outer most layer of the canopy. Terrestrial and aerial LiDAR both demonstrated the ability to distinguish forest sites from structural diversity metrics in multivariate space, but terrestrial LiDAR was able to resolve finer-scale detail within sites. Our findings indicate that aerial LiDAR can be of use in quantifying broad-scale variation in structural diversity across macroscales.

Background: The present study analyzes the evaluation of communication skills by standardized patients (SPs) and medical evaluators (Es) in an OSCE setting. Methods: The OSCE involved 189 sixth-year medical students, as well as 34 SPs and 63 Es. Communications skills were evaluated in 8 stations, simultaneously by SPs and Es. The SPs were actors who had been trained in the clinical case and who acted in accordance with a standardized script in a simulated clinical situation. The evaluators, also standardized, were Resident Doctors or staff Doctors from the Hospital Services involved. Results: The global scores awarded to students for communication skills were very similar in both groups, although the score awarded by Es was significantly higher, and a direct relationship was also observed between the mean scores awarded by both groups. Evaluators awarded significantly higher scores than SPs in 7 out of the 10 items on the checklist. Female medical students also scored significantly higher than their male counterparts in many items, including external appearance, listening, cordiality, optimism, interest, expression and empathy. Conclusions: Our data indicate that SPs and Es evaluated communication skills in a similar manner in an OSCE setting, a finding which suggests that health-related professionals can be used as an alternative to SPs, thus helping to lower economic costs. Our study also confirms a gender difference (in favor of women) in the evaluation of communications skills by both groups.Our data indicate that SPs and Es evaluated communication skills in a similar manner in an OSCE setting, a finding which suggests that health-related professionals can be used as an alternative to SPs, thus helping to lower economic costs. Our study also confirms a gender difference (in favor of women) in the evaluation of communications skills by both groups.

This paper presents the results of quantitative research regarding the examination of the levels of efficiency of the fire protection system and the training of employees in Electrical power distribution of Serbia for fire protection, ie the analysis of the manner of implementation of fire protection measures, and determining whether these measures are adequately applied. Following the subject set in this way, the aim of the research was set, and that is the scientific analysis of the manner of application of fire protection measures. The results of this research showed that the employees of Electro distribution Belgrade perceive the preparedness of their company highly positively and certain socio-demographic characteristics also affect certain attitudes, especially those related to the experience with fire, the knowledge of employees to react in case of fire, and the choice of the appropriate response, assessment of the readiness of the company and the employees themselves. It was found that knowledge of how to react in case of fire is influenced by gender and level of education; the choice of one of the methods of action in case of fire is influenced by gender; the assessment of the readiness of the employees themselves is influenced by the marital status; the desire of employees to participate in training to increase their preparedness for fires is influenced by gender. The results of the research can be used to improve the preparedness of employees in various organizations to respond to disasters caused by fires.

Exploring new approaches to innovating higher vocational education is a worldwide pursuit driven by the increasing demand for graduates equipped with competencies. This study investigated how Outcomes-Based Education (OBE) influences the attainment of graduate competence (AGC) in China’s higher vocational education, using the theory of Constructive Alignment (CA) as its foundation. The OBE framework incorporates intended learning outcomes (ILOs), project-based learning (PjBL), assessment strategies (ASs), and AGC. The study tested the model by assessing the impact of ILOs, PjBL, and ASs on AGC, respectively and sequentially. Surveys were administered to 320 CBEC learners in April 2024, resulting in 301 usable responses. Data were analyzed using structural equation modeling (SEM) with SPSS 23.0 and AMOS 24.0. Results indicated that PjBL and ASs were directly and positively related to graduate competency attainment, while ILOs were indirectly associated with graduate competence through the mediating roles of PjBL and ASs. The most substantial mediating effect between ILOs and graduate competence was ASs, followed by PjBL, and then, the chain mediating effect of PjBL and ASs. These findings enhance the theoretical understanding of OBE and provide detailed procedures for tracking and enhancing sustainability in higher vocational education. Beneficiaries include educators, policymakers, and accreditation bodies.

A hallmark of eutherian pregnancy is its dependency on elevated progesterone levels for its entire length. This fact substantially affected the evolution of pregnancy and even the life history of eutherians. Progesterone synthesis by the ovarian corpus luteum, however, long predates all origins of vertebrate viviparity, which requires reconsidering the role of the corpus luteum and progesterone before and beyond placental mammals. Pregnancy originated repeatedly by co-opting the ancestral luteal phase of the ovarian cycle. Therefore, it represents a physiological equivalent and “serial homolog” of the nonpregnant luteal phase. Because of this relationship, we argue that understanding the origin and evolution of pregnancy can substantially benefit from examining the evolution and regulation of the corpus luteum during nonpregnancy. In the first part of this paper, we investigate what is known about the evolution of the corpus luteum and its changing regulation and function across vertebrates, covering both pregnant and nonpregnant cycles wherever possible. Studying pregnancy in the context of the nonpregnant cycle reveals the key evolutionary innovation that facilitated the characteristically long eutherian pregnancies: the regulatory decoupling of the pregnant and nonpregnant cycle, that is, the evolution of maternal recognition of pregnancy. Only after the two cycles became separately modifiable, could gestation length increase without deleteriously extending the time to the next fertile phase. In this review, we argue that, assuming selection for body size increase, the origin of MRP led to a punctuated increase of gestation length, which could imply that we overestimate pregnancy length in the eutherian ancestor when using conventional phylogenetic inference methods. Mechanistically, decoupling between cycles means that the progesterone-dominated phase in pregnancy is extended beyond that of nonpregnancy. In eutherians, this is achieved by extending the lifespan of progesterone-producing corpus luteum, providing additional corpora lutea, extraovarian sources of progesterone, or a combination of these. Alternatively, it can also be accomplished by shortening the nonpregnant cycle. The differences in underlying mechanisms for decoupling among the major eutherian lineages suggest that they originated independently, which leads us to conclude that eutherian gestation consists of parts that are not homologous across eutherians. Consequently, eutherian gestation length is not a homologous trait that can be productively compared across species directly. Finally, we find no evidence for an increase in gestational length due to maternal-fetal conflict. Rather, as long appreciated, body size explains a major portion of the variation in gestation length in eutherians. This suggests that the increase in eutherian gestation length was driven by the need to accommodate an increasing body size, a well-documented trend (Cope’s rule), once independent evolution of pregnant and non-pregnant cycles became possible.

This paper discusses how Marsascala, a coastal town in Malta, has been urbanized over the years, with particular attention to the development of the tourist industry. In this regard, Bruce Young’s (1983) “general model of the process of ‘touristization’ and landscape change” is engaged with in conjunction with other conceptualisations of tourism and urban development. This paper contributes to the following sustainable development goals respectively, 3 (Good Health and Wellbeing); 8 (Decent Work and Economic Growth); 11 (Sustainable Cities and Communities); 14 (Life Below Water); and 15 (Life on Land), in particular with respect to quality of life, urbanization and respective social, environmental, and economic impacts, and through respective recommendations for a sustainable tourism in Marsascala and similar localities.

Background: We explored the potential value of serum MUC5AC (sMUC5AC) as a biomarker to guide perioperative management of early-stage pancreatic ductal adenocarcinoma (PDA) patients receiving neoadjuvant therapy (NAT). Methods: We performed enzyme-linked immunoassays using a human MUC5AC kit (NBP2-76703) in serum samples obtained from The Ohio State University biorepository (between January 2010 and June 2021). Univariate (UV) and multivariate (MV) Cox regression models were used to quantify progression-free survival (PFS); clinical and pathological variables were adjusted in the MV models. UV logistic regression analysis was utilized to examine the association of sMUC5AC with pathological features and survival. 2 Results: Overall, 23 samples (19 FOLFIRINOX, 3 gemcitabine/nab-paclitaxel, 1 FOLFOX) were available for analysis. The median age was 66 years, and 52% were females. sMUC5AC was associated with a) treatment response, margin status, and residual disease (R0 vs. R1/R2) (all P<0.05); b) PFS on both UV (hazard ratio (HR) of 1.4, 95% confidence interval (CI) of 1.07 to 1.82, P=0.01) and MV (HR of 49.2, 95% CI, 4.4 to 1008.3, P=0.002) analyses; and c) PFS in pre-surgery models along with carbohydrate antigen 19-9 (CA19-9) measured on the same day alone (HR of 1.47, 95% CI, 1.06 to 1.93, p=0.04), with CA19-9 on the same day, and NAT regimen (HR of 1.44, 95% CI, 1.12 to 1.93, p=0.01), and with CA19-9 on the same day, NAT regimen, and CA19-9 at diagnosis (HR of 1.52, 95% CI, 1.1 to 2.09, p=0.007). Conclusion: sMUC5AC shows promise to helping to predict outcomes in our preliminary study. Larger prospective studies should validate these findings.

The impact of financial risks extends beyond the normal operation and survival of industrial and commercial enterprises and financial institutions. They also have the potential to impede the stable development of a country's and even the world's financial economy. This is clearly demonstrated by the severe consequences of the frequent financial crises that have occurred in recent years. It thus follows that the prevention of financial risks has become one of the core tasks of the operation and management of industrial and commercial enterprises and financial institutions. Portfolio optimization techniques are employed in the construction of a variety of asset allocation models, with reinforcement learning algorithms used to dynamically adjust investment strategies with the objective of maximizing returns and minimizing risks. The preliminary construction of an asset allocation model is achieved through the utilization of a genetic algorithm. Genetic algorithms emulate the processes of natural selection and genetic variation to identify the optimal portfolio of assets that will yield the greatest returns at a pre-established level of risk. Subsequently, the Deep Q-Learning algorithm is introduced to facilitate dynamic adjustments and optimization of the asset allocation, based on the initial construction. Deep Q-learning employs deep neural networks to forecast the prospective returns of disparate investment strategies, thereby optimizing the decision-making process through continuous learning and updating. The combination of genetic algorithms and deep Q-learning enables the system to identify the optimal investment strategy under a diverse range of initial conditions and to adapt it in real-time to respond to market fluctuations and uncertainties. The experimental analysis demonstrates that the proposed method exhibits an exemplary capacity for risk control and a robust ability to generate stable income growth across diverse market environments. In simulation experiments, portfolios constructed using this method demonstrate lower volatility and higher average returns than those generated through traditional methods.

With the rapid development of Intelligent Transportation Systems (ITS), vehicle type classification, as a key link in Automatic Toll Collection systems (ATC), is of great significance in improving traffic efficiency and reducing economic losses. This study proposes an intelligent vehicle classification system based on deep learning and multi-sensor data fusion to address the accuracy issues existing in vehicle classification methods based on optical sensors (OS) and human observers. The system significantly improves the accuracy and robustness of vehicle classification by combining deep Convolutional Neural Networks (CNN), LiDAR sensors, and machine learning algorithms. We first constructed a large-scale annotated dataset containing multiple vehicle types and complex traffic scenes to improve our model's capability to identify different vehicle characteristics. Next, CNN models based on different architectures were designed to extract global and local features of the vehicle, respectively. In addition, the LiDAR sensor was used to achieve the spatial structure architectures of the vehicle and combined with the output of the CNN model to improve classification performance under occlusion and complex scenes.

The paper proposes a traceability framework based on methodological approach for the deployment of IOTA-Based Distributed Ledger in the mining industry, as a first step of certification and labelling of the sustainable material production. The methodology is evaluated in real-world implementation in scope of the DIG_IT project. The implementation provides significant amount of heterogeneous data originating from different processes and sources during the operation in mines, such as vehicles operations, mining and raw material extraction, workers physical parameters, environmental parameters, sensors (field, biometric, assets), market (supply chains, commodity prices), weather data, etc. Most of these datasets, especially the data about the emission, needs integrity and the ability to be audited by the public community, government and the stakeholders. The methodology for establishing the transparency for the mining operations, the requirements, technical architecture, General Data Protection Regulation (GDPR) assessment, Distributed Ledger Technology (DLT) infrastructure, as well as end-to-end traceability, are proposed and evaluated with the actual deployment. The approach advocates end to end security for the increased traceability and data integrity based on Distributed Ledger Technology. Namely, W3C Decentralised Identities and Public Key Infrastructure (PKI) should be deployed from the data source to the cloud and anchored onto the Blockchain. The approach showed promising results with the regulations compliance made possible, making the data available and advertised publicly for the emission compliance.

Heptanuclear {GdIII7} (complex 1) and tetradecanuclear {GdIII14} (complex 2) have been synthesized using a Rhodamine 6G ligand HL (Rhodamine 6G salicylaldehyde hydrazone) and characterized. Complex 1 has a rare disc-shaped structure, where the central Gd ion was connected to the peripheral six GdIII ions via CH3O-/μ3-OH- bridges. Complex 2 has an unexpected three-layer double sandwich structure with a rare μ6-O2- ion in the center of the cluster. Magnetic studies revealed that complex 1 exhibits a magnetic entropy change of 17.4 J kg−1 K−1 at 3 K and 5 T. On the other hand, complex 2 shows a higher magnetic entropy change of 22.3 J kg−1 K−1 at 2 K and 5 T.

The prohibitive costs of small-scale solar photovoltaic (PV) racks decreases PV adoption velocity. To overcome these costs challenges, an open hardware design method is used to develop two novel variable tilt racking designs. These are the first stilt mounted racking designs that allow manual change of tilt angle from zero to 90 degrees by varying the length of cables. The racks are designed using the calculated dead, wind and snow loads for Canada as conservative design for most of the rest of the world. Structural capacities of the wooden members are then ascertained and resisting bending moment, shear force, tensile force, and compressive force for them is calculated. A structural and truss analysis is performed to ensure that racking design with-stands the applicable forces. Moreover, implications of changing the tilt angle on the wooden members/cables used to build the system are also determined. The systems offer significant economic savings ranging from 1/3rd to 2/3rd the capital expenses of the commercially available alternatives. In addition, the racking designs are easy-to-build and require minimal manufacturing operations, which increases their accessibility. The stilt-mounted designs can be employed for agrivoltaic settings while allowing farm workers shaded ergonomic access to perform planting, weeding, and harvesting.

In this paper, a relatively new hyper convolution spectra for characterization liquid, viscoelastic and solid samples is presented. Special attention is paid to the biophysical characterization of epithelial tissues and early detection of cancer of the skin, cervix, colon, and oral cavity. The method is based on the use of two types of white light, diffuse and polarized, from the same LED light sources. Based on the subtraction of the intensity values of these two lights and using the hyper convolution algorithm, the ratio of paired and unpaired electrons of tissue is obtained. This ratio correlates well with the state of the tissue: normal, irritating, precancerous, and cancerous. First, a proof-of-concept studies were performed, and later clinical studies on samples from one thousand two hundred fifteen people were performed. It showed that hyper Opto-Magnetic Imaging Spectroscopy (OMIS) can detect epithelial tissue cancers with an accuracy of 85-96%. Bearing in mind that the OMIS method also acquires the biophysical characteristics of the deeper skin layers (epidermis, basement membrane, dermis) by analysing convolution spectra of RGB channels of both diffuse and polarized white light, it is possible to apply this method in cosmetics (skin hydration, collagen, and elastin states, etc.). During OMIS applications, we noticed places where improvements could be made to increase method’s accuracy using advanced machine learning methodologies.

Tamm Plasmon Polariton (TPP) is a nanophotonic phenomenon that has attracted much attention due to its spatial strong field confinement, ease of mode excitation and polarization independence. TPP has applications in sensing, storage, lasing, perfect absorber, solar cell, nonlinear optics, and many others. In this work, we demonstrate a biosensing platform based on TPP resonant mode. Both theoretical analyses based on Transfer Matrix Method and experimental validation through nonspecific detection of liquids of different refractive indices and specific detection of SARS-CoV-2 nucleocapsid protein (N-protein) are presented. Results show that TPP biosensor has high sensitivity and good specificity. For N-protein detection, the sensitivity is 1.5 nm/(µg/ml) and the limit of detection can reach down to below 7 ng/ml with spectrometer of 0.01 nm resolution of wavelength shift. This work paves the way for design, optimization, fabrication, characterization, and performance analysis of TPP based biosensors

Water distribution networks are often susceptible to pipeline leaks caused by mechanical damages, natural hazards, corrosion, and other factors. This paper focuses on the detection of leaks in water distribution networks (WDN) using a data-driven approach based on machine learning. A hybrid Autoencoder neural network (AE) is developed, which utilizes unsupervised learning to address the issue of unbalanced data (as anomalies are rare events). The AE consists of a 3DCNN encoder, a ConvLSTM decoder, and a ConvLSTM future predictor, making the anomaly detection robust. Additionally, spatial, and temporal attention mechanisms are employed to enhance leak localization. The AE first learns the expected behavior and subsequently detects leaks by identifying deviations from this expected behavior. To evaluate the performance of the proposed method, the Water Network Tool for Resilience (WNTR) Simulator is utilized to generate water pressure and flow rate data in a water supply network. Various conditions such as fluctuating water demands, data noise, and the presence of leaks are considered using the pressure-driven demand (PDD) method. Datasets with and without pipe leaks are obtained, where the AE is trained using the dataset without leaks and tested using the dataset with simulated pipe leaks. The results, based on a benchmark WDN and a confusion matrix analysis, demonstrate that the proposed method successfully identifies leaks in 96% of cases and a false positive rate of 4% compared to a random forest model baseline based on supervised learning with a false positive rate of 15% due to unbalanced data. Furthermore, a real case study demonstrates the applicability of the developed model for leak detection in operational conditions of water supply networks using inline sensor data.

Ankle joint flexion and extension exercises play an important role in the rehabilitation training of patients who have been injured or bedridden for a long time before and after surgery. Accurately guiding patients to perform ankle flexion and extension exercises can significantly reduce deep vein thromboembolism. Currently, most ankle rehabilitation devices focus on assisting patients with ankle flexion and extension movements, and there is a lack of devices for effectively monitoring these movements. In this study, we designed an ankle joint flexion and extension movement monitoring device based on a pressure sensor. It is composed of an STM32 microcontroller, a pressure sensor, an HX711A/D conversion chip, and an ESP8266 WiFi communication module. The value of force and effective number of ankle joint flexion and extension movements were obtained. An experimental device was designed to verify the accuracy of the system. The results showed that the device can effectively monitor and control ankle flexion and extension movements remotely, ultimately ensuring that the patient can effectively monitor and grasp the active ankle pump movement.

Currently available DNA detection techniques frequently require compromises between simplicity, speed, accuracy, and cost. Here, we propose a simple, label-free and cost-effective DNA detection platform developed at screen printed carbon electrodes (SPCEs) modified with reduced graphene oxide (RGO) and gold nanoparticles (AuNPs). The preparation of the detection platform involved a two-step electrochemical procedure based on GO reduction onto SPCEs followed by the electrochemical reduction of HAuCl4 to facilitate the post-grafting reaction with AuNPs. The final sensor was fabricated by the simple physical adsorption of a single-stranded DNA (ssDNA) probe onto AuNPs-RGO/SPCE electrode. Each preparation step was confirmed by morphological and structural characterization using scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy, respectively. Furthermore, the electrochemical properties of the modified electrodes have been investigated by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The results demonstrated that the introduction of AuNPs onto RGO/SPCEs led to an enhancement in surface conductivity, characteristic that favored an increased sensitivity in detection. The detection process relied on the change in the electrochemical signal induced by the binding of target DNA to the bioreceptor, and was monitored particularly by the change in the charge transfer resistance of a [Fe(CN)6]4−/3− redox couple added in the test solution.

This paper addresses the problem of low accuracy of conventional channel estimation and signal detection in complex fields like mines; and the problem of replacing the deep receiver at the receiver side with a deep neural network requires a large amount of data for training and slow training time. Combining the receiver side with traditional communication knowledge, a channel estimation and signal detection method based on the joint ReEsNet and SDRNet under the mine is proposed. The detailed step is to use ReEsNet network for interpolation of conventional channel estimation. Then, on the basis of ReEsNet, the output data is channel equalized and pilots removed, and the previously processed data is input to SDRNet to perform signal recovery. In the complex mine environment, the MSE values are 2e-2 for LS, 6e-3 for MMSE, and 8.44e-5 for ReEsNet at the 1500th sample data for interpolation compared to the conventional channel estimation. the MSE of ReEsNet is the smallest and is closer to the real channel estimation. Compared with the proposed joint channel estimation and signal detection methods, fewer trainable parameters reduce 97% of the parameters compared to CCRNet and 84% of the parameters compared to CNNNet; their detection results, however, are not very different.

Blood cell analysis is a crucial diagnostic process in medical practice. In particular, detecting white blood cells (WBCs) is essential for diagnosing of many diseases. The manual screening of blood films is a time-consuming and subjective process, which can lead to inconsistencies and errors. Therefore, automated detection of blood cells can improve the accuracy and efficiency of the screening process. In this study, an explainable Vision Transformer (ViT) model was proposed for the automatic detection of WBCs from blood films. The proposed model utilizes the self-attention mechanism to extract relevant features from the input images and leverages transfer learning by incorporating pre-trained model weights to improve its performance. The proposed model achieved a classification accuracy of 99.40% for five distinct types of WBCs and exhibited potential in reducing the time required for manual screening of blood films by pathologists. Upon examination of the misclassified test samples, it was observed that incorrect predictions were correlated with the presence or absence of granules in the cell samples. To validate this observation, the dataset was divided into two classes, namely Granulocytes and Agranulocytes, and a secondary training process was conducted. The resulting ViT model trained for binary classification achieved an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43% during the test phase. To ensure the reliability of the ViT model's multi-class classification of WBCs, the pixel areas that the model focuses on in its predictions are visualized through the Score-CAM algorithm.

Epilepsy is a neurological disorder that affects approximately 1% of the world's population. To diagnose and estimate the occurrence of epilepsy, the analysis of recorded brain activity is performed by a neurologist, which is not only time-consuming and tedious but also occasionally accompanied by human error. Therefore, in recent decades, researchers have aimed to unravel an approach for designing and building an automated method for diagnosing and estimating the occurrence of epilepsy. Accordingly, the present study proposed two new-fangled ways based on brain signals and a convolutional neural network (CNN). Moreover, this research implements a CNN with a sequential three-layer structure. Numerous experiments were performed, and the accuracy of estimating epilepsy using the developed methods was achieved at 95% without feedback and 97% with feedback. The proposed methods were proven to be more accurate than the previous techniques and can be employed as a physician's assistant once entering the field of operation.

Background: This work presents Com@Rehab, a technological patient-centred activity for individuals needing a physical rehabilitation approach and with a specific loss of functionality. Within this scope, this paper focuses on the description of the activity in virtual reality (VR), its components, design and user profile, and its versatility and future adaptability regarding different ages, pathologies and rehabilitation contexts. Methods: The VR activity has been customised according to the patient's clinical needs while replicating an activity of daily living, namely showering. In addition, to validate and get feedback on the developed system, a validation questionnaire was applied to a group of healthy individuals to test the efficiency of the technology that supports the VR activity. Results: Preliminary results show that 94% of the participants recommended the experience, the performance of the various components of the system was successfully implemented, participants quickly adhered to the VR technology, and the UI assistant’s functionality should be improved. Conclusion: This solution creates a more effective, interactive, humanised rehabilitation approach based on a Virtual Reality (VR) activity. The Com@Rehab Communication Module improves human-human and human-machine communication while contributing to health literacy.

Classical antibacterial drugs were designed to target specific bacterial properties distinct from host human cells to maximize potency and selectivity. These designs were quite effective as they could be easily derivatized to bear next generation drugs. However, the rapid mutation of bacteria and their associated acquired drug resistance have led to the rise of highly pathogenic superbug bacterial strains for which treatment with first line drugs is no match. More than ever, there is a dire need for antibacterial drug design that goes beyond conventional standards. Taking inspiration by the body’s innate immune response to employ its own supply of labile copper ions in a toxic attack against pathogenic bacteria, which have a very low Cu tolerance, this review article examines the feasibility of Cu-centric strategies for antibacterial preventative and therapeutic applications. Promising results are shown for the use of Cu-containing materials in the hospital setting to minimize patient bacterial infections. Studies directed at disrupting bacterial Cu regulatory pathways elucidate new drug targets that can enable toxic increase of Cu levels and perturb bacterial dependence on iron. Likewise, Cu intracellular chelation/prochelation strategies effectively induce bacterial Cu toxicity. Cu-based small molecules and nanoparticles demonstrate the importance of the Cu ions in their mechanism and display potential synergism with classical drugs.

Due to their increased surface area, extent of swelling and active substance loading capacity and flexibility, nanogels made from natural and synthetic polymers have gained significant interest in the scientific and industrial areas. Especially, customized design and implementation of non-toxic, biocompatible, and biodegradable micro/nano carriers makes their usage very feasible for a range of biomedical applications, including drug delivery, tissue engineering, and bioimaging. The design and application methodologies of nanogels have been outlined in this review. Additionally, the most recent advancements in nanogel biomedical applications have been discussed, with a particular emphasis on applications for the delivery of drugs and biomolecules.

The present study reports on the fabrication and performance of ammonia sensors based on single-walled carbon nanotubes (SWCNTs) coated with gold nanoparticles (AuNPs). The sensors were subjected to controlled concentrations of ammonia at two temperatures, namely 25 oC and 140 oC, and their response was monitored through successive cycles of ammonia exposure and nitrogen purging. The results demonstrate that the sputtering-based deposition of AuNPs on SWCNTs led to the best sensor performance, characterized by a rapid increase in resistance values upon exposure to ammonia, and efficient recovery at 140 oC. By contrast, the sensor with chemically impregnated AuNPs exhibited a slower response time. Furthermore, a novel device was developed that combined MoS2-AuNPs (sputtering)-SWCNTs, and it showed improved sensor performance compared to the devices with only AuNPs. Overall, the study provides valuable insights into the fabrication and optimization of SWCNT-based ammonia sensors for various applications, especially for detecting and quantifying small amounts of ammonia (concentrations below 1 ppm).

A new low-pass filter (LPF) circuit with independent adjustment of various, incl. digitally controlled, pole frequency resistors, pole quality factor and transmission coefficient. The proposed low-pass filter is based on the use of the properties of a multi-differential operational amplifier that performs the functions of a signal adder. The peculiarity of the filter is that it has three inputs, with respect to which different transmission coefficients are implemented, incl. inverting (-1) and non-inverting (+1). To check these properties of a low-pass filter in the Micro-Cap environment, computer simulation of a specific circuit was performed on a multi-differential operational amplifier AD 8130. Mathematical expressions are given for the main parameters of the proposed low-pass filter, which allow parametric synthesis of elements of a specific circuit under given restrictions on the used element base.

The service conditions of underground coal mine equipment are poor, and it is difficult to accurately extract the fault characteristics of rolling bearings. In order to better improve the accuracy of fault identification of rolling bearings, a fault detection method based on multiscale permutation entropy and SOA-SVM is proposed. First, the whale optimization algorithm is used to select the modal analysis number K and the penalty factor α of the variational mode decomposition algorithm. Then, the vibration signal of rolling bearings is dissolved according to the optimized variational mode decomposition algorithm, and the multi-scale permutation entropy of the main intrinsic mode function is calculated. Finally, the feature values of the matrix are entered into the SVM algorithm optimized by the seagull optimization algorithm to obtain the classification result. The experimental results based on the published rolling bearing datasets of Western Reserve University show that the identification success rate of the proposed method can reach 98.75%. The fault detection of the rolling bearings can be completed accurately and efficiently.

Various efforts have been made to detect minimum value of glucose in any medium like water or body buffer solutions with high-sensitivity, accurate, and low-cost sensors in order to enhance life style. Therefore, the present study was done to investigate reliability of two-dimensional plasmonic structure by circular dichroism (CD) and ellipsometry tools in different concentrations of glucose. Our results confirmed a dependency of the CD signal on glucose concentrations and also a very good sensitivity based on the phase difference between each polarization in ellipsometry parameters with the help of an achiral plasmonic structure.

Within phototherapy, a grand challenge in clinical cancer treatments is to develop a simple, cost-effective, and biocompatible approach to treat this disease using ultra-low doses of light. Carbon-based materials (CBM), such as graphene oxide (GO), reduced GO (r-GO), graphene quantum dots (GQDs), and carbon dots (C-DOTs), are rapidly emerging as a new class of thera-peutic materials against cancer. This mini-review summarizes the progress in lasts years re-garding the applications of CBM in photodynamic (PDT) and photothermal (PTT) therapies for tumor destruction. The current understanding of the performance of modified CBM, hybrids and composites, is also addressed. This approach seeks to achieve an enhanced healing action by im-proving and modulating the properties of CBM to treat various types of cancer. Metal oxides, organic molecules, biopolymers, therapeutic drugs, among others, have been combined with CBM to treat cancer by PDT, PTT, or synergistic therapies.

Since the 1970s, the application of microprocessor in industrial machinery and the development of computer systems have transformed the manufacturing landscape. The rapid integration and automation of production systems have outpaced the development of suitable human design criteria, creating a deepening gap where human factor was seen as an important source of errors and disruptions. Today the situation seems different: the scientific and public debate about the concept of Industry 4.0 has raised the awareness about the central role humans have to play in manufacturing systems, to the design of which they must be considered from the very beginning. The future of industrial systems, as represented by Industry 4.0, will rely on the convergence of several research fields such as Intelligent Manufacturing Systems (IMS), Cyber-Physical Systems (CPS), Internet of things (IoT), but also socio-technical fields such as social approaches within technical systems. This article deals with different Human dimensions associated with CPS and IoT and focuses on their conceptual evolution of automatization to improve the sociability of such automated production systems and consequently puts again the human in the loop. Hereby, our aim is to take stock of current research trends, and to show the importance of integrating human operators as a part of a socio-technical system based autonomous and intelligent products or resources. As results, different models of sociability as way to integrate human into the broad sense and/or the development of future automated production systems, were identified from the literature and analysed.

Deep convolutional neural network (CNN) has shown their great advantages in the single image deraining task. However, most existing CNN-based single image deraining methods still suffer from residual rain streaks and details lost. In this paper, we propose a deep neural network including the Multi-scale feature extraction module and the channel attention module, which are embed in the feature extraction sub-network and the rain removal sub-network respectively. In the feature extraction sub-network, the Multi-scale feature extraction module is constructed by a Multi-layer Laplacian pyramid, and is then integrated multi-scale feature maps by a feature fusion module. In the rain removal sub-network, the channel attention module, which assigns different weights to the different channels, is introduced for preserving image details. Experimental results on visually and quantitatively comparison demonstrate that the proposed method performs favorably against other state-of-the-art approaches

Mineral identification is an important part of geological analysis. Traditional identification methods rely on either the experiences of the appraisers or the various measuring instruments and the methods are either easily influenced by the experiences or need too much work. To solve the above problems, there have been studies using image recognition and intelligent algorithms to identify minerals. But the current studies can not identify many minerals, and the accuracy is low. To increase the number of identified mineral categories and the accuracy, we proposed the method that uses both the mineral images and the Mohs hardness in the deep neural networks to identify the minerals. The experimental results showed that the method can reach 94.0% Top-1 accuracy and 99.9% Top-5 accuracy for 28 common minerals. The model that combines image and Mohs hardness together can identify more minerals and increase the accuracy using less training data.

As a non-antibody scaffold, monobodies based on the fibronectin type III (FN3) domain overcome antibody size and complexity while maintaining analogous binding loops. However, antibodies and their derivatives remain the gold standard for design of new therapeutics. In response, clinical therapeutic proteins based on the FN3 domain are beginning to use native fibronectin function as a point of differentiation. The small and simple structure of monomeric monobodies confers increased tissue distribution and reduced half-life, whilst the absence of disulphide bonds improves stability in cytosolic environments. Where multi-specificity is challenging with an antibody format that is prone to mis-pairing of chains, FN3 domains in the fibronectin assembly already interact with a large number of molecules. As such, multiple monobodies engineered for interaction with therapeutic targets are being combined in a similar beads-on-a-string assembly which improves both efficacy and pharmacokinetics. Furthermore, full length fibronectin is able to fold into multiple conformations as part of its natural function and a greater understanding of how mechanical forces allow for the transition between states will lead to advanced applications that truly differentiate the FN3 domain as a therapeutic scaffold.

The case for demand-driven research and development has received important considerations among governments, donors and programme implementing partners in development planning and implementation. Addressing demand is believed to be a bottom-top approach for designing and responding to development priorities and is good for achieving development outcomes. In this paper, we discuss the concept and application of demand driven research for development (DDRD) in Africa. We use evidence of six projects implemented under the BiomassWeb Project in Africa. We focus on parameters on level of engagement of stakeholders - whose demand is being articulated, the processes for demand articulation, capacity building and implementation processes, innovativeness of the project, reporting and sustainability of the project. We find that the nature of the institutions involved in articulation and implementation of demand-driven research and development projects and their partnerships influence the impact and reporting of demand-driven projects.

In this study, we examine the relationship of bank level lending and borrowing decisions and the risk preferences on the dynamics of the interbank lending We develop an agent-based model that incorporates individual bank decisions using the temporal difference reinforcement learning algorithm with empirical data of 6600 S. banks. The model can successfully replicate the key characteristics of interbank lending and borrowing relationships documented in the recent literatur A key finding of this study is that risk preferences at individual bank level can lead to unique interbank market structures which are suggestive of the capacity that the market responds to surprising

In recent years, governments applied intelligent transportation system (ITS) technique to provide several convenience services (e.g., garbage truck app) for residents. This study proposes a garbage truck fleet management system (GTFMS) and data feature selection and data clustering methods for travel time prediction. A GTFMS includes mobile devices (MD), on-board units, fleet management server, and data analysis server (DAS). When user uses MD to request the arrival time of garbage truck, DAS can perform the procedure of data feature selection and data clustering methods to analyses travel time of garbage truck. The proposed methods can cluster the records of travel time and reduce variation for the improvement of travel time prediction. After predicting travel time and arrival time, the predicted information can be sent to user’s MD. In experimental environment, the results showed that the accuracies of previous method and proposed method are 16.73% and 85.97%, respectively. Therefore, the proposed data feature selection and data clustering methods can be used to predict stop-to-stop travel time of garbage truck.

The purpose of this paper is to compare various indicators of microbiological contamination in coastal areas near estuaries around the world. A major factor contributing to the presence of fecal coliforms is the geomorphology of the area, and, more particularly, whether the area concerned is an open or enclosed bay, the climate of the area, its tourism development, and, lastly, the urban, agricultural, livestock, or industrial activities carried out near river mouths. The aim was also to attempt to systematize the microbiological contamination of the above – mentioned estuaries around the world for broader applicability. The method used and the experimental apparatus for the isolation of bacteria was the membrane filtration method described in ISO 7704:1985. For the growth and enumeration of total coliforms and Escherichia coli the method described in ISO 9308-1 was used, while for enterococcus the one described in ISO 7899-2:2001. No statistically significant difference was found at many points in Greece and Spain, for either of the two patho-genic bacteria. In this work, the documentation of the microbiological contamination of river del-tas was achieved in similar areas (Greece and Spain) and could be applicable throughout the Mediterranean.

A consistent theory of quantum entanglement requires that constituent single-particle states belong to the same Hilbert space, the coherent eigenstates of a complete set of operators in a given representation, defined with respect to a shared continuous parameterization. Formulating such eigenstates for a single relativistic particle with spin, and applying them to the description of many-body states, presents well-known challenges. In this paper, we review the covariant theory of relativistic spin and entanglement in a framework first proposed by Stueckelberg and developed by Horwitz, Piron, et.\ al. This approach modifies Wigner's method by introducing an arbitrary timelike unit vector $n^\mu$ on which one induces a representation of $SL(2,C)$, rather than induce on the spacetime momentum $p^\mu$. Generalizing this approach, we construct relativistic spin states on an extended phase space $\{(x^\mu,p^\mu),(\zeta^\mu,\pi^\mu)\}$, inducing a representation on the momentum $\pi^\mu$, thus providing a novel dynamical interpretation of the timelike unit vector $n^\mu = \pi^\mu / M$. Studying the unitary representations of the Poincar\'e group on the extended phase space allows us to define basis quantities for quantum states and develop the gauge invariant electromagnetic Hamiltonian in classical and quantum mechanics. We write plane wave solutions for free particles and construct stable singlet states, and relate these to experiments involving temporal interference, analogous to the spatial interference known from double slit experiments.

In response to increasing urbanization and the need for infrastructure resilient to natural hazards, this study introduces an AI-driven predictive model designed to assess the risk of soil liquefaction. Utilizing advanced ensemble machine learning techniques, the model integrates geotechnical and geographical data to accurately predict the potential for soil liquefaction in urban areas, with a specific focus on Yokohama, Japan. This methodology leverages comprehensive datasets from geological surveys and seismic activity to enhance urban planning and infrastructure development in smart cities. The primary outputs include detailed soil liquefaction risk maps that are essential for effective urban risk management. These maps support urban planners and engineers in making informed decisions, prioritizing safety, and promoting sustainability. The model employs a robust combination of neural networks and gradient boosting decision trees to analyze and predict data points, assessing soil susceptibility to liquefaction during seismic events. Notably, the model achieves high accuracy in predicting soil classifications and N-values, which are critical for evaluating soil liquefaction risk. Validation against an extensive dataset from geotechnical surveys confirms the model’s practical effectiveness. Moreover, the results highlight the transformative potential of AI in enhancing geotechnical risk assessments and improving the resilience of urban areas against natural hazards.

The microstructural evolution, thermodynamics and kinetics of Mo-21%Tm₂O₃ (mass fraction, %) powder mixtures during ball milling were investigated using X-ray diffraction and transmission electron microscopy. Ball milling induced Tm₂O₃ to be decomposed and then dissolved into Mo crystal. The supersaturated nanocrystalline solid solution of Mo (Tm, O) was obtained after 96 h of ball milling. The elements of Mo, Tm and O were distributed uniformly in the ball-milled particles. Based on the semi-experimental theory of Miedema, a thermodynamic model was developed to calculate the driving force of phase evolution. There was no chemical driving force to form a crystal solid solution of Tm atoms in Mo crystal or an amorphous phase because the Gibbs free energy for both processes was higher than zero. For Mo-21%Tm₂O₃, it was mechanical work, not negative heat of mixing, that provided the driving force to form supersaturated nanocrystalline Mo (Tm, O) solid solution.

Several lime mortars for the repair of painted plasters of the rock cut church of Ss. Pietro and Paolo in Matera were studied. They were designed taking into account both aesthetic criteria that need to be fulfilled in the field of paintings restoration, and physical-mechanical compatibility with the original materials on site, i.e. the pre-existing plasters and the supporting rock. Mixes with calcareous and silica aggregates, based on different grain size proportions, were prepared for filling missing portions of the original painted plaster. The effects of the mineralogical nature and size of the aggregates on the characteristics and properties of the mixes were investigated in relation to the microstructure, physical-mechanical features and resistance to salt ageing. At the end of the experimental campaign, the overall performances were evaluated.

Cirina forda caterpillars are a significant protein source in Central Africa, yet their preservation and consumption pose challenges. This study investigates the drying of Cirina forda caterpillars to enhance preservation methods and product quality. Caterpillars were subjected to drying experiments at temperatures ranging from 40°C to 70°C, using tunnel and oven dryers. The drying kinetics was analyzed using mathematical models, revealing a water diffusion-limited mechanism in all the tested conditions. The Newton model provided adequate estimations of the drying behavior, facilitating further practical dryer design. Shrinkage analysis indicated a two-step process independent of the drying conditions. Achieving a moisture content suitable for prolonged storage resulted in substantial volume loss, highlighting the challenges of preserving dried caterpillars. These findings shed light on how to optimize drying processes for Cirina forda caterpillars, crucial for sustaining protein sources in Central Africa.

(1) Background: Pedestrian injuries (PIs) represent a significant proportion of road traffic injuries. We aimed at investigating the incidence and mortality of PIs in different age groups and Socio-demographic Index (SDI) categories between 1990 and 2017. (2) Method: Estimates of age-standardized incidence and mortality along with trends of PIs by SDI levels were obtained from the Global Burden of Disease from 1990 to 2017. We also calculated forecasts until 2040. (3) Results: Globally, PIs incidence increased by 3.31% (–9.94 to 16.56) in 2017 compared to 1990. Men have a higher incidence of PIs than women. Forecasted incidence was 132.02 (127.37 to 136.66) per 100,000 population in 2020, 101.52 (65.99 to 137.05) in 2030 and reduced further to 71.02 (10.62 to 152.65) by 2040. Globally across all SDI categories, there was a decreasing trend in mortality due to PIs with the global estimated percentage reduction of 37.12% (–45.19 to –29.04). (4) Conclusions: The results show that PIs are still a burden for all SDI categories despite some variation. Although incidence and mortality are expected to decrease globally, some SDI categories may require particular attention in addition to specific vulnerable age groups. Further studies addressing incidence and mortality patterns in vulnerable SDI categories are needed.

Despite the advances in stent design, coating drugs, polymers, the use of intravascular imaging, special devices (such as intravascular lithotripsy, ultra-high-pressure non-compliant balloons, and drug-coated balloons), and improved operator skills and techniques, the problem of in-stent restenosis (ISR) and stent thrombosis (ST) remains a challenge for interventional cardiologists. Atherosclerotic lesions, ISR, and ST, are systemic metabolic chronic inflammatory illnesses with a local manifestation in the coronary vessels. Therefore, we propose managing these systemic illnesses systemically through plant-based diet (PBD) intervention. We explain how PBD intervention can help mitigate atherosclerosis, ISR, and ST. Thus, PBD intervention can overcome biological and epigenetic problems in managing ISR and ST similarly to reversing atherosclerosis. Our team is pioneering the implementation of PBD for cardiology patients who have undergone coronary intervention. So far, the results have been very promising. We hope that in the future, many research studies will be performed to investigate further the role of PBD in decreasing the incidence of ISR and ST.

This paper proposes a simulation technique for investigating the battery thermal management system based direct refrigerant cooling (BTMS-DRC). It employs finite element method for a combined-conduction-convection heat transfer to predict the module temperature and to prove the temperature uniformity. The refrigerant side cooling is based on the two-phase flow evaporation which is represented by the convection heat transfer (flow evaporation) under a certain refrigerant saturation temperature. The battery heat generation is modeled as the constant heat flux. The main module is modeled as the conduction heat transfer. The real BTMS-DRC is constructed for experimentation with the test bench which is based on the dual-evaporators vapour compression refrigeration system. The simulated result is validated with the experimental results to ensure the correction of the modelling. The simulation also investigates the impact of the heat generation, convection heat transfer coefficient, refrigerant saturation temperature, and thermal conductivity on the module temperature and temperature uniformity. It is found that the simulated results agree well with the experimental results. The errors are around 2.9 - 7.2% throughout the specified working conditions. Overall, the proposed technique can be used as a design tool for further developing the BTMS-DRC.

Mallard (Anas platyrhynchos) are currently one of the most popular species in rare bird breeding in several southern provinces of China, but there are very few studies on the gut microbial communities of domestic and wild mallards. In this study, 16S rRNA gene high-throughput sequencing technology was used to compare the composition and diversity of gut microbial communities in domestic and wild mallards. Alpha diversity analysis revealed significant differences in the gut microbial communities of domestic and wild mallards. Beta diversity analysis showed that the two groups of stool samples were mostly separated on the principal coordinate analysis (PCOA) plot. In domestic mallards, Firmicutes (67.97%) was the most abundant bacterial phylum, followed by Proteobacteria (24.48%), Bacteroidetes (3.13%), Fusobacteria (2.21%), and Actinobacteria (1.10%). The dominant bacterial phyla in wild mallards were Firmicutes (79.02%), Proteobacteria (12.85%), Fusobacteria (3.37%), and Bacteroidetes (2.79%). At the genus level, a total of 10 dominant genera (Streptococcus, Enterococcus, Clostridium, Lactobacillus, Soilbacillus, Bacillus, Acinetobacter, Comamonas, Shigella, and Cetobacterium) with an average relative abundance greater than 1% were detected in the fecal samples of both groups. The average relative abundance of five potential pathogens (Streptococcus, Enterococcus, Acinetobacter, Comamonas, and Shigella) was higher in domestic mallards than in wild mallards. The enrichment of pathogenic bacteria in the intestinal tract of domestic mallards should be of sufficient concern.

Mallard (Anas platyrhynchos) are currently one of the most popular species in rare bird breeding in several southern provinces of China, but there are very few studies on the gut microbial communities of domestic and wild mallards. In this study, 16S rRNA gene high-throughput sequencing technology was used to compare the composition and diversity of gut microbial communities in domestic and wild mallards. Alpha diversity analysis revealed significant differences in the gut microbial communities of domestic and wild mallards. Beta diversity analysis showed that the two groups of stool samples were mostly separated on the principal coordinate analysis (PCOA) plot. In domestic mallards, Firmicutes (67.97%) was the most abundant bacterial phylum, followed by Proteobacteria (24.48%), Bacteroidetes (3.13%), Fusobacteria (2.21%), and Actinobacteria (1.10%). The dominant bacterial phyla in wild mallards were Firmicutes (79.02%), Proteobacteria (12.85%), Fusobacteria (3.37%), and Bacteroidetes (2.79%). At the genus level, a total of 10 dominant genera (Streptococcus, Enterococcus, Clostridium, Lactobacillus, Soilbacillus, Bacillus, Acinetobacter, Comamonas, Shigella, and Cetobacterium) with an average relative abundance greater than 1% were detected in the fecal samples of both groups. The average relative abundance of five potential pathogens (Streptococcus, Enterococcus, Acinetobacter, Comamonas, and Shigella) was higher in domestic mallards than in wild mallards. The enrichment of pathogenic bacteria in the intestinal tract of domestic mallards should be of sufficient concern.

We appeal the science community to review the basic definition of wavelength dependent cosmic red shift. We would like to emphasize the point that, the true cosmic red shift must be defined as the ratio of loss in energy of photon to the energy of photon at galaxy. In terms of wavelength, photon red shift must be defined as the ratio of change in wavelength to the observed wavelength of photon. Considering the recent paper pertaining to cosmic halt authored by Cosmin Andrei, Anna Ijjas, and Paul J. Steinhardt and reviewed by Saul Perlmutter, in this paper we propose a very simple model of the universe having early stage light radius of proton seems to be equal to half the product of reduced Planck’s constant and current speed expansion and current stage quantum halt accompanied by continuous light speed rotation. In this context, representing early cosmic expansion and rotation as an outward spiral, it seems possible to consider Hubble’s law as a representation of current cosmic light speed rotation having no further expansion. To understand the current cosmic halt, we are proposing very simple and very strange atomic and nuclear relations that constitute Hubble parameter and gravitational constant. Qualitatively and quantitatively, gravitational potential energy of proton and electron separated by a distance of root mean square radius of proton. Based on uncertainty condition, it needs further study, observations and analysis. We have extended our analysis towards the microscopic coupling constants. Thus, instead of considering the cosmological variable nature of elementary physical constants, we consider the case of current or nearby cosmic halt by means of considering the equality of current cosmic variable physical constants having similar dimensions of fixed elementary physical constants and their combinations. In this connection, it is very interesting to note that, our estimated cosmic age of (2/H0) is matching with recent estimates proposed by Rajendra P Gupta and published in MNRAS.

This study investigates the Hagen–Poiseuille pipe flow of viscoplastic fluids, focusing on analytical predictions of concrete pumping using the shear-stress-dependent parabolic model. Research highlights encompass solving the steady laminar pipe flow for viscoplastic fluids described by the parabolic model, extending analytical studies to a nonlinear rheological model with easily accessible experimental parameters, presenting detailed results for the two-fluid parabolic model, and introducing computational apps implementing theoretical findings. The parabolic model outperforms linear models, such as the Bingham model, in accuracy by accounting for the nonlinearity in the shear stress and shear rate relations. The influence of rheological parameters on these relations is analyzed, and their versatility is demonstrated by a Wolfram Mathematica-based application program. This approach is adaptable for other models with shear stress as the independent variable, offering valuable insights into viscoplastic fluid flows.

The metro station ridership features are associated significantly with the built environment factors of the pedestrian catchment area surrounding metro stations. The existing studies have focused on the impact on total ridership at metro stations, ignoring the impact on varying patterns of metro station ridership. Therefore, the reasonable identification of metro station categories and built environment factors affecting the varying patterns of ridership in different categories of stations is very important for metro construction. In this study, we developed a data-driven framework to examine the relationship between varying patterns of metro station ridership and built environment factors in these areas. By leveraging smart card data, we extracted the dynamic characteristics of ridership and utilized hierarchical clustering and K-means clustering to identify diverse patterns of metro station ridership, and finally identified six main ridership patterns. We then developed a new built environment measurement framework and adopted multinomial logistic regression analysis to explore the association between ridership patterns and built environment factors. (1) The clustering analysis result revealed that six station types were classified based on varying patterns of passenger flow, representing distinct functional characteristics. (2) The regression analysis indicated that diversity, density, and location factors were significantly associated with most station function types, while destination accessibility was only positively associated with employment-oriented type station, and centrality was only associated with employment-oriented hybrid type station. These results could inform the coordinated development of rail transit and land use, and the renewal and enhancement of the built environment in the pedestrian catchment area surrounding metro stations.

Two solvated rhenium(IV) complexes with formula [ReCl4(bpym)]·MeCN (1) and [ReCl4(bpym)]·CH3COOH·H2O (2) [bpym = 2,2'-bipyrimidine] have been prepared and characterized by means of Fourier transform infrared spectroscopy (FT–IR), scanning electron microscopy and energy dispersive X-ray analysis (SEM–EDX), single-crystal X-ray diffraction (XRD) and SQUID magnetometer. 1 and 2 crystallize in the monoclinic system with space groups P21/n and P21/c, respectively. In both compounds, the Re(IV) ion is six-coordinate and bound to four chloride ions and two nitrogen atoms of a 2,2’-bipyrimidine molecule forming a distorted octahedral geometry around the metal ion. In the crystal packing of 1 and 2, intermolecular halogen⋯halogen and π⋯halogen type interactions are present. Hydrogen bonds take place only in the crystal structure of 2. Both compounds exhibit an akin crystal framework based on halogen bonds. Variable-temperature dc magnetic susceptibility measurements performed on microcrystalline samples of 1 and 2 show a similar magnetic behavior for both compounds, with antiferromagnetic exchange between the Re(IV) ions connected mainly through intermolecular Re-Cl···Cl-Re interactions.

With the increase in occupation-specific risks of lip cancer associated with solar radiation, there is a need for developing photoprotective lipsticks to protect skin against harmful effects of UV radiation. Considering the unique chemical and physical properties of low-molecular-weight organogelators (LMOGs), the present study intended to assess the UV protective properties of LMOGs-based lipstick formulations. In this study, dibenzylidene-D-sorbitol (DBS) and 12-hydroxystearic acid (12-HSA) were used to formulate lipsticks : L1 (1% DBS), L2 (10% 12-HSA), L3 (1.5% DBS) and L4 (control, no LMOGs). The lipstick formulations were tested for in vitro sun protection factors (SPF), UVA protection factor (UVA-PF), thermal, mechanical and texture analyses. Lipsticks with LMOGs exhibited higher UVA-PF and SPF, and more particularly 12-HSA-based lipstick. Results showed also the viscoelastic and heat-resistant properties of LMOGs and their effect of increasing pay-off values. In general, texture analysis indicating that 12-HSA-based lipstick was significantly harder to bend compared to control, while other formulations became softer and easier to bend throughout the stability study. Finally, sensorial and instrumental analyses permitted to classify lipsticks into two groups. This work suggests the potential use of LMOGs as a structuring agent for lipsticks paving the way towards more photoprotective and sustainable-derived alternatives.

In a world where conventional sources of energy are fast depleting, the quest for alternative energy sources may hold the key for the survival of humanity. In the present work, emphasis has been given to the idea of producing energy from perovskite based solar cells. In order to bring this idea into fruition, a unique and novel nano structured perovskite material n-propyl ammonium lead chloride (C₃H₇NH₃⁺PbCl₃⁻) was prepared through a unique co-precipitation route using n-propyl amine (n-C₃H₇NH₂) and hydrochloric acid as the starting precursors with aqueous solution of Pb(CH₃COO)₂3H₂O. Finally acetic acid was added to the solution and this solution was allowed to concentrate and then gradually cooled down to room temperature. After math, the synthesized material was spin-coated on TiO₂ film to fabricate the solar cell. The device was then undergone systematic analysis using XRD, SEM, UV and Photo Conversion to get a transparent idea regarding its structural, electrical and optical properties. When experimentally applied, this perovskite-based solar cell has shown energy conversion efficiency (η) of around 6.01 % which is noticeably good. Thus it can be concluded that this material is promising for fabrication of vastly efficient solar cells. This technology can be tried in large scale as an alternative of conventional energy in the near future.

Starting from the environmental issues related to global warming, climate change and reduction of greenhouse gas emissions, this review paper describes how CO₂ recycling can represent a challenging strategy suitable to explore new concepts and opportunities for catalytic and industrial development. In this view, the production of dimethyl ether (DME) from catalytic hydrogenation of CO₂ appears as a viable technology, able to meet also the ever-increasing need for alternative environmentally-friendly fuels and energy carriers. Basic considerations on thermodynamic aspects controlling DME production from CO₂ are presented, then summarizing the main catalytic systems developed in such a field. Special attention is paid on the role assumed during last years by zeolite-based systems, either in the methanol-to-DME dehydration step or in the one-pot CO₂-to-DME hydrogenation. On the whole, the productivity of DME results significantly to be dependent on several catalyst features, linked not only to the metal-oxide phase responsible for CO₂ activation/hydrogenation, but also to specific properties of the zeolites (i.e., topology, porosity, surface area, acidity, interaction with active metals, distributions of metal particles, …) influencing activity and stability of hybridized bifunctional heterogeneous catalysts.

The learning equations of an ANN are presented, giving an extremely concise derivation based on the principle of backpropagation through the descendent gradient. Then, a reflection is developed on the dual attention network that applies the learning equations and coordinates subnetworks toward purposeful behaviors. Speculation is made on possible developments of functionalities that could provide additional skills and at the same time shed light on competencies typical of ``natural intelligence".

Objective: The main purpose of this meta-analysis was to assess the efficacy and safety of Oncolytic Viruses (OVs) in glioma therapy. Methods: We searched the literature in PubMed, EMBASE, Web of Science, and the Cochrane Library. The primary outcomes assessed were overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and adverse events (AEs). The risk of bias was evaluated by sensitivity analysis and publication bias. Results: We identified 21 studies with 440 patients in this meta-analysis. In the single-arm analysis, the results showed that the 1-year OS rate was 47% (95% CI: 34%-61%, I2 = 75%) and the 2-year OS rate was 14% (95% CI: 10%-20%, I2 = 0%). The median OS was 12.48 months (95% CI: 10.71-14.25, I2 = 96%). The 1-year PFS rate was 13% (95% CI: 5%-24%, I2 = 0%), with the median PFS being 4.01 months (95% CI: 2.99-45.03, I2 = 96%). The pooled estimate of ORR was 7% (95% CI: 3%-12%, I2 = 23%). Funnel plots for median PFS were asymmetric with Egger’s test P < 0.01 indicating publication exists. The incidence of OVs-related AEs was 49% (95% CI: 20%-79%, I2 = 95%), and AEs > grade 3 was 8% (95% CI: 3%-16%, I2 = 62%). Conclusion: This meta-analysis indicated that OVs therapy was relatively safe but did not significantly extend survival in patients with gliomas.

Prigogine’s 1978 concept of dissipative structures, drawing parallels with living systems, forms the basis for exploring life’s unique traits. However, these identified similarities prove insufficient in capturing the entirety of life. To address this gap, our proposed modeling approach emphasizes the distinctive ability of living organisms to observe other systems—an attribute intricately tied to quantum mechanics’ "measurement" processes, as highlighted by Howard Pattee. This article introduces a comprehensive mathematical model centered on quantum dynamical dissipative systems, portraying living systems as entities defined by their observational capacities within this framework. The exploration extends to the core dynamics of these systems and the intricacies of biological cells, including the impact of membrane potentials on protein states. Within this theoretical structure, the model is expanded to multicellular living systems, revealing how cells observe quantum dynamical systems through protein state changes influenced by membrane potentials. The conclusion acknowledges the current theoretical status of the model, underscoring the crucial need for experimental validation, particularly regarding the superposition state of membrane proteins under the influence of an electric field.

This article presents the results of analyzing emergency situations that lead to substantial economic losses during recovery efforts. Various technical solutions are explored that can identify the onset and, in some cases, the duration of natural or technical factors influencing the occurrence of such emergencies. The relevance of developing a combined protection system for wind generators is substantiated. The system comprises a three-tier structure. The first, lowest level, collects real-time data on monitored parameters. The second level stores this data, calculates missing coordinates using observers, and generates warning messages for wind generator operators about potential emergency situations. The third, highest level, includes the operator's computer. This combined protection system plays a crucial role in reducing emergency occurrences and enhancing the reliability and lifespan of wind generators.

In modern agriculture, decision-making is deeply intertwined with data, yet essential agricultural knowledge remains encased within dense scientific texts such as journals, manuals, and free-text reports. This necessitates specialized search systems capable of distilling and delivering precise information in response to specific queries from agricultural users. This study introduces AgriQuery, a sophisticated agent designed to field natural language questions from the agricultural sector by exploring vast troves of scientific documents. A comprehensive review and analysis of farmers' information needs have been conducted, leading to the creation of an information retrieval test collection that includes actual queries, a vast array of scientific documents segmented into passages, and a set of ground truth relevance assessments that map these passages to corresponding queries. We explore and benchmark several information retrieval models, highlighting the efficacy of advanced neural ranking models in this context. AgriQuery's proposed deployment architecture features a client interface on the Telegram platform and a backend model on standard hardware. This test collection aims to catalyze further research into matching natural language queries with scientific text answers, with implications extending beyond agriculture.

Early in the middle of the 1800s. Up until about 1900, the automotive land speed record 1 was held by an electrical vehicle. Unfortunately, internal combustion engine vehicles did not do 2 well in the market due to their high cost, limited speed, and short range in the 20th century, which 3 resulted in a global fall in their use as private motor vehicles. Since there are no fuel emissions into 4 the environment, there are important uses for electrical vehicles, Electric cars, or EVs, have a closed 5 circuit, cheap operating costs, no maintenance requirements, and excellent performance. The goal 6 of this project is to assess the current state of electric vehicles and the issues surrounding the global 7 advancement of electric vehicle technology. Because of the changing climate.8

During the development of new medicines mutually exclusive specificity appears to be required but hitherto is most often included at the late clinical trial stage. Here, I describe the use of Hedgehog signalling in Drosophila as a model to represent a mutually exclusive relationship within the context and report the identification of CG43658, a putative Rho guanine nucleotide exchange factor and orthologue of the Charcot-Marie-Tooth disease associated gene ARHGEF10. Furthermore, a Computer that is able to consider the whole and describe the sum of everything in it absolutely in unambiguous terms is a useful tool. Here I describe such a computer with a suggestion for an application to examine any component in the context where a medicine is sought. Taken, together I would like to propose this method as a fundamental necessity and improvement upon current existing methods related not only to Hedgehog signalling but plausibly to life science research and the search for medicines in general.

In politics and society, organic farming is widely seen as the ideal way to address the many challenges facing agriculture and food security. In order to examine the extent to which this view is realistic, the impact of organic and conventional farming is compared on the basis of relevant criteria such as land requirements, greenhouse gas emissions, biodiversity, food quality, production and environmental costs, reduced meat consumption, the need for agricultural imports and the global expansion of arable land. Land is considered to be a central factor. Such a comprehensive analysis is necessary to assess the impact of different influencing factors and to provide important suggestions for targeted decision making. Although organic farming has a positive impact on land use in many ways, it quickly reaches its limits. The low yields and high production costs resulting from the limited choice of means of production make organic farming an unsuitable model for the future in a world with limited arable land, a high and growing demand for food, and low incomes for large sections of the population. It can, however, provide significant impetus and contribute important technological knowledge for the further development of agriculture as a whole to meet the complex objectives of sustainability, greenhouse gas mitigation, biodiversity and global food security by incorporating all available technologies. Based on this, a model for responsible, sustainable intensive agriculture is outlined and proposals for an eco-social framework to support this model are put forward.

This paper presents a quantum formulation for classical abstract dynamical systems (ADS), defined by coupled sets of first order differential equations. They are referred as “abstract” because their dynamical variables can be of different interrelated nature, not necessarily corresponding to physics, such as populations, socioeconomic variables, behavioural variables, etc. A first order classical Hamiltonian in canonical moments can be derived for ADS by using Dirac’s dynamics for singular Hamiltonian systems. And also a corresponding first order Schrödinger equation (which involves the existence of a system Planck constant particular of each system) can be derived from this Hamiltonian. However, Bohm and Hiley’s reinterpretation of quantum mechanics produces no further information about the mathematical formulation of ADS. However, a second order classical Hamiltonian in canonical moments can be also derived for ADS, as well as a corresponding second order Schrödinger equation. In this case, Bohm and Hiley’s reinterpretation of quantum mechanics provides a quantum Hamiltonian that does provide the quantum formulation for ADS, which provides new quantum variables interrelated dynamically with the classical variables. An application case is presented: the one-dimensional autonomous system given by the logistic dynamics, where the differences between the classical and the quantum trajectories are highlighted.

Premature failure and degradation of layers are the main problems for transportation infrastruc-ture. Addressing these issues necessitates implementing structural health monitoring (SHM) for transportation layers. This strategic approach is instrumental in mitigating maintenance expenses, prolonging the operational lifespan, and preventing accidents resulting from abrupt layer col-lapse. To this end, this research investigated the stress/strain and damage detection capabilities of a self-sensing cementitious composite developed for potential utilization in the construction of an intelligent subgrade layer. The prepared self-sensing cementitious composite consisted of 10% cement and hybrid conductive fillers, including multiwalled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) in sand. Initial findings reveal that the electrical resistivity of the composite is significantly affected by the concentration of MWCNTs/GNPs, with a minimum concentration of more than 0.5% needed to achieve a responsive cementitious composite. More-over, the piezoresistive analysis indicates that an increase in the concentration of MWCNTs/GNPs and stress levels leads to an improvement in the stress/strain sensing perfor-mance. When the self-sensing cementitious composite is subjected to equivalent stress levels, variations in the FCR exhibit an increasing trend with decreasing resilient modulus, stemming from a decrease in stiffness due to the increased concentration of MWCNTs/GNPs. Additionally, the EIS analysis demonstrates a contraction for the Nyquist curves under compressive ramp loading prior to failure, followed by the expansion of these curves postfailure. Scanning electron microscopy (SEM) images visually showcase the bridging effects of MWCNTs and the filling ef-fects of GNPs within the composite structure.

Background The falsification of food products is a practice that has always been with mankind. Initially, it stemmed from the need to satisfy basic needs, and then from the desire for profit, at the expense of the health and sometimes the lives of consumers. Today, food safety issues are becoming a priority in consumers' daily lives. It is estimated that one in four food products is falsified. Man has lost the ability to naturally identify healthy food. He has therefore begun to prioritise the speed and ease of its preparation at the expense, often of health. The techniques currently available for detecting falsification are inaccessible to the ordinary consumer. The technique proposed by the authors is the beginning of a publicly available method for detecting falsification. Scope and approach Currently, instrumental or sensory methods are used to assess the authenticity, and therefore identification, of foods. The former are expensive and time-consuming and the latter are unobjective. The authors proposed a method using ultrasound with parameters that are neutral to the material to be tested. The technique allows results to be obtained in times measured in microseconds without destroying the material under test, saving costs. Three differently weighted groups of beers were used for the study. These were industrial, regional and domestic beers. Main findings and conclusions The results of the study indicate that the ultrasonic method is useful for the non-destructive identification of industrial and regional beers, which are produced using different technologies and differ in composition. The method analysed was not suitable for separating homebrew beers from other types of beer.

The air is a fundamental element for the life on Earth. Rapid urbanization and industrialization release large amounts of pollutants (harmful gases, microorganisms, and particulate matter, among others) into the atmosphere, leading to health hazards. Air filtration is still the most used and promising technique for protecting air against pollutants. The quality and efficiency of filtering process are dependent of material used for the filter and other filter’s properties. Several methods can be used for manufacturing filters, one of them uses fibrous membranes obtained through electrospinning from polymeric solutions. Electrospinning is a technique that combines the application of an electrostatic field to a moving fluid which results in the fibers formation. Electrospun membranes are constituted by fibers that the sizes spread on submicro and or nanometric scales. This review focuses on fiber membranes obtained by electrospinning for filtration of particulate matter. It is addressed the effect of solution, processing and ambient parameters on the morphology and dimensional characteristics of fiber membranes. The basic principles of air filtration and test and characterization of filter performance are presented. The research progress on electrospun nanofibers as air filters in recent years is summarized and examined. Finally, conclusion and future perspectives in electrospun fibers for air filtration are provided and discussed.

Mucin glycans are important component of the mucus barrier and vital defence against physical and chemical damage as well as pathogens. Abnormal expression of mucin glycans can lead to disease, especially cancer. Here, we first summarize the main types of glycosylation on mucins and the mechanisms by which abnormal mucin glycans occur. We next describe the role of ab-normal mucin glycans in cancer. Finally, we describe MUC1-based antibodies, vaccines, radio-pharmaceuticals, and CAR-T therapies using the best characterized MUC1 as an example.

Savolitinib is a highly selective small molecule inhibitor of the mesenchymal epithelial transition factor (MET) tyrosine kinase, primarily developed for the treatment of non-small cell lung cancer (NSCLC) with MET mutations. It is also being investigated as a treatment for breast, head and neck, colorectal, gastric, pancreatic, and other gastrointestinal cancers. In both preclinical and clinical studies, it has demonstrated efficacy in lung, kidney, and stomach cancers. Savolitinib is an oral anti-cancer medication taken as a 600 mg dose once daily. It can be used as a monotherapy in patients with non-small cell lung cancer with MET mutations, and in combination with epidermal growth factor receptor (EGFR) inhibitors for patients who have developed resistance to them. Furthermore, savolitinib has shown positive results in gastric cancer treatment, particularly in combination with docetaxel. As a result, this review aims to validate its efficacy in NSCLC and suggests its potential application in other gastrointestinal cancers, such as pancreatic cancer, based on related research in gastric and renal cancer.

Background: Cochlear implantation is an efficient treatment for postlingually deafened adults who do not benefit sufficiently from acoustic amplification. Implantation is indicated when it can be foreseen that speech recognition with a cochlear implant (CI) is superior to that with a hearing aid. Especially for subjects with residual speech recognition it is desirable to predict CI outcome on the basis of preoperative audiological tests. Purpose: The purpose of the study was to extend and to refine a previously developed model for CI outcome prediction for subjects with preoperative word recognition to include subjects with no residual hearing by incorporat-ing additional results of routine examinations. Results: By introducing the duration of unaided hearing loss (DuHL) the median absolute error (MAE) of the prediction was reduced. While for subjects with preoperative speech recognition the model modification did not change the MAE, for subjects with no residual speech recognition before surgery, the MAE decreased from 23.7% with the previous model to 17.2% with the extended model. Conclusions: Prediction of word recognition with CI is possible within clinically relevant limits. Outcome prediction is particu-larly important for preoperative counselling and in CI aftercare, to support systematic monitor-ing of CI fitting.

Although relatively rare, affecting 8% of the general population, autoimmune disorders are causative linked with chronic diseases and morbidity. Control of the course of the disease is closely dependent on the ability to monitor its onset as well as response to treatment. In the present report, we review the progress in the development of biosensor-based approaches and related tools for the Point-of-Care diagnosis and monitoring of biomarkers related to several autoimmune diseases, such as Myasthenia Gravis, Rheumatoid Arthritis, Multiple Sclerosis, Systemic Erythematosus Lupus, Crohn’s Disease, Diabetes Mellitus, Behcet’s disease, Psoriasis and Celiac Disease. Various biosensing technologies are discussed, including electrochemical, optical and mechanical ones, along with the use of advanced nanomaterials and immobilization techniques for the biorecognition elements. The need for innovative devices with unique features of rapid, low-cost, real-time detection is considered in the context of preventing permanent organ and tissue damage from chronic autoimmune diseases.

In this review, the effect of colostrum on immune system development in buffalo calves was examined. The neonatal period is critical for buffalo calves and is characterized by a high mortality rate (more than 40%); the intake of colostrum improves the immune system of calves, increasing their chances of survival. Mainly in intensive farms, the availability of good quality colostrum is necessary, thus, good quality colostrum is often stored to provide newborn calves which cannot be fed by their mothers. The buffalo breeding is constantly expanding in Italy, mainly thanks to the Mozzarella cheese production that represents an excellence of the “Made in Italy” and is exported worldwide. High calf mortality rates directly affects the profitability of the business. Compared with other species, researches on buffalo colostrum are scarce; nevertheless it is fundamental to improve colostrum characteristics and management to guarantee buffalo calves health and reduce their mortality rate.

The number of animals bred, raised, and slaughtered each year is on the rise, resulting in increasing impacts to welfare. Farmed animals are also becoming more diverse, ranging from pigs to bees. The diversity and number of species farmed invites questions about how best to allocate currently limited resources towards safeguarding and improving welfare. This is of the utmost concern to animal welfare funders and effective altruism advocates, who are responsible for targeting the areas most likely to cause harm. For example, is tail docking worse for pigs than beak trimming is for chickens in terms of their pain, suffering, and general experience? Or are the welfare impacts equal? Answering these questions requires making an interspecies welfare comparison; a judgment about how good or bad different species fare relative to one another. Here, we outline and discuss an empirically-based methodology that aims to improve our ability to make interspecies welfare comparisons by investigating welfare range, which refers to how good or bad animals can fare. We begin our proposal with a theory of welfare. We operationalize that theory of welfare by identifying metrics that are defensible proxies for measuring welfare, including cognitive, affective, behavioral, and neuro-biological measures. We assign differential weights to those proxies that reflect their evidential value for the determinants of welfare, such as the “Delphi'' structured deliberation method with a panel of experts. Then we review the evidence and score its quality to ascertain whether a particular taxa may possess the proxies in question to construct a taxa-level welfare range profile. Finally, we use a Monte Carlo simulation to generate an overall estimate of comparative welfare range relative to our hypothetical index species - humans. Interspecies welfare comparisons will help facilitate empirically informed decision-making to streamline the allocation of resources and to ultimately better prioritize and improve animal welfare.

Agroecology, defined as the ecological science of food production is also as practical approach to design food production systems based on local concerted solutions that aim to promote synergy among the diversity of human and non human food systems elements. These two facets makes agroecology a good candidate for participatory research. Information technology should help using this information for the production of structured scientific knowledge. In this respect, there is a need for information technology that is adaptive to encompass the diversity of within and between systems and that provide benefit to farmers that feed it with data. We present MiCampoApp, a webapp that aims to join participatory research and certification in agroecology, with two roles, the farmer and the administrator. The idea to join participatory research for decision support with certification for market differentiation in single system is that much of the certification data harbors information for scientific research, and this incentivize data collection for the interest of the farmer and the community. The administrator create a model for data collection in order to solve a research question of interest for a communities or to produce traceability information to feed participatory guarantee system in a community. The farmer collects information using simple icons and produces traceability pages for research or certification purposes.

Chitosan arouses large interest due to its properties and possible applications. Every year the number of publications and patents based on this polymer increases. Chitosan exhibits poor solubility in neutral and basic media limiting its use in such conditions. Another serious obstacle is directly related to its natural origin. Chitosan is not a single polymer with a defined structure but a family of molecules with differences in their composition, size, and monomer distribution. These properties have a fundamental effect on the biological and technological performance of the polymer. Moreover, some of the biological properties claimed are discrete. In this review, we discuss how chitosan chemistry can solve the problems related to its poor solubility and can boost the polymer properties. We focus on some of the main biological properties of chitosan and the relationship with the physicochemical properties of the polymer. Then, we visit two polymer applications related to green processes: the use of chitosan in the green synthesis of metallic na-noparticles and its use as support in biocatalyst. Finally, we briefly describe how making use of the technological properties of chitosan it is possible to develop a variety of systems for drug delivery

Susceptible, infective, recovered, and hospitalized/isolated individuals are placed on the cells of a nxn square lattice, where each cell is occupied by a single individual or is vacant. At discrete time units (typically one day each) all susceptibles and infectives execute a random movement and when a coincidence of the two types occurs the susceptible is converted to infective status according to some probability. Infectives are labelled by the number of days since originally infected. At each time increment the age label of the infectives is increased by one unit. When the label reaches a number like 15 or 20 days the susceptibles recover with some probability or become isolated/hospitalized. Upon reaching some age the latter types either recover or die. Probabilities for the movements and conversions from one status to another are implemented by random numbergeneration. Simulations were carried out to investigate the effect of several probability and age parameters, the size of population (proportional to nxn) and density (related to fraction of occupied cells), and the size of the movements. Mid-term gradual conversion of susceptibles to isolated was explored as intervention policy.

The paper demonstrates the analysis of Corona Virus Disease based on a CNN probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed Convolution neural network structure. The Study is validated on 2002 chest X-ray images with 60 confirmed positive covid19 cases and (650 bacterial – 412 viral -880 normal) x-ray images. The proposed CNN compared with traditional classifiers with proposed CHFS feature extraction model. The experimental study has done with real data demonstrates the feasibility and potential of the proposed approach for the said cause. The result of proposed CNN structure has been successfully done to achieve 98.20% accuracy of covid19 potential cases with comparable of traditional classifiers.

In this paper, we present a framework to study the spatial structure of noctilucent clouds formed by ice particles in the upper atmosphere at mid and high latitude during summer. We study noctilucent cloud activity in optical images taken from three different locations and under different atmospheric conditions. In order to identify and distinguish noctilucent cloud activity from other objects in the scene, we employ linear discriminant analysis (LDA) with feature vectors ranging from simple metrics to higher-order local autocorrelation (HLAC), and histogram of oriented gradients (HOG). Finally, we propose a Convolutional Neural Networks (CNN) based method for the detection of noctilucent clouds. The results clearly indicate that the CNN based approach outperforms LDA based methods used in this article. Furthermore, we outline suggestions for future research directions to establish a framework that can be used for synchronizing the optical observations from ground based camera systems with echoes measured with radar systems like EISCAT in order to obtain independent additional information on the ice clouds.

Phenotypic studies require large datasets for accurate inference and prediction. Collecting plant data in a farm can be very labor intensive and costly. This paper presents the design, architecture (hardware and software) and deployment of a distributed modular agricultural multi-robot system for row crop field data collection. The proposed system has been deployed in a soybean research farm at Iowa State University.

Like other ICT communities, sustainability in software engineering is a major research and development concerns. Current research focusses on eliciting the meanings of sustainability and proposing approaches for its engineering and integration into the mainstream software development lifecycle. However, few concrete guidelines that software designers can apply effectively are available and applicable. Such guidelines are needed for the elicitation of sustainability requirements and testing software against these guidelines. This paper introduces a sustainability design catalogue to assist software developers and managers in eliciting sustainability requirements, and then in measuring and testing software sustainability. The paper reviews the current research on sustainability in software engineering which is the grounds for the development of the catalogue. Four different case studies were analyzed using the Karlskrona manifesto on sustainability design. The output from this research paper is a software sustainability design catalogue through which a pilot framework is proposed that includes a set of sustainability goals, concepts and methods. The integration of sustainability for/in software systems requires a concrete framework that exemplifies how to apply and quantify sustainability. The paper demonstrates how the proposed software sustainability design catalogue provides a step towards this direction through a series of guidelines.

Personalized medicine is an aspect of the P4 medicine (predictive, preventive, personalized and participatory) based precisely on the customization of all medical characters of each subject. In personalized medicine, the development of medical treatments and drugs is tailored to the individual characteristics and needs of each subject, according to the study of diseases at different scales from genotype to phenotype scale. To make concrete the goal of personalized medicine, it is necessary to employ high-throughput methodologies such as Next Generation Sequencing (NGS), Genome-Wide Association Studies (GWAS), Mass Spectrometry or Microarrays, that are able to investigate a single disease from a broader perspective. For example, by using genotyping microarrays (e.g. collections of Single Nucleotide Polymorphism - SNP) it is possible to uncover the reasons (i.e. mutation in genes) because a treatment works properly in some patients (for example absence of mutated genes), but it does not work (presence of mutated genes) in others. A side effect of high-throughput methodologies is the massive amount of data produced for each single experiment, that poses several challenges (e.g. high execution time and required memory) to bioinformatic software. Thus a main requirement of modern bioinformatic software is the use of good software engineering methods and efficient programming techniques, able to face those challenges, that include the use of parallel programming and efficient and compact data structures. Thus, to exploit all the potential of this massive amount of data in the short possible time (before that data becomes obsolete), the necessity to develop parallel software tools for efficient data collection and analysis arise. Moreover, due to the heterogeneity of the data produced by the different kinds of experimental platforms, it is necessary to automatize in a comprehensive software pipeline, the various steps that compose a bioinformatic analysis, such as: the preprocessing of raw data to remove noise or corrupted data; the annotation of data with external knowledge (e.g. Gene Ontology), and the integration of molecular data with clinical data. It should be noted that such steps are necessary to make statistical or data mining analysis more effective. This paper presents the design and the experimentation of a comprehensive software pipeline, named microPipe, for the preprocessing, annotation and analysis of microarray-based SNP genotyping data. A case study in pharmacogenomics is presented. The main advantages of using microPipe are: the reduction of errors that may happen when trying to make data compatible among different tools; the possibility to analyze in parallel huge datasets; the easy annotation and integration of data.

Monochromatic documents claim for much less computer bandwidth for network transmission and storage space than their color or even grayscale equivalent. The binarization of historical documents is far more complex than recent ones as paper aging, color, texture, translucidity, stains, back-to-front interference, kind and color of ink used in handwritting, printing process, digitalization process, etc. are some of the factors that affect binarization. This article presents a new binarization algorithm for historical documents. The new global filter proposed is performed in four steps: filtering the image using a bilateral filter, splitting image into the RGB components, decision-making for each RGB channel based on an adaptive binarization method inspired by Otsu's method with a choice of the threshold level, and classification of the binarized images to decide which of the RGB components best preserved the document information in the foreground. The quantitative and qualitative assessment made with 21 binarization algorithms in three sets of “real world” documents showed very good results.

Human non-conscious reasoning is one of the most successful procedures developed to solve everyday problems in an efficient way. This is why the field of artificial intelligence should analyze, formalize and emulate the multiple ways of non-conscious reasoning with the purpose of applying them in knowledge based systems, neurocomputers and similar devices for aiding people in the problem-solving process. In this paper, a framework for those non-conscious ways of reasoning is presented based on object-oriented representations, fuzzy sets and multivalued logic.

Recent advancements highlight the utilization of vegetable oils as additives in polymeric materials, particularly for replacing conventional plasticizers. Buriti oil (BO), extracted from the Amazon's Mauritia flexuosa palm tree fruit, boasts an impressive profile of vitamins, minerals, proteins, carotenoids, and tocopherol. This study investigates the impact of incorporating buriti oil as a plasticizer in linear low-density polyethylene (LLDPE) matrices. The aim of this research was to evaluate how buriti oil, a bioactive compound, influences the thermal and rheological properties of LLDPE. Compositions of buriti oil\LLDPE were prepared via melt intercalation techniques, and the resulting materials were characterized through Thermogravimetric Analysis (TGA), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), mechanical property testing, and contact angle measurement. The addition of buriti oil was found to act as a processing aid and plasticizer, enhancing the fluidity of LLDPE polymer chains. TGA revealed distinct thermal stabilities for buriti oil/LLDPE under different degradation conditions Notably, buriti oil exhibited an initial weight loss temperature at 402°C, whereas LLDPE at 466.4°C. This indicated a minor reduction in the thermal stability of buriti oil/LLDPE compositions. The thermal stability, as observed through DSC displayed a nuanced response to the oil's incorporation, suggesting a complex interaction between the oil and polymer matrix. Detailed mechanical testing indicated a marked increase in tensile strength and elongation at break, especially at optimal concentrations of buriti oil. SEM analysis showcased a more uniform and less brittle microstructure, correlating with the enhanced mechanical properties. Contact angle measurements revealed a notable shift in surface hydrophobicity, indicating a change in the surface chemistry. This study demonstrates that buriti oil can positively influence the processability and thermal properties of LLDPE contributing to expand its potential applications as an effective plasticizer.

To obtain an accurate and reproducible experimental results in microbial fuel cell (MFC), it is important to know ‘anode maturation biofilm’ to produce a stable and maximum performance. For this purpose, four single chamber MFCs were tested in this study. The linear sweep voltammetry (LSV) polarization tests illustrated that maximum power densities of three MFCs became stable after 9 weeks. Although there were variations afterwards, such variations were negligible. Average maximum power densities from the 9th to the 17th week were 2,990 mW/m2 (MFC-4), 2,983 mW/m2 (MFC-2), 2,368 mW/m2 (MFC-3) and 837 mW/m2 (MFC-1). Polarization resistance shows that MFC-1 had much larger anode resistance (36.6-85.4 Ω) than the other MFCs (1.7-11.6 Ω). Anodic cyclic voltammetry (CV) shows that current production increased over time and MFC-1 had much smaller current production (24.4 mA) than the other MFCs (31.0-34.9 mA) at 17th week. The increased current production indicates anode biofilm became more mature over time, but overall cell performance did not increased accordingly. Possibly due to the bad inoculation, MFC-1 showed the lowest performance. However, its performance was restored to the initial performance and anode resistance was reduced by 47% at 17th week. This study shows that the optimum anode maturation time is 9 weeks and that bioanode performance is a key factor for MFC performance. This study also shows than LSV polarization and CV tests are accurate and non-destructive measurement methods for diagnosing anode performance.

The coronaviral pandemic has led to a shift in traditional teaching methods to more innovative approaches, such as high-fidelity patient simulation (HFPS), which can improve students’ clinical judgement and decision-making for quality patient care. A modified guideline was introduced to enhance students’ satisfaction and self-confidence in learning through HFPS. The study involved 189 baccalaureate nursing students, with 92 in the intervention group and 97 in the control group. The intervention group received the modified HFPS guideline, while the control group received standard treatment with basic instruction. After the HFPS debriefing session, students provided narrative feedback on their learning experience. The quantitative results showed that students in the intervention group reported a significant improvement in satisfaction and self-confidence in learning compared to the control group. The modified HFPS guideline provided clear guidance for students to learn and apply knowledge and skills more effectively, leading to increased engagement during interactive simulation sessions. The results suggest that the HFPS guideline should be added to the curriculum to enhance students’ satisfaction and self-confidence in learning, even in junior students. Innovative teaching methods, such as HFPS, can be necessary and beneficial for healthcare professional training, after the pandemic.

We live in a complex world characterised by complex people, complex times, and complex social, technological, and ecological environments. There is clear evidence that governments are failing at most public matters. The recent COVID-19 pandemic is a high example of global governance failure both at preventing such pandemics and managing the COVID-19 pandemic. It is time that all of us take responsibility and look into ways of collaboratively improving the governance of public matters, our matters. While there are many reasons for government failures, we believe the lack of information availability is a fundamental reason that limits the government’s ability to act smartly and allows the lack of transparency to creep into policy and action leading to corruption and failure. To this end, this paper introduces the concept of deep journalism, a data-driven deep learning-based approach for discovering multi-perspective parameters related to a topic of interest. We build three datasets (a newspaper, a technology magazine, and a Web of Science dataset) and discover the academic, industrial, public, governance, and political parameters for the transportation sector as a case study to introduce deep journalism and our tool DeepJournal (Version 1.0) that implements our proposed approach. We elaborate on 89 transportation parameters and hundreds of dimensions reviewing 400 technical, academic, and news articles. The findings related to the multi-perspective view of transportation reported in this paper show that there are many important problems seen by the public that industry and academia seem to not place their focus on. On the other hand, academia produces much broader and deeper knowledge on the subject such as a wide range of pollutions affecting the people and planet do not get to reach the public eye. Our deep journalism approach could find the gaps and highlight them to the public and other stakeholders.

Climate change may cause organisms to seek thermal refuge from rising temperatures, either by shifting their ranges or seeking microrefugia within their existing ranges. We evaluate the potential for thermal stratification to provide refuge for two fish species in the San Francisco Estuary (SFE): Chinook Salmon (Oncorhynchus tshawytscha) and Delta Smelt (Hypomesus transpacificus). We compiled water temperature data from multiple monitoring programs to evaluate spatial, daily, hourly, intra-annual, and inter-annual trends in stratification using generalized additive models. We used our data and models to predict the locations and periods of time that the bottom of the water column could function as thermal refuge for salmon and smelt. Periods in which the bottom was cooler than surface primarily occurred during the peak of summer and during the afternoons, with more prominent stratification during warmer years. Although the SFE is often exceedingly warm for fish species and well-mixed overall, we identified potential for thermal refugia in a long and deep terminal channel for Delta Smelt, and in the periods bordering summer for Chinook Salmon. Thermal stratification may increase as the climate warms, and pockets of cooler water at depth, though limited, may become more important for at-risk fishes in the future.

A major obstacle faced by nanopore-based polymer sequencing and analysis is the high speed of translocation of an analyte (nucleotide, DNA, amino acid (AA), peptide) through the pore; the rate currently exceeds available detector bandwidth. Except for one method that uses an enzyme ratchet to sequence DNA, attempts to resolve the problem satisfactorily have been largely unsuccessful. Here a counterintuitive method based on reversing the pore voltage, and, with some analytes, increasing their mobility, is described. A simplified Fokker-Planck model shows a significant increase in translocation times for single nucleotides and AAs (up from ~10 ns to ~1 ms). More realistic simulations show that with a bi-level positive-negative pore voltage profile all four nucleotides in DNA (dAMP, dTMP, dCMP, and dGMP) and the 20 proteinogenic amino acids can be trapped inside the pore long enough for detection with bandwidths of ~1-10 Khz.

The energy loss of photons due to the cosmological red shift is interpreted, here, as a periodical process of transferring electromagnetic field energy into the space. The transferred energy portions are independent on photon energy if this transfer occurs with their frequency. The amounts of periodically released energy are so small that the related ultra-long wave length photons have to be understood as perfectly delocalized. Thus, the described point of view bridges the quantum micro cosmos with the macro cosmos. It is proposed to regard this energy exchange as a typical property of universal time arrow and to interpret the “large numbers”, in particular the dimensionless reciprocal of product of Hubbles constant and Planck time as the fundamental parameter describing cosmic evolution.

The global biodiversity crisis has emphasized the unique contribution of functional diversity to ecosystem function, stability, and resilience. In this regard, the increasing availability of remotely sensed data together with the development of new sensors and approaches has the potential to improve our ability to quantify and monitor ecosystem traits and functions at unprecedent spatial, temporal, and spectral scales. In particular, air- and spaceborne hyperspectral data are making possible the measurement of plant-level functional traits to investigate ecosystem function and functional diversity in novel ways. In this review we pose that these developments together with similar ones focusing on soils and lithologies can help us understand relationships between functional diversity, ecosystem processes, and landsliding, and more broadly the disturbance dynamics of mountainscapes. Acknowledging the challenges associated with mountainscapes, this review 1) synthesizes broad established methods to retrieve functional traits from remotely sensed data, 2) summarizes approaches derived from remotely sensed data to characterize functional diversity and its relationships with ecosystem functioning, and 3) elaborates on how these methods and approaches can provide a needed holistic view of landslides. This holistic view recognizes the critical role that interactions between ecosystem and geomorphic processes play in the dynamics of mountainscapes mediated by landslides and the contribution of ecosystem diversity and processes to landslide susceptibility and recovery. In this “ecosystem-centered” view of landslides it might be necessary to scale from individual landslides and sites to entire landslide populations, assemblages, and landscapes.

Medicinal activities carried out within hospitals lead to a high consumption of antibiotics and subsequently result in a high excretion rate of antibiotic residues. These antibiotics consumed in human medicine are not fully absorbed by the body and are often discharged into the environment as biological waste from infected human patients. The significant water consumption in hospitals and the pharmaceutical impact on wastewater from healthcare facilities facilitate the emergence and spread of antibiotic-resistant bacteria and antibiotic-resistant genes in the environment. Hospital effluents could be doubly involved in the dissemination of antibiotic molecules and multidrug-resistant bacteria in various ecosystems. The objective of this study is to characterize hospital effluents by assessing antibiotic concentrations and the diversity of antibiotic-resistant bacteria in these hospital wastewaters and within the environment (aquatic environments; rivers), as well as to inventory the bacteria present and those carrying antibiotic resistance in both hospital wastewater and the environment.

Fish species diversity in the Black Sea is affected by the introduction of non-indigenous species, particularly through the process of "mediterranization" and the expansion of the ranges of invasive species. In August 2023, we documented an increasing quantity of four non- indigenous sparid species, namely Spicara smaris (Linnaeus, 1758), Diplodus puntazzo (Walbaum, 1792), Diplodus annularis (Linnaeus, 1758), and Lithognathus mormyrus (Linnaeus, 1758), in the coastal Bulgarian waters of the Black Sea, based on citizen science data obtained from small-scale fisheries and pelagic trawlers. However, in the autumn of 2022, only small quantities of S. smaris were collected periodically, indicating significant fluctuations in abundance and occasional presence. The other three species were recorded in August 2023 in the central coastal region near the Fandakliyska River, at depths of less than 10 m. This brief communication presents information on the identified species, including sampling locations and biological data, and examines the sparid species records in the Black Sea. In addition, we reviewed recent studies on the distribution and biological traits of the identified species in their natural Mediterranean habitats.

Background: IoT and Big Data are newer technologies that can provide substantial support for healthcare systems and help overcome their shortcomings. The aim of this paper was to analyze literature production descriptively, thematically, and chronologically from an interdisciplinary perspective in a holistic way to identify the most prolific research entities and themes. Methods: The synthetic knowledge synthesis qualitatively and quantitatively analyzes the production of literature through a combination of descriptive bibliometrics, bibliometric mapping and content analysis. For this analysis the Scopus bibliometric database was used. Results: In the Scopus database, 2272 publications were found, which were published between 1985 and June 10, 2024. The first article in this field was published in 1985. Until 2012 the production of literature was steady increasing, after that exponential growth began, which reached its peak in 2023. The most productive countries were the USA, India, China, the United Kingdom, South Korea, Germany and Italy. The content analysis resulted in 8 themes (four from the perspective of computer science and four from the perspective of medicine) and 21 thematic concepts (eight from the perspective of computer science and 13 from the perspective of medicine). Conclusions: The results show that IoT and Big Data have become key technologies in preventive health. The study outcomes might represent a starting point for the further development of research that combines multidisciplinary aspects of healthcare.

Recently two new degree concepts have been defined in graph theory: ev-degree and ve-degree. Also the ev-degree and ve-degree Zagreb and Randić indices have been defined very recently as parallel of the classical definitions of Zagreb and Randić indices. It was shown that ev-degree and ve-degree topological indices can be used as possible tools in QSPR researches . In this paper we define the ve-degree and ev-degree Narumi–Katayama indices, investigate the predicting power of these novel indices and extremal graphs with respect to these novel topological indices. Also we give some basic mathematical properties of ev-degree and ve-degree Narumi-Katayama and Zagreb indices.

The giant octopus caught off the east coast of Korea is an important fishery resource with the largest haul and is mostly caught using net pots. While giant octopus pots have different shapes, drum, rectangular, and cylinder pots are mostly used. However, these pots have not been thoroughly evaluated. Here, we evaluated the fishing performance of three giant octopus pots, their fluid resistance, and underwater movement. Six sea trials were conducted near the Hupo region in Gyeongsangbuk-do. In total, 163 individuals (62,724 g), 190 individuals (49,913 g), and 154 individuals (52,364 g) were caught using drum, rectangular, and cylinder pots, respectively. Drum pots showed significant differences in the amount of catch by weight (ANOVA test, p&lt;0.05). Moreover, the percentage of minimum conservation size was the lowest in cylinder pots (6.5%). Drum pots showed the highest stability in the underwater stability test, while underwater fluid resistance at 2.5 kt fluid speed was in the following order: drum pots (7.66 kgf) &gt; rectangular pots (7.16 kgf) &gt; cylinder pots (7.04 kgf). Therefore, drum pots showed excellent fishing performance. This study provides foundational data for developing managing systems for giant octopus resources and establishing the extent of fishing gear usage.

Biochar has a carbon-based porous structure that is advantageous for adsorbing air pollutants. The pore structure of biochar ranges from several tens to hundreds of m²/g depending on the production temperature. We compared the pore characteristics of biochar with its NOx and SOx adsorption performances to evaluate its adsorbing potential. Biochar was produced from cypress biomass via slow pyrolysis in a fixed-bed reactor and CO2 partial gasification. Slow pyrolysis experiments at 500–700°C were conducted to produce biochar, followed by yield, proximate, elemental, and surface area analyses. CO2 partial gasification activated the biochar produced by slow pyrolysis at the same temperature, and similar analyses were performed. NO and SO2 adsorption experiments were conducted separately and together for biochar produced under each condition. Biochar produced at 600°C showed the highest specific surface area. CO2 partial gasification increased the surface area at each temperature, reaching 680.3 m²/g at 700°C. In single adsorption experiments, N2 carbonized biochar showed NO and SO2 adsorption efficiencies of 84–90% and 88.2–91.4%, respectively, with similar trends in simultaneous adsorption experiments. CO2 activated biochar exhibited higher adsorption efficiencies due to its increased surface area, with NO and SO2 adsorption at 89.8–93.9% and 92–98.9%, respectively. Biochar activated at 700°C simultaneously adsorbed approximately 99% NO and SO2. The NO adsorption efficiency increased with increasing surface area of the small pores, whereas the SO2 adsorption efficiency improved with the development of both small and large pores.

Abstract: This study explores the antibacterial and antifungal activity of four floral honey types (12 samples) (A. seyal, Zizuphus spina-christi, Cucurbita maxima and A. nilotica) as related to their phenolic content, antioxidant capacity, and assessing their cytotoxic effects. The cup-plate agar diffusion assay was employed to examine the antimicrobial potency against different pathogens including Bacillus subtilis, Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Aspergillus niger and Candida albicans. Scavenging activity, total phenolic, flavonoid contents, and cytotoxicity were determined by the DPPH radical scavenging, Folin–Ciocalteu, spectrophotometry, and Brine shrimp lethality, bioassays respectively. All assessed honeys significantly inhibited the growth of the tested pathogens. Both Acacias honey exhibited high antioxidants with IC50: 6.68 and 9.08 mg/ml respectively for A. seyal and A. nilotica. The total phenol content varied from 5.75 to 67.95 mg (GAE)/100 g while the overall flavonoid content varied from 0.15 to 0.5mg (QE)/100 g. All honey types expressed no cytotoxicity effects one cell lines. Thus the study, supports the existing antimicrobial activity of honey. However, the strong antimicrobial activity of Cucurbita and Ziziphus honeys, with their corresponded poor antioxidant furtherly provides evidence that the high levels of phenolic substances in honeys is not always the only cause of their antimicrobial potency.

This paper summarizes the estimates of the total changes in sales, expenses, and income of participants of the horticulture research and extension programs at the Mississippi State University - Coastal Research and Extension Center for the past five years. Major items outline the estimation procedures for the past five years. The average annual values were used in estimating the total economic impacts of added gross sales, expenses, and incomes of participants in horticulture events. The cumulative total impacts reach $8.7 million in sales, 76 jobs, $1.4 million in labor income, $2.4 million in value-added, and $0.4 million in local, state, and federal taxes. In addition, the total willingness to pay for the horticulture program by the adult participants reached $1.8 million. In comparison, the annual public spending on the horticulture program averaged $1.4 million, creating additional substantial economic impacts to the region.

Purpose: Nycthemeral (24-hour) glaucoma inpatient intraocular pressure (IOP) monitoring has been used in Europe for more than 100 years to detect peaks missed during regular office hours. Data supporting this practice is lacking, partially because it is difficult to correlate manually drawn IOP curves to objective glaucoma progression. To address this, we deployed automated IOP data extraction tools and tested for a correlation to a progressive retinal nerve fiber layer loss on spectral-domain optical coherence tomography (SDOCT). Methods: We created and deployed a machine-learning image analysis software to extract IOP data from hand-drawn, nycthemeral IOP curves of 225 retrospectively identified glaucoma patients. The relationship between demographic parameters, IOP and mean ocular perfusion pressure (MOPP) data to SDOCT data was analyzed. Sensitivities and specificities for the historical cut-off values of 15 mmHg and 22 mmHg in detecting glaucoma progression were calculated. Results: IOP data could be extracted efficiently. The IOP average was 15.2±4.0 mmHg, nycthemeral IOP variation was 6.9±4.2 mmHg, and MOPP was 59.1±8.9 mmHg. Peak IOP occurred at 10 AM and trough at 9 PM. Disease progression occurred mainly in the temporal-superior and -inferior SDOCT sectors. No correlation could be established between demographic, IOP, or MOPP parameters and SDOCT disease progression. The sensitivity and specificity of both cut-off points (15 and 22 mmHg) were insufficient to be clinically useful. Outpatient IOPs were non-inferior to nycthemeral IOPs. Conclusion: IOP data obtained during a single visit make for a poor diagnostic tool, no matter whether obtained using nycthemeral measurements or during outpatient hours.

Insects quickly develop resistance to conventional chemical insecticides. The use of silica nanoparticles (SiO₂NPs) is a promising new approach for managing insect pests. The insects that have been studied are, in most cases, external feeders and internal leaf feeders have not been well studied. Here, we investigate the efficiency of SiO₂NPs in controlling the American serpentine leafminer Liriomyza trifolii (Diptera: Agromyzidae), a devastating insect pest of a wide range of crops. SiO₂NPs at concentrations of 50, 100, 200 and 400 mg/L compared to a control of distilled water were applied to intact Phaseolus vulgaris leaves by spraying to evaluate their effects, via the plant, on the number, survival, developmental/feeding speed, body mass/size and sex ratio of L. trifolii released after spraying. A qRT-PCR analysis was conducted to assess oxidative stress in L. trifolii based on the gene expression level of the two major antioxidant enzymes; catalase and superoxide dismutase. The number of mines, survival rate and puparia weight were lower than the control at the highest concentration of SiO₂NPs, while pupariation was delayed at most of the concentrations studied and puparium weight/size decreased at low concentrations. Sex ratio was not affected by SiO₂NPs. Gene expression levels of the antioxidant enzymes at the pupal stage were not significantly affected by SiO₂NPs at any concentration but some individuals showed up-regulation at low SiO₂NP concentrations, inferring development of resistance. This study demonstrated that a priori application of the highest concentration of SiO₂NPs to plants effectively reduces a leafminer population but that application of even the lowest concentration can decrease the life history trait values of surviving leafminers.