Aim: This study aimed to understand the role of workforce agility as a coping strategy to curtail the COVID-19 pandemic. Method: The study reviewed and integrated two lines of literature to develop a theoretical framework to help business organizations to respond, adapt, and recover from the COVID-19 pandemic crisis. Results: The results showed that there are several commonalities between the workforce agility literature and the crisis management literature, mostly manifested in the speed of responding to uncertain and unpredictable, adverse events in the business environment. A thorough review of key themes in both kinds of literature led to developing a theoretical framework that links crisis drivers, crisis enablers, crisis capabilities, and response strategy. Workforce agility can be used as a coping strategy to deal with the COVID-19 pandemic crisis. Both kinds of literature evolve to respond to any crisis; the crisis management literature focuses on Meso-level (organizational level) or collective actions to mitigate the crisis, while the workforce agility literature focuses on the Micro-level (individual level). However, the successful response to any crisis requires the synergy of both actions. Future Studies: The results of this research are of prime importance for both researchers and practitioners. Researchers can use the theoretical framework as a baseline for building empirical research design. The current study also sets the context for future researchers who are interested in workforce agility to test the validity of agile work behaviors in real adverse context. It also opens a new stream of debate by deconstructing understudied links between workforce agility, resilience, and crisis management. Mangerial implication: For practitioners to ensure a successful response to any impending crisis, they must develop their employees’ agile behaviors. Practitioners can also redesign their recruitment strategy to select the right individuals with the right agile attributes, abilities, and behaviors to easily alter their behaviors according to the progressing crisis phases. Finally, practitioners can design continual learning interventions to include required agile skills and competencies to prepare their employees for future crises.

Porcine circovirus-associated diseases, caused by porcine circovirus type 2 (PCV2), are widespread and result in significant economic losses to the global swine industry. It is generally believed that PCV2 can be divided into nine genotypes (PCV2a to PCV2i), with the dominant being the PCV2d genotype. In this study, 2675 samples from pig farms in 13 cities in Jiangsu Province, China, were collected between 2014 and 2021 and subjected to polymerase chain reaction analysis to investigate the prevalence and genetic diversity of PCV2. The results showed that 41.42% (1108/2675) of samples tested positive for PCV2. The researchers further analyzed the genetic characteristics of 251 PCV2 strains and found that they belonged to the following four genotypes: PCV2a, PCV2b, PCV2d, and PCV2i. The dominant genotype was PCV2d, with a prevalence of 49.80% (125/251). The detection rate of PCV2b was significantly higher than those of PCV2a and PCV2i, at 35.46% (89/251), 7.57% (19/251), and 7.17% (18/251), respectively. We have further revealed the fingerprint of PCV2i genomic nucleotides for the first time. In conclusion, this study illustrates the high prevalence and evolutionary features of PCV2 in Jiangsu Province over the past few years.

This study aimed to determine whether the EQ-5D-5L tool captures the most common persistent symptoms, such as fatigue, memory/concentration problems and dyspnea in patients with post COVID-19 conditions, and if adding these symptoms improves the explained variance of the health-related quality of life (HRQoL). In this exploratory cross-sectional study, two cohorts of Swedish patients (n=177) with a history of COVID-19 infection answeared a questionnaire covering sociodemographic characteristics, clinical factors and HRQoL assessed using EQ-5D-5L with the Visual Analogue Scale (EQ-VAS). Spearman rank correlation and multiple regression analyses were employed to investigate the extent to which the most common persistent symptoms such as fatigue, memory/concentration problems and dyspnea were explained by the EQ-5D-5L. The explanatory power of EQ-5D-5L for EQ-VAS was also analysed, both with and without inclusion of symptom(s). We found that the EQ-5D-5L dimensions partly captured fatigue and memory/concentration problems but performed poorly in capturing dyspnea. Specifically, the EQ-5D-5L explained 55% of the variance in memory/concentration problems, 47% in fatigue and only 14% in dyspnea. Adding fatigue to the EQ-5D-5L increased the explained variance of the EQ-VAS by 5.7%, while the addition of memory/concentration problems and dyspnea had a comparatively smaller impact on the explained variance. Our study highlights the EQ-5D-5L’s strength in capturing fatigue and memory/concentration problems in post COVID-19 patients. However, it also underscores the challenges in assessing dyspnea in this group. Fatigue emerged as a notably influential symptom, significantly enhancing the EQ-5D-5L's predictive ability to for the EQ-VAS score in these patients.

Inositol is a natural carbocyclic sugar that plays an essential role in regulating the vital cellular functions of plants and animals. Current research has explored methyl derivatives of inositol, reporting on their various biological activities, including antitumor, anti-inflammatory, and anti-osteoporosis activities. Our previous study demonstrated that L-quebrachitol, a methyl derivative of inositol, enhances osteoblastogenesis and bone formation; however, its effect on osteoclastogenesis remains unclear. Consequently, we aimed to investigate the effect of L-quebrachitol on the receptor activator of nuclear factor-κB ligand-induced osteoclastogenesis in pre-osteoclastic RAW 264.7 cells. The results revealed that L-quebrachitol suppressed RANK-mediated signalling, including nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and Fos proto-oncogene (cFOS) pathways, at both gene and protein levels. Moreover, the critical transcription factor for osteoclastogenesis, nuclear factor of activated T cells c1 (NFATc1), was downregulated. Inhibition of osteoclast-associated marker genes encoding proteolytic enzymes, such as tartrate-resistant acid phosphatase (TRAP), matrix metallopeptidase 9 (MMP-9), and cathepsin K, led to reduced formation of TRAP-positive multinucleated cells and resorption pits. These findings demonstrate the dose-dependent inhibitory effect of L-quebrachitol on osteoclastogenesis through the modulation of receptor activator of nuclear factor κB (RANK)-mediated signalling pathways. However, further exploration of L-quebrachitol's potential as a therapeutic approach for osteoporosis is warranted.

Summer annual species are the most widely grown crops in the Upper Midwest, but reliance on a summer annual system has led to excessive soil and nutrient loss when crops are not present. Integrating winter rye (Secale cereale L.), known for its hardiness and environmental benefits, into the rotation can address these issues. However, there's limited understanding of the phenotypic diversity in winter rye during key growth stages that overlap with the typical planting dates for corn (Zea mays L.), soybean [Glycine max (L.) Merr], and late-planted crops like dry bean (Phaseolus vulgaris L.). Thus, the objectives of this study were to evaluate changes in biomass accumulation and quality of winter rye and triticale (x Triticosecale Wittmack) cultivars at three growth stages. Twenty cultivars were assessed over the 2014-2015 and 2015-2016 growing seasons across three environments in Minnesota. Winter rye and triticale were harvested for biomass at the tillering, booting, and soft dough growth stages and rye biomass yield and quality [i.e., crude protein, neutral detergent fiber (NDF), and 48-hour digestibility (NDFD)] were determined following harvest. Overall, there was little variation in each parameter among the cultivars within a location and growth stage. NDFD had some variation at the soft dough stage where digestibility ranged from 273 to 324 g kg-1 at the Crookston site and 332 to 406 g kg-1 at the St. Paul site. The lack of difference between cultivars indicates a wide range of choice when selecting a winter rye cultivar.

Differential Evolution (DE) is an evolutionary algorithm that has become increasingly popular due to its effectiveness and efficiency. However, its adaptation for multi-objective optimization requires further investigation. To address this issue, a novel algorithm called ILSDEMO has been proposed, which incorporates indicator-based selection and local search into a self-adaptive DE. ILSDEMO employs an archive population to store non-dominated solutions, generating an initial population uniformly distributed over the feasible solution space using orthogonal design. Additionally, two variants of DE are used to expedite convergence, while q-Gaussian mutation is utilized to exploit better trial individuals. The k-nearest neighbor rule is used to eliminate crowded solutions, and indicator-based selection is employed to generate a new parent population without diversity preservation. The performance of ILSDEMO was investigated on the test instances from the ZDT series and DTLZ series in terms of the selected indicators. The results suggest that ILSDEMO accurately and evenly approximates the true Pareto front compared to NSGAII, IBEA, and DEMO.

In today's technologically advanced landscape, sensors feed critical data for accurate decision-making and actions. Ensuring the integrity and reliability of sensor data is paramount to system performance and security. This paper introduces an innovative approach utilizing discrete wavelet transforms (DWT) embedded within microcontrollers to scrutinize sensor data meticulously. Our methodology aims to detect and isolate malfunctions, misuse, or any anomalies before they permeate the system, potentially causing widespread disruption. By leveraging the power of wavelet-based analysis, we embed computational intelligence directly into the microcontrollers, enabling them to monitor and validate their outputs in real-time. This proactive anomaly detection framework is designed to distinguish between normal and aberrant sensor behaviors, thereby safeguarding the system from erroneous data propagation. Our approach significantly enhances the reliability of embedded systems, providing a robust defense against false data injection attacks and contributing to overall cybersecurity.

In the era of the post-quantum Internet of Things (IoT), the implementation of anti-quantum cryptographic algorithms in numerous terminals can successfully defend against prospective quantum computing assaults. Lattice-based cryptography can withstand quantum computing attacks, making it a viable substitute for the currently prevalent classical public-key cryptographic technique. Nevertheless, the algorithm’s significant time complexity will result in a substantial computational burden on the edge computing chip in the IoT terminal. The computation of polynomial multiplication is the most demanding task in lattice-based cryptographic algorithms. Therefore, the investigation into efficient methods for calculating polynomial multiplication is highly important. Quick number theory transformations (NTT) are a widely employed technique to accelerate polynomial multiplication. This study presents a hardware implementation of an efficient number theory transformation. We utilize a multi-level pipeline architecture in the design to accomplish parallel calculations and execute it on a low-profile Artix7-XC7A100T FPGA device. The performance evaluation results demonstrate that our implementation significantly enhances performance and reduces resource usage compared to other existing proposals on the same platform. We suggested NTT core can be implemented in edge computing chips to enhance computational speed due to its small and low-latency design. The experimental results show that the proposed design, which supports both NTT and inverse NTT, achieves 417-Megahertz and consumes only 541-LUTs on Artix-7 XC7A100T.

The exceptional performance of graphene has driven the advancement of its preparation technique and application. Laser-induced graphene (LIG), as a novel graphene preparation technique, has been applied in various fields. Graphene periodic structures created by LIG technique exhibit superhydrophobic characteristics and can be used for deicing and anti-icing applications, which are significantly influenced by the laser parameters. The laser surface treatment process was simulated by a finite element software analysis (COMSOL Multiphysics) to optimize the scanning parameter range, and the linear array surface structure were subsequently fabricated by the LIG technique. The generation of graphene was confirmed by Raman spectroscopy, and energy-dispersive X-ray spectroscopy. The periodic linear array structure were observed by Scanning electron microscopy (SEM) and confocal laser imaging (CLSM). In addition, CLSM testings, contact angle measurements, and delayed icing experiments were systematically performed to investigate the effect of scanning speed on surface hydrophobicity. The results show that the high quality and uniform graphene can be achieved using the laser scanning speed of 125 mm/s. The periodic linear array structures can obviously increase the contact angle and suppress delayed icing. Furthermore, these structures have the enhanced ability of the electric heating deicing, which can reach 100°C and 240°C within 15 s and within 60 s under the DC voltage power supply ranging from 3 to 7 V, respectively. These results indicate that the LIG technique can be developed to provide an efficient, economical, and convenient approach for preparing graphene and the hydrophobic surface array structure based on LIG has considerable potential for deicing and anti-icing applications.

Raman spectroscopy is a type of inelastic scattering that provides rich information about a sub-stance based on the coupling of the energy levels of their vibrational and rotational modes with incident light. It has been applied extensively in many fields. As there is an increasing need for remote detection of chemicals in planetary exploration and anti-terrorism, it is urgent to develop a compact and easily transportable fully automated remote Raman detection system for trace detection and identification of information with high-level confidence about the target’s compo-sition and conformation in real-time and for real field scenarios. Here, we present an unmanned vehicle-based remote Raman system, which includes a 266 nm air-cooling passive Q-switched nanosecond pulsed laser of high-repetition frequency, a gated ICMOS, and an unmanned vehicle. This system obtains good spectral signals from remote distances ranging from 3 m to 10 m for simulating realistic scenarios, such as aluminum plate, woodblock, paperboard, black cloth, and leaves, and even for detected amounts as low as 0.1 mg. Furthermore, a CNN-based algorithm is implemented and packaged into the recognition software to achieve fast and more accurate de-tection and identification. This prototype provides a proof-of-concept for an unmanned vehicle with accurate remote substance detection in real-time, which can be helpful for remote detection and identification of hazardous gas, explosives, their precursors, and so forth.

Agriculture is one of the indispensable fields for the survival of mankind. Potatoes also have a significant role in the field of agriculture. The quality and quantity of potatoes are significantly impacted by several diseases, such as early blight and late blight, and manual interpretation of these leaf diseases is time-consuming and inconvenient. Fortunately, leaf appearance is used to detect diseases in potato plants. Productivity will considerably rise if the infections are detected early. Different types of image processing methods and machine learning methods are used for early recognition of those diseases from leaf images so that the product losses are decreased significantly. For the incredible and marvelous performance of CNN, it is the most popular deep learning method that is used immensely for the recognition of leaf diseases from images. Several pretrained deep learning models, such as VGG16, ResNet50, InceptionV3, MobileNetV2, Xception, and a deep learning model developed using CNN, are employed for potato leaf disease classification and recognition on the same dataset. The transfer learning technique is applied to the pretrained model and the data augmentation technique is applied to the Proposed CNN model for potato disease classification from leaf samples. Compared to pretrained models, the proposed CNN model offers the lowest loss and highest accuracy for potato leaf disease detection while using fewer parameters and layers. It achieves the best performance with a test accuracy of 99.33% compared to other Pretrained models used in the diagnosis of potato leaf disease.

The use of visual signals in horticulture has attracted significant attention and encompassed a wide range of data types such as 2D images, videos, hyperspectral images, and 3D point clouds. These visual signals have proven to be valuable in developing cutting-edge computer vision systems for various applications in horticulture, enabling plant growth monitoring, pest and disease detection, quality and yield estimation, and automated harvesting. However, unlike other sectors, developing deep learning computer vision systems for horticulture encounters unique challenges due to the limited availability of high-quality training and evaluation datasets necessary for deep learning models. This paper investigates the current status of vision systems and available data in order to identify the high-quality data requirements specific to horticultural applications. We analyse the impact of the quality of visual signals on the information content and features that can be extracted from these signals. To address the identified data quality requirements, we explore the usage of a deep learning-based super-resolution model for generative quality enhancement of visual signals. Furthermore, we discuss how these can be applied to meet the growing requirements around data quality for learning-based vision systems. We also present a detailed analysis of the competitive quality generated by the proposed solution compared to cost-intensive hardware-based alternatives. This work aims to guide the development of efficient computer vision models in horticulture by overcoming existing data challenges and paving a pathway forward for contemporary data acquisition.

Localized surface plasmon resonance (LSPR) based sensors exhibit enormous potential in the areas of medical diagnosis, food safety regulation and environmental monitoring. While, the broadband spectral lineshape of LSPR hampers the observation of wavelength shifts in sensing processes, thus preventing their widespread applications in sensors. Here, we describe an improved plasmonic sensor based on Fano resonances between LSPR and Rayleigh anomaly (RA) in a metal-insulator-metal (MIM) meta-grating, which is constructed by silver nanoshells array, an isolation grating mask and a continuous gold film. The MIM configuration offers more freedoms to control the optical properties of LSPR, RA and the Fano resonance between them. Strong couplings between LSPR and RA form a series of narrowband reflection peaks (with a linewidth of ~20 nm in full width at half maximum (FWHM) and a reflectivity closing to 100%) within a LSPR based broadband extinction window in experiment, making the meta-grating promising for applications of high-efficiency reflective filters. While, a well-optimized Fano resonance between LSPR and RA by carefully adjusting the angles of incident light can switch such nano-device to an improved biological/chemical sensor with the figure of merit (FOM) large than 60 and capability for detecting the local refractive index changes caused by the bonding of target molecules on surface of the nano-devices. The figure of merit of hybrid sensor in detection of target molecules is 6 and 15 times higher than the simple RA and LSPR based sensors, respectively.

City zones have become increasingly overcrowded as a result of the extensive population widening ratio and the swift relocation of people from villages. The traffic monitoring process is a major issue in these areas due to the massive traffic flow on the roads. This research proposed a cluster-based improved authentication and communication scheme for an Intelligent Transportation System in Vehicular AdHoc Networks (VANETs). Our primary objective is to optimize resource sharing in vehicular communication. We enhanced the reliability, scalability, and stability of fast-moving VANETs by introducing cluster-based routing schemes for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. We used a third-party certification authority for vehicle authentication to address security and privacy concerns. Our proposed schemes support minimizing the end-to-end (E2E) delay and route request, in addition to reducing link failure. Our schemes’ leading yield includes throughput enhancement, TCP Socket Initialization time minimization, TCP handshake response speedup, and DNS lookup improvement. The schemes are centred on short-range peer-to-peer (P2P) wireless communication in a 400-meter radius cluster. This includes innovative P2P wireless communications on VANET using minimized resources. The proposed schemes deliver a secure authentication mechanism with a securely generated vehicle authentication key provided by a certification authority. Furthermore, we have developed RESTful APIs in vehicular communication for implementation purposes, and also offered and implemented algorithms for resource sharing regarding V2V and V2I communication. Ultimately, we evaluated the performance of our experiments.

We introduce a magnetic bead-based sample preparation scheme for enabling a Raman spectroscopic differentiation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) positive and negative samples. The beads were functionalized with the angiotensin-converting enzyme 2 (ACE2) receptor protein, which is used as recognition element to selectively enrich SARS-CoV-2 on the surface of the magnetic beads. Subsequent Raman measurements directly enable discriminating SARS-CoV-2 positive and negative samples. The proposed approach is applicable for other virus species, too, when the specific recognition element is exchanged. Series of Raman spectra were measured on three types of samples, namely SARS-CoV-2, Influenza A H1N1 virus and a negative control. For each sample type, eight independent replicates were considered. All spectra are dominated by the magnetic bead substrate and no obvious differences between sample types are apparent. In order to address the subtle differences in the spectra, we calculated different correlation coefficients, namely the Pearson coefficient and the Normalized Cross Correlation coefficient. By comparing the correlation with the negative control differentiating between SARS-CoV-2 and Influenza A virus is possible. This study provides a first step towards the detection and potential classification of different viruses with the use of conventional Raman spectroscopy.

Lithium batteries are widely used due to their advantages such as high energy density, stable performance, low pollution, and long recyclable life. Accurate SOC estimation is important for the use of lithium batteries. To address the problem of low accuracy of SOC estimation by traditional methods, this paper proposes a joint method of fractional order robust unscented Kalman filter (FORUKF) and robust unscented Kalman filter (RUKF) to estimate SOC. The method is based on a fractional order model (FOM) that combines the unscented transformation (UT) technique, the H∞ observer and the joint estimator. Specifically, an adaptive genetic algorithm (AGA) was first used to identify the parameters of the FOM for lithium batteries and to verify the accuracy of the model. Estimation and updating of the ohmic resistance R0 and the capacity QN in the model in real-time by RUKF and then estimation of the SOC by FORUKF. Finally, the accuracy of FORUKF-RUKF was verified under the Federal Urban Driving Schedule (FUDS), US06 Highway Driving Schedule and Beijing Dynamic Stress Test (BJDST). According to the results, the FORUKF-RUKF estimated SOC was found to have better accuracy and robustness than the RUKF, FOUKF and EKF.

The robust control of high precision electromechanical systems, such as micropositioners, is challenging in terms of the inherent high nonlinearity, the sensitivity to external interference, and the complexity of accurate identification of the model parameters. To cope with these problems, this work investigates a disturbance observer-based deep reinforcement learning control strategy to realize high robustness and precise tracking performance. Reinforcement learning has shown great potential as optimal control scheme, however, its application in micropositioning systems is still rare. Therefore, embedded with the integral differential compensator (ID), deep deterministic policy gradient (DDPG) is utilized in this work with the ability to not only decrease the state error but also improves the transient response speed. In addition, an adaptive sliding mode disturbance observer (ASMDO) is proposed to further eliminate the collective effect caused by the lumped disturbances. The sterling performance is revealed with intensive tracking simulation experiments and demonstrates the improvement in the accuracy and response time of the controller.

Human-Robot Interaction (HRI) for collaborative robots has become an active research topic recently. Collaborative robots assist the human workers in their tasks and improve their efficiency. But the worker should also feel safe and comfortable while interacting with the robot. In this paper, we propose a human-following motion planning and control scheme for a collaborative robot which supplies the necessary parts and tools to a worker in an assembly process in a factory. In our proposed scheme, a 3-D sensing system is employed to measure the skeletal data of the worker. At each sampling time of the sensing system, an optimal delivery position is estimated using the real-time worker data. At the same time, the future positions of the worker are predicted as probabilistic distributions. A Model Predictive Control (MPC) based trajectory planner is used to calculate a robot trajectory that supplies the required parts and tools to the worker and follows the predicted future positions of the worker. We have installed our proposed scheme in a collaborative robot system with a 2-DOF planar manipulator. Experimental results show that the proposed scheme enables the robot to provide anytime assistance to a worker who is moving around in the workspace while ensuring the safety and comfort of the worker.

Owing to their unique physicochemical properties, nanoparticles are used in a variety of ways in the field of cancer treatment, including imaging, drug delivery, and photothermal and photodynamic therapies. The fascinating properties of nanoparticles are determined by their size, morphology, and constituent elements, and various synthetic methods and post-synthetic techniques have been applied to control these factors. Herein, we present examples of shape and composition control through galvanic replacement, a technique that exploits redox potential differences between elements to induce spontaneous ion-exchange and highlight its specific contributions to cancer treatment applications. The present article identifies the recent advances in nanoparticle formation techniques and discusses the future outlook of the field.

The purpose of this study was to report data analysis results from the International Health Regulations (2005) Ship Sanitation Certificates (SSC), recorded in the European Information System (EIS). International sea trade and population movements by ships can contribute to the global spread of diseases. SSC are issued to ensure the implementation of control measures if public health risk exists on board. EIS designed according to the World Health Organization (WHO) “Handbook for Inspection of Ships and Issuance of SSC”. Inspection data were recorded and SSC issued by inspectors working at European ports were analysed. From July 2011-February 2017, 107 inspectors working at 54 ports in 11 countries inspected 5579 ships. Of these, there were 29 types under 85 flags (including 19 EU flags). As per IHR (2005) 10,281 Ship Sanitation Control Exception Certificates (SSCEC) and 296 Ship Sanitation Control Certificates (SSCC) were issued, 74 extensions to existing SSC were given, 7565 inspection findings were recorded, and 47 inspections were recorded without issuing an SSC. The most frequent inspection findings were the lack of potable water quality monitoring reports (23%). Ships aged ≥12 years (Odds Ratio, OR = 1.77, 95%Confidence Intervals, CI = 1.37–2.29) with an absence of cargo at time of inspection (OR = 3.36, 95%CI = 2.51–4.50) had a higher probability of receiving an SSCC, while ships under the EU flag had a lower probability of having inspection findings (OR = 0.72, 95%CI = 0.66–0.79). Risk factors to prioritise the inspections according to IHR were identified by using the EIS. A global information system, or connection of national or regional information systems and data exchange, could help to better implement SSC using common standards and procedures.

Intermediate temperature solid oxide fuel cell (SOFC) operation provides numerous advantages such as high combined heat and power (CHP) efficiency, potentially long-term materials stability, and the use of low-cost materials. However, due to the sluggish kinetics of the oxygen reduction reaction at intermediate temperature, the cathode of SOFC requires an efficient and stable catalyst. Significant progress in the development of cathode materials has been made over recent years. In this article, multiple strategies for improving the performance of cathode materials have been extensively reviewed such as A and B site doping of perovskites, infiltration of catalytic active materials, the use of core-shell composites, etc. Emphasis has been given to intrinsic properties such as thermal expansion compatibility, chemical and thermal stability, and oxygen transport number. Furthermore, to avoid any insulating phase formation at the cathode/electrolyte interface, strategies for interfacial layer modifications have also been extensively reviewed and summarized. Based on major technical challenges, future research directions have been proposed for efficient and stable intermediate temperature solid oxide fuel cell (SOFC) operation.

(1) Background: The purpose of this study was to assess the external load demands in futsal, con-sidering both home and away matches and their outcomes, in order to plan microcycles throughout the season based on the external load of each match. (2) Methods: The external load of 10 players from a First Division team in the Spanish Futsal League was recorded. The total dura-tion of player involvement with the clock stopped was 18.15 ±7.04 minutes. The players' external load was monitored using OLIVER devices. To analyse the influence of the match outcome and location on the external load, a univariate general linear model (GLM) analysis was conducted. (3) Results: There are no differences between the variables, except for accelerations of 2 to 3 m/s² (m) per minute and the number of accelerations of 2 to 3 m/s² per minute. Despite not showing significant differences, there is a trend indicating a higher demand for external loads in away matches as well as in matches won. (4) Conclusions: These findings are important for planning microcycles and providing the appropriate dosage to each player to achieve optimal performance in matches.

This paper investigates the effect of Fe contamination on the microstructure and mechanical properties of Al-7Si-0.7Mg-Fe2.5 alloy processed by Laser Powder Bed Fusion (LPBF) and heat treated in two different conditions: (1) Solution Heat Treatment (SHT) at 520 °C, followed by artificial ageing at 160 °C (T6), and (2) Direct Artificial Ageing of the as-printed material at 160 °C (DAA). Microstructural characterization was carried out by Optical Microscopy (OM), X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Transmission Electron Microscope (TEM). Mechanical characterization was carried out by tensile tests at room temperature. The formation of fine AlSiFe phases such as α-AlFeSi, β-AlFeSi, and π-Al8Si6Mg3Fe had no deleterious effect on the mechanical properties compared to previous studies for AlSIMg alloys without Fe. However, compared to the as-printed condition, the T6 heat treatment decreased the ultimate tensile strain significantly, while the elongation to the fracture presented a slight improvement.

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

Heavy metals pollution in the Straits of Malacca warrants the development of rapid, simple and sensitive assays. Enzyme-based assays are excellent preliminary screening tool with near real-time potential. The heavy-metal assay based on the protease ficin was optimized for mercury detection using Response Surface Methodology. The inhibitive assay is based on ficin action on the substrate casein and residual casein is determined using the Coomassie dye-binding assay. Heavy metals strongly inhibit the hydrolysis. A Central Composite Design (CCD) was utilized to optimize detection. The results show a marked improvement for the concentration causing 50% inhibition (IC50) for mercury, silver and copper. Compared to One-factor-at-a-time (OFAT) optimization, RSM gave an improvement of IC50 from 0.060 (95% CI, 0.0300.080) to 0.017 (95% CI, 0.0160.019), from 0.098 (95% CI, 0.0770.127) to 0.028 (95% CI, 0.0220.037) and from 0.040 (95% CI, 0.035.045) to 0.023 (95% CI, 0.0200.027), for mercury, silver and copper, respectively. A near real-time monitoring of mercury concentration in the Straits of Malacca at one location in Port Klang was carried out over a 4-h interval for a total of 24 h and validated by instrumental analysis with the result revealing an absence of mercury pollution in the sampling site.

Vitamin A, or retinol, is one of the most effective anti-ageing molecules, but it presents issues with photo-sensitivity and irritation. Retinol-like alternatives are emerging, but have so far been less effective. Here, we present a Silibum marianum extract (SME) as a retinol-like ingredient providing both safety and efficacy. SME was compared to the reference compound, retinol, and to the main alternative, bakuchiol. In ex vivo tests, skin explants from a 58-year-old donor were treated with pure retinol (0.1%), bakuchiol (0.2%) or SME (0.8%). After 5 days, collagen and hyaluronic acid levels were analysed. A placebo-controlled study involving 57 volunteers (ages 45-73 years) was also conducted, with products applied twice-daily for 56 days. Results were measured by AEVA-HE and VISA. Levels of collagen III were significantly increased by SME, by 23% and 16% compared to bakuchiol and retinol, respectively. Compared to bakuchiol, SME treatment increased hyaluronic acid production by 36%. In clinical tests, SME had a significantly stronger anti-wrinkle effect than bakuchiol – reducing the number of wrinkles on the forehead by 21%, and their circumference by 17% – producing effects similar to retinol, and better than bakuchiol. In the self-assessment, 43% of volunteers reported discomfort while using retinol, as compared to 0% for the SME formulation. By enhancing levels of collagen III – the youth collagen – and hyaluronic acid in the skin, SME paves the way for the maturation of collagen I fibrils and skin plumping. With its stronger efficacy compared to bakuchiol and enhanced safety profile compared to retinol, SME may be the next-generation of natural alternatives to retinoids.

[Introduction]: In the contemporary international financial environment, marked by intricate complexities and increasing uncertainties, the impact of exchange rate fluctuations on national economies has become increasingly significant. In this context, this study uses the Chinese economy as a case study to delve deeply into the interaction effects between macroprudential and monetary policies, analyzing how these policies jointly address economic instability. The aim is to offer new perspectives and strategies for stabilizing the global economic and financial landscape. [Methods]: This paper constructs an open-economy Dynamic Stochastic General Equilibrium (DSGE) model specifically tailored for China, incorporating Bayesian estimation and numerical simulation techniques. The study meticulously examines how macroprudential and monetary policies can be coordinated to achieve economic stability in response to exchange rate shocks. [Results]: The research finds that (1) targeted coordination of macroprudential and monetary policies towards asset prices and price levels can maintain stability under exchange rate shocks; (2) the effective implementation of such coordinated policies not only mitigates the fluctuations in asset prices due to exchange rate shocks but also enhances the level of societal welfare; (3) in terms of choosing an exchange rate regime, a managed floating rate system can prevent policy overlaps and conflicts that might occur in a floating rate system during the coordination of macroprudential and monetary policies, thus offering comparative advantages. [Discussion]: The findings underscore the importance of policy integration in a dynamic and complex global economic environment. They provide policy recommendations for China and other emerging market countries on how to effectively maintain economic stability through the synergistic interplay of macroprudential and monetary policies amid fluctuations in exchange rates and asset prices.

Background and Objectives: Treatment for antineutrophil cytoplasmic antibody-associated vasculitis (AAV) must deal with immunosuppression as well as infections associated with compromised immune system, such as tuberculosis (TB). Our aim was to overcome the gap in the literature concerning the risk of incidental TB after diagnosis of AAV. Materials and Methods: This retrospective population-based cohort study was based on the data from the National Health Insurance Research Database in Taiwan. We used a novel algorithm to identify patients with newly diagnosed granulomatous polyangiitis (GPA) or microscopic polyangiitis (MPA) between January 1, 2000 and December 31, 2012. The primary outcome was risk of incidental TB. Cox proportional hazard models were used to evaluate the association between AAV and incidental TB. Results: A total of 2,257 patients with AAV and a propensity-score matched cohort of 9,028 patients were studied. Overall, patients with AAV were at a 1.48x higher risk of contracting incidental TB than were patients in the matched cohort (adjusted HR 1.48; 95% confidence interval [CI], 1.02-2.15). Note that the highest risk of contracting incidental TB was in the first two years following a diagnosis of AAV, with a nearly 1-fold increase in risk (adjusted HR, 1.91; 95% CI, 1.01-3.60). Female AAV patients were 3.24x more likely than females without AAV to develop TB (adjusted HR 3.24; 95%CI, 1.85-5.67). Conclusion: Patients with AAV face an elevated risk of contracting incidental TB, particularly within the first two years after AAV diagnosis. The risk of contracting TB is higher among female AAV patients than among females without AAV.

The frequency synthesizer is a critical component in Cs beam clock technology. In this paper, we present a demonstration of a direct microwave frequency-synthesis chain for a cesium-beam atomic clock, which utilizes frequency multiplication and a dual-phase-locked loop mode. A detailed analysis of the frequency-synthesis chain is conducted, and a mathematical model is established. The phase settling time and system stability are simulated, measured, and verified. The experimental results for the phase settling time align with the simulation outcomes. The phase settling time can be adjusted within the range of 644.5 µs to 1.5 ms, and the absolute phase noise values are -63.7 dBc/Hz, -75.7 dBc/Hz, -107.1 dBc/Hz, and -122.5 dBc/Hz at 1 Hz, 10 Hz, 1 kHz, and 10 kHz offset frequencies, respectively. Additionally, the Ramsey fringes are detected, and the Allan deviations of the 10 MHz output from the cesium-beam atomic clock are measured to be 2.99×10−12 at 1s and 8.02×10−14 at 10,000 s.

The Industrial Internet of Things (IIoT) paradigm is a key research area derived from the Internet of Things (IoT). The emergence of IIoT has enabled a revolution in manufacturing and production, through the employment of various embedded sensing devices connected with each other by an IoT network, along with a collection of enabling technologies such as artificial intelligence (AI) and edge/fog computing. One of the unrivaled characteristics of IIoT is the inter-connectivity provided to industries; however, this characteristic might open the door for cyber-criminals to launch various attacks. In fact, one of the major challenges hindering the prevalent adoption of the IIoT paradigm is IoT security. Inevitably, an increasing number of research proposals have been introduced over the last decade to overcome these security concerns. To obtain an overview of this research area, conducting a literature survey of the published research is necessary, eliciting the various security requirements and their considerations. This paper provides a literature survey of IIoT security, focused on the period from 2017 to 2023. We identify IIoT security threats and classify them into three categories, based on the IIoT layer they exploit to launch these attacks. Additionally, we characterize the security requirements that these attacks violate. Finally, we highlight how emerging technologies, such as AI and edge/fog computing, can be adopted to address security concerns and enhance IIoT security.

Diabetes mellitus (DM) comprises a range of metabolic disorders characterized by high blood glucose levels caused by defects in insulin release, insulin action, or both. DM is a widespread condition that affects a substantial portion of the global population, causing high morbidity and mortality rates. The prevalence of this major public health crisis is predicted to increase in the forthcoming years. Although, several drugs are available to manage DM, these are associated with adverse side effects, which limits their use. In underdeveloped countries, where such drugs are often costly and not widely available, many people continue to rely on alternative traditional medicine, including medicinal plants. The latter serve as a source of primary healthcare and plant-based foods in many low and middle-income countries. Interestingly, many of the phytochemicals they contain have been demonstrated to possess antidiabetic activity such as lowering blood glucose levels, stimulating insulin secretion and alleviating diabetic complications. Therefore, such plants may provide protective effects that could be used in the management of DM. The purpose of this article was to review the medicinal plant-based foods traditionally used for the management of DM, including their therapeutic effects, pharmacologically-active phytoconstituents and antidiabetic mode of action at the molecular level. It also presents future avenues for research in this field.

This case report examined the characteristics of body composition, sleep quality, and autonomic nerve activity in active older adults with a younger body age—calculated from age trends in body composition and basal metabolic rate. We selected two cases who had a metabolic age younger than their actual age. They had good sleep quality, low sleep quality scores, no sarco-penia, strong muscle and grip strength, and balanced autonomic nervous system activity. Fur-thermore, they were determined to be much younger than their actual age. They were compared with two other age- and sex-matched cases, who had poor sleep quality, unbalanced autonomic nervous system activity, and had a physical age closer to their actual age. Older adults with more muscle mass and higher basal metabolism were younger than their actual age, had a better sleep status, and a good balance of autonomic nervous activity in the exercise stimulation. They also had lower percentages of body- and visceral fat and higher body water. Meanwhile, the older adults with standard muscle mass and basal metabolic rate had a poor sleep status and no sympathetic hyperactivity in the exercise simulation.

Affective prosody is an indispensable cognitive cue that moderates social activities, and has become a prevailing research topic in psychology-related disciplines. The present study conducts the first bibliometrics-based visualization analysis concerning affective prosody to evaluate the influential cases, including countries/regions, institutions, publication venues, academic articles, and disciplinary contributions, and the diachronic changes of publication trends and research hotspots. With the combination of statistical results and a qualitative literature inspection, limitations of extant studies and promising research directions were also proposed. The present study extracted the bibliographic data of 1,624 articles retrieved from the Web of Science Core Collection, which were published over the past 25 years (1997-2021). Statistical results revealed four leading powers (the U.S., Germany, England, and Canada) and four emerging fronts (China, France, Netherlands, and Switzerland), and identified three primary research themes in this field, including clinical implication, measurable index, and modality-specific issues. Literature inspection demonstrated current limitations in individual characteristics control and experiment-related influential factors, and proposed two prosperous research directions. Findings of the present study could facilitate academic retrieval of affective prosody research, help concerned researchers identify thematic hotspots and seek appropriate collaboration, and provide convenience for research policy and management in this field.

Self-conscious emotions, such as shame and guilt, play a fundamental role in regulating moral behavior and in promoting the welfare of the society. Despite their relevance, the neural bases of these emotions are uncertain. In the present meta-analysis, we performed a systematic literature review in order to single out functional neuroimaging studies on healthy individuals specifically investigating the neural substrates of shame, embarrassment and guilt. Seventeen studies investigating the neural correlates of shame/embarrassment, and seventeen studies investigating guilt brain representation met our inclusion criteria. The analyses revealed that both guilt and shame/embarrassment were associated with the activation of the left anterior insula, involved in emotional awareness processing, and arousal. Guilt specific areas were located within the left temporo-parietal junction, which is thought to be involved in social cognitive processes. Moreover, specific activations for shame/embarrassment involved areas related to social pain (dorsal anterior cingulate, insula, thalamus), behavioral inhibition (premotor cortex) networks. This pattern of results might reflect distinct action tendencies associated with the two emotions.

The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which is often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security nature, Blockchain has a great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind Software Defined Network (SDN) technology, this paper envisions SAUSA, a blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud data sets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-enabled point cloud service architecture which allows for efficient data processing and delivery to satisfy diverse Quality-of-Service (QoS) requirements. A blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution.

Specialized classes of proteins, working together in a tightly orchestrated manner, induce and maintain highly curved cellular and organelles membrane morphology. Due to the various ex- perimental constraints, including the resolution limits of imaging techniques, it is non-trivial to accurately elucidate interactions among the various components involved in membrane deformation. The spatial and temporal scales of the systems also make it formidable to investigate them using simulations with molecular details. Interestingly, mechanics-based mesoscopic models have been used with great success in recapitulating the membrane defor- mations observed in experiments. In this review, we collate together and discuss the various mechanics based mesoscopic models for protein-mediated membrane deformation studies. In particular, we provide an elaborate description of a mesoscopic model where the membrane is modeled as a triangulated sheet and proteins are represented as either nematics or fila- ments. This representation allows us to explore the various aspects of protein-protein and protein-membrane interactions as well as examine the underlying mechanistic pathways for emergent behavior such as curvature-mediated protein localization and membrane deforma- tion. We also put forward current efforts in the field towards back-mapping these mesoscopic models to finer-grained particle based models - a framework that could be used to explore how molecular interactions propagate to physical scales and vice-versa. We end the review with an integrative-modeling based road map where experimental imaging micrograph and biochemical data are combined with mesoscopic and molecular simulations methods in a theoretically consistent manner to faithfully recapitulate the multiple length and time scales in the membrane remodeling processes.

The emergence of the COVID-19 pandemics prompted a fast development of novel diagnostic methods of the etiologic virus SARS-CoV-2. Methods based on CRISPR-Cas systems have been particularly promising because they can achieve a similar sensitivity and specificity to the golden standard RT-qPCR, especially when coupled to an isothermal pre-amplification step. Furthermore, they have also solved inherent limitations of RT-qPCR that impede its decentralized use and deployment in the field, such as the need for expensive equipment, high cost per reaction, and delivery of results in hours, among others. In this review, we evaluate publicly available methods to detect SARS-CoV-2 that are based on CRISPR-Cas and isothermal amplification. We critically analyze the steps required to obtain a successful result from clinical samples and pinpoint key experimental conditions and parameters that could be optimized or modified to improve clinical and analytical outputs. The COVID outbreak has propelled intensive research in a short time, which is paving the way to develop effective and very promising CRISPR-Cas systems for the precise detection of SARS-CoV-2. This review could also serve as an introductory guide to new labs delving into this technology.

To explore a new set of anticancer agents, a novel series of pyrazolo[4,3-e]pyrido[1,2-a]pyrimidine derivatives 7a-l have been designed and synthesized via cyclocondensation reactions of pyrazolo-enaminone 5 with a series of arylidene malononitriles; compound 5 was obtained from 5-amino-4-cyanopyrazole (3). The structures of the target compounds 7a-l were investigated by spectral techniques and elemental analysis (IR, UV-Vis, 1H NMR, 13C NMR and ESI-MS). All compounds were evaluated for their in vitro cytotoxicity employing a panel of different human tumor cell lines, A375, HT29, MCF7, A2780, FaDu as well as non-malignant NIH 3T3 and HEK293 cells. It has been found that the conjugate 7e was the most active towards many cell lines with EC50 values ranging between 9.1 and 13.5 µM, respectively. Moreover, in silico docking studies of 7e with six anticancer drug targets, i.e. DHFR, VEGFR2, HER-2/neu, hCA-IX, CDK6 and LOX also was performed, in order to gain some insights into their putative mode of binding interaction and to estimate the free binding energy of this bioactive molecule.

This mini-review critically summarizes the knowledge of imprinted polymer-based electrochemical sensors, for the detection of pesticides, metal ions and waterborne pathogenic bacteria, focusing on the period the last 5 years (citation of 78 papers published in 2017-2021, ie 63% of total citations). MIP-based electrochemical sensors exhibit low limit of detection, high selectivity, high sensitivity and low cost. Herein, we focused on the timely topics of water pollution by organics, inorganics and micro-organisms represented by pesticides, metal ions and bacteria, respectively. We put the emphasis on the design of imprinted polymers and their composites and coatings by radical polymerization, oxidative polymerization of conjugated monomers or sol-gel chemistry. Whilst most imprinted polymers are used in conjunction with differential pulse or square wave voltammetry for sensing organics and metal ions, electrochemical impedance spectroscopy (EIS) appears as the chief technique for detecting bacteria. This successful combination of EIS and imprinting technology should be harnessed in the coming years in the case of bacteria. Interestingly, bacteria are not always probed by bacteria-imprinted polymers; we report here their detection by monitoring specific (macro)molecules that reflect bacterial activity, for example quorum sensing signaling molecules or flagella proteins. If much has been developed in the past decade with glassy carbon or gold electrodes, it is clear that carbon paste electrodes of imprinted polymers are more and more investigated due to their versatility. Shortlisted case studies were critically reviewed and discussed; clearly a plethora of tricky strategies of designing selective electrochemical sensors are offered to “Imprinters”. We anticipate this review will be of interest to experts and new comers in the field who are paying time and effort combining electrochemical sensors with MIP technology.

This study shows the application of self-organizing maps (SOMs) to probabilistic forecasts of wind power generation and ramps in Japan. SOMs are applied to atmospheric variables obtained from atmospheric reanalysis over the region, thus deriving classified weather patterns (WPs). Probabilistic relationships are established between the synoptic-scale atmospheric variables over East Japan and the generation of regionally integrated wind power in East Japan. Medium-range probabilistic wind power predictions are derived by SOM, as analog ensembles based on the WPs of the multi-center ensemble forecasts. As this analog approach handles stochastic uncertainties effectively, probabilistic wind power forecasts are rapidly generated from a very large number of forecast ensembles. The use of a multi-model ensemble provides better results than a one-forecast model. The hybrid ensemble forecasts further improve the probabilistic predictability skill of wind power generation, as compared with non-hybrid methods. It is expected that long-term wind forecasts will provide better guidance to transmission grid operators. The advantage of this method is that it can include an interpretative analysis of meteorological factors for variations in renewable energy.

The vibration signal of heavy gearbox presents non-stationary and nonlinear characteristics, which increases the difficulty to extract the fault feature. When the gear has a subtle fault, it may cause a perceptible change of local fluctuation rather than the large scale fluctuation. Therefore, the feature parameters extracted from local fluctuation can effectively improve the recognition performance of the gear fault. In this paper, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to identify the gear fault of heavy gearbox. Firstly, the raw vibration signal is decomposed several mode components by VMD, which is an adaptive and non-recursive signal decomposition method. Next, the sensitive mode component is selected by a maximal indicator, which is composed of kurtosis and correlation coefficient of relative higher frequency mode components corresponding to local fluctuation of raw vibration signal. Finally, the characteristics of the double-scales feature parameters of selected sensitive mode are extracted by DFA. In addition, the position of turning point of double scales is estimated by sliding windowing algorithm. The proposed method is evaluated through its application to gear fault classification using vibration signal. The results demonstrates that the recognization rate of gear faults condition have marked improvement by proposed method than the DFA of Small Time Scale (STS-DFA) method.

The COVID-19 disease has infected many countries with generalised impacts on the different income categories. To test this hypothesis, we carried out a survey among households (n = 412) representing different income groups in Nigeria. We used validated food insecurity experience and socio-psychologic tools. We analysed the data obtained using descriptive and inferential statistics. The earning capacities of the respondents range from US$145/month for the low-income earners to US$1945/month for high-income earners. A total of 173 households (42%) ran out of food during the COVID-19 pandemic. All categories of households have experienced increasing dependency from the general public and perception of increasing insecurity, with the high-income earners having the greatest shift. In addition, increasing levels of anger and irritations have been experienced with all categories. Of the socio-demographic variables, only gender (p = 0.012), level of education of household head (p = 0.000), work hours per day (p = 0.006), and family income based on society class (p = 0.000) were significantly associated with food security and hunger due to the COVID-19 pandemic. Household heads with medium (OR: 4.36, 95% CI: 2.64 – 7.21, p < 0.001) and high (OR: 30.27, 95% CI: 3.99 – 229.90, p < 0.001) family income based on society class were more likely to have satisfactory experience on food security and hunger than respondents with low family income. Irrespective of the social class of the respondents, some respondents had negative experiences such as movement/change of location within the same city (17.68%), begin a new relationship (14.43%), recent change of job/loss of job (11.55%), movement to another city (10.81%), and separation from spouse or long-term relationship (10.07%). However, 231 (60.16%) reported satisfaction with their jobs despite the pandemic. Psychological stress due to impact of COVID-19 was observed to be more in the low income earning group than others. It is recommended that socio-economic groups should be mapped and support systems should target each group as per the needed support in terms of health, social, economic, and mental wellness; the government should show political commitments and transparency in food security and counselling supports. National authorities should prioritize empirical-based interventions on resources allocation during crises.

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

Online identity-based theft is known to be one of the most serious and growing threats to victims, such as individuals and organizations, over the last 10 years due to the enormous economic damage these crimes have caused. The availability of personal information on the Internet has increased the chances of this cyber-crime. Online identity theft crime is the result of a combination of cyber-crimes on the one hand and lack of awareness and training of users on the other hand to protect personal data on the other. Education and awareness, which also contributes to early detection, is the strongest tool for consumers to safeguard themselves from online identity fraud. This paper provides a comprehensive explanation of online identity theft, the various approaches that thieves use to attack individuals and organizations and the types of fraud involved in this cyber-crime. The aim of this research is to evaluate the need for a reformulation of the concept of identity theft in order to be compatible with the evolution of behaviors and fraud.

Antidepressants are among the most used medications in the US, with significant deleterious effects on people’s well-being. At any given time, depression impacts approximately 1 in 10 Americans, causing wide and broad societal costs. Interest is developing for non-pharmacological treatments and preventative measures. We summarize the literature on non-invasive dietary and lifestyle approaches for treating depression, including recent work with the psychedelic treatment of depression. This review aims to inform future research and treatment programs for depression. This review provides an evidentiary summary of integrative therapeutic approaches for depression.

Mathematical models of non-small cell lung cancer are powerful tools that use clinical and experimental data to describe various aspects of tumorigenesis. The developed algorithms capture phenotypic changes in the tumor and predict changes in tumor behavior, drug resistance, and clinical outcomes of anti-cancer therapy. The aim of this study was to propose a mathematical model that predicts the changes in the cellular composition of patient-derived tumor organoids over time with a perspective of translation of these results to the parental tumor, and therefore to possible clinical course and outcomes for the patient. Using the data on specific biomarkers of cancer cells (PD-L1), tumor-associated macrophages (CD206), natural killer cells (CD8), and fibroblasts (αSMA) as input, we proposed a model that accurately predicts the cellular composition of patient-derived tumor organoids at a desired time point. Combining the obtained results with “omics” approaches will improve our understanding of the nature of NSCLC. Moreover, their implementation into clinical practice will facilitate a decision-making process on treatment strategy and develop a new personalized approach in anti-cancer therapy.

The research improves the strength of plastic using avocado seed starch and PLA. The effect of blending avocado seed starch and PLA was optimized using the RSM approach by using two variables: water absorption and biodegradability. Mixing them using RSM gave the best result: 1.8 g of starch and 3 g of PLA. Degradable plastic has a (tensile strength of 10.1 MPa; elongation at break of 85.8%; young modulus of 190 MPa). Infrared spectroscopy showed that the plastic had a -OH bond at 3273.20 cm-1, 3502.73 cm-1, 3647.39 cm-1, a CH2 bond at 2953.52 cm-1, 2945.30 cm-1, 2902.87 cm-1, a C=C bond at 1631.78 cm-1, and a C-O bond at 1741.72 cm-1. The plastic decomposed in soil. It was organic and hydrophilic. Thermal tests, the plastic can withstand heat well, losing weight at 356.86°C to 413.64°C, forming crystals and plastic melts at 159.10°C—the same as PLA. Melt flow test, sample melts before measurement, therefore not measurable—process conditions that affect. Water absorption of 5.763% and biodegradation rate of 37.988% decomposed for 12 days. In morphology analysis, the starch and PLA fused to form a smooth surface. RSM found value was close to 1. RSM gave the best process parameters.

Cardiac hypertrophy is an important and independent risk factor for the development of heart failure. To better understand the mechanisms and regulatory pathways involved in cardiac hypertrophy, there is a need for improved in vitro models. In this study, we investigated how hypertrophic stimulation affected human induced pluripotent stem cell (iPSC)-derived cardiomyocytes (CMs). The cells were stimulated with endothelin-1 (ET-1) for 8, 24, 48, 72, or 96h. Parameters including cell size, ANP-, proBNP-, and lactate concentration were analyzed. Moreover, transcriptional profiling using RNA-sequencing was performed to identify differentially expressed genes following ET-1 stimulation. The results show that the CMs increase in size by approximately 13% when exposed to ET-1 in parallel to increases in ANP and proBNP protein and mRNA levels. Furthermore, the lactate concentration in the media was significantly increased indicating that the CMs consume more glucose, a hallmark of cardiac hypertrophy. Using RNA-seq, a hypertrophic gene expression pattern was also observed in the stimulated CMs. Taken together, these results show that hiPSC-derived CMs stimulated with ET-1 display a hypertrophic response. The results from this study also provide new molecular insights about the underlying mechanisms of cardiac hypertrophy and may help accelerate the development of new drugs against this condition.

Background: The purpose of this article is to update the diagnostic assessment, therapeutic approach, and 12-18 month follow up of patients added in the Italian Lombardy ADHD Register. Methods: Data on patients evaluated by the 18 Regional ADHD Reference Centres in the ten year period from 2011 to April 2021 were analysed. Results: 4091 of 5934 added patients received a diagnosis of ADHD. In 20.3% of cases, there was a family history of ADHD. 2879 children (70.4%) had at least one comorbidity disorder, the most common of which was learning disorder (39%). Nearly all (95.9%) received at least one psychological prescription, 17.9% of them almost one pharmacological treatment, and 15.6% a combination of both. Values of ≥5 of the Clinical Global Impressions- Severity (CGI-S) are more commonly presented by patients with a pharmacological prescription than with a psychological treatment (p < .0001). A significant improvement was reported in half of the patients followed after 1 year, with Clinical Global Impressions- Improvement (CGI-I ≤ 3). In all, 233 of 4091 are 18 year old patients. Conclusion: A ten year systematic monitoring of models of care was a fruitful shared and collaborative initiative in order to promote significant improvement in clinical practice, providing effective and continuous quality of care. The unique experience here reported should spread.

In our work, using the analysis of DNA barcodes and morphology (wing color, male genitalia, sphragis shape in females), we show that the group of species close to P. mnemosyne is represented by two phylogenetic lineages, the western and eastern ones. The eastern lineage includes P. stubbendorfii, P. glacialis and P. hoenei. The western lineage includes three morphologically similar species, P. mnemosyne (Western Eurasia), P. turatii (southwestern Europe) and P. nubilosus stat. nov. (Turkmenistan and NE Iran), as well as P. ariadne (Altai). The latter species differs from the rest of the group in the presence of red spots on the wings. Parnassius mnemosyne s.s. is represented by four differentiated mitochondrial clusters that show clear association with specific geographic regions. We propose to interpret them as subspecies: P. mnemosyne mnemosyne (Central and Eastern Europe, N Caucasus, N Turkey), P. mnemosyne adolphi (the Middle East), P. mnemosyne orientalis (Tian Shan) and P. mnemosyne gigantea (Gissar-Alai in Central Asia). We demonstrate that in P. ariadne the red spots on the wing evolved as a reversion to the ancestral wing pattern. This reversion is observed in Altai, where the distribution areas of the western lineage, represented by P. ariadne, and the eastern lineage, represented by P. stubbendorfii, overlap. These two species hybridize in Altai, and we hypothesize that the color change in P. ariadne is the result of reinforcement of prezygotic isolation in the contact zone. Lectotype of Parnassius mnemosyne var. nubilosus Christoph, 1873 is designated.

Background Gingivitis can develop without mechanical cleaning within three weeks. The first clinical sign is bleeding on positive probing. The accumulation of dental biofilm generates an inflammatory gingival response. In the past decade, attention has focused mainly on the interproximal areas and the use of customsized interproximal toothbrushes.The aim of the study was to evaluate the effectiveness of individualized oral hygiene education and its role in dental prevention among patients with dental problems. Methods Before treatment, patients were clinically and radiologically examined, full mouth plaque score (FMPS), full mouth bleeding score (FMBS), and bleeding on brushing (BOB) were recorded, and matrix-metalloproteinase-8 (MMP-8) was measured by using a chair-side MMP-8 measuring system. Patients in group A had gingivitis, but no periodontal damage and patients in group B had periodontal damage. Patients in both groups A and B were identified in to four subgroups based on their toothbrushing habits and the oral health education they received. The three months after initial examination, each patient was examined three more times (2, 4 and 12 weeks later). Results It was concluded that subjects in groups A1 and B1 showed significant reduction in BOB, MMP-8, FMBS and FMPS levels after two weeks. Solo Prophylaxis (A1and B1) remained as a well-constructed protocol with hte complete resolution of interdental inflammation after two weeks. Other subgroups achieved significant reductions only after 12 weeks. Conclusions BOB and MMP-8 are valuable complements in preventive dentistry, and are able to detect potential pathological processes in interproximal spaces. The clinical relevance of BOB, in addition to FMBS, FMPS and gingival inflammation can be demonstrated to patients, which may increase compliance.

This study introduces a sophisticated neural network structure for segmenting breast tumors. It achieves this by combining a pretrained Vision Transformer (ViT) model with a U-Net framework. The U-Net architecture, commonly employed for biomedical image segmentation, is further enhanced with Depthwise Separable Convolutional Blocks to decrease computational complexity and parameter count, resulting in better efficiency and less overfitting. The Vision Transformer, renowned for its robust feature extraction capabilities utilizing self-attention processes, efficiently captures the overall context within images, surpassing the performance of conventional convolutional networks. By using a pretrained ViT as the encoder in our U-Net model, we take advantage of its extensive feature representations acquired from extensive datasets, resulting in a major enhancement in the model’s ability to generalize and train efficiently. The suggested model has exceptional performance in segmenting breast cancers from medical images, highlighting the advantages of integrating transformer-based encoders with efficient U-Net topologies. This hybrid methodology emphasizes the capabilities of transformers in the field of medical image processing and establishes a new standard for accuracy and efficiency in activities related to tumor segmentation.

Cobalt oxide nanoparticles are promising candidates for various kinds of practical application. The interest of researchers and scientists in these nanoparticles is due to their unique physical and chemical properties such as density, melting point, magnetic characteristics and band gap. The need to develop high performance and low-cost technologies for obtaining Co3O4 nanoparticles is a major reason in research in this area. The widespread commercial use of Co3O4 nanoparticles will open doors for numerous novel cost effective technologies, which are mostly dependent on the type of synthesis method and raw materials. A possible solution to this problem is to use Cobalt nitrates as a cost-effective precursor using the solution combustion method. Solution combustion synthesis method is one of the best candidate methods that have the potential for high performance and is also cost-effective. This review examines recent progress in the synthesis of Co3O4 nanoparticles by solution combustion method and underscores their significance across various fields, highlighting the need for further research and exploration of their multifaceted applications. In addition, the most recent advancements in the applications of Co3O4 nanoparticles in sensitive sensing, catalysis, supercapacitors/batteries and water treatment/pollutants removal are reviewed and described, focusing on current trends, challenges, and future outlooks.

Optical coherence tomography (OCT) is a non-invasive imaging technique widely used in ophthalmology for visualizing retinal layers, aiding in early detection and monitoring of retinal diseases. OCT is useful for detecting diseases such as age-related macular degeneration (AMD) and diabetic macular edema (DME), which affect millions of people globally. Over the past decade, the area of application of Artificial Intelligence (AI), particularly Deep Learning (DL), has significantly increased. The number of medical applications is also rising, with solutions from other domains being increasingly applied to OCT. The segmentation of biomarkers is an essential problem that can enhance the quality of retinal disease diagnostics. For 3D OCT scans, AI is beneficial since manual segmentation is very labor-intensive. In this paper, we employ the new SAM 2 and MedSAM 2 for the segmentation of OCT volumes for two open-source datasets, comparing their performance with the traditional U-Net. The model achieved an overall Dice score of 0.913 and 0.902 for macular holes (MH) and intraretinal cysts (IRC) on OIMHS and 0.888 and 0.909 for intraretinal fluid (IRF) and pigment epithelial detachment (PED) on the AROI dataset, respectively.

The growing of Brain-Computer Interface (BCI) applications is closely related to the increasing accessibility of Electroencephalography (EEG) hardware (EEG headsets), which are noninvasive, portable, wireless and often with open software. However, there is a limited number of BCI software platforms tailored to help inexperienced programmers in the development of BCI applications. Only few of them integrate BCI applications with IoT devices and services. To address these challenges, a model for visual node-based programming has been designed utilizing BrainFlow library within the Node-RED platform, which can be applied to more than 20 biosensors. New Nodes for Node-RED Library within OpenBCI Category (openBCI toolkit for Node-RED) have been developed for design of EEG-Based Brain-Machine Interface and Integration in IoT. The proposed toolkit have been implemented and validated through a case study for controlling a robotic arm by OpenBCI headset. The results from the pilot experiment demonstrated that through concentration levels classified by BrainFlow performance metrics, the control of a TinkerKit Braccio robot arm is possible.

Despite the fact that strong routine separation methodologies can give reliable specificity and validity at usual working pharmaceuticals concentrations, they may fail at very low concentration levels. This poses considerable challenges for researchers inves-tigating product purity and therapeutic drug monitoring. Sensitivity enhancement pro-cedures are thus required to maximize the performance of separation techniques. Large volume injection, solid phase extraction/solid phase enrichment (SPE/SPEn), pre-, post-, and in-column derivatization, as well as the use of sensitive detection devices are the simplest strategies for improving sensitivity of the separation-based analytical techniques. Large volume injection of samples with online SPE/SPEn coupled with separation techniques increased sensitivity and improved detection as well as quantification limits without affecting peak shape and system performance. Although the primary purpose of derivatization is to improve sensitivity and selectivity, greener derivatization is growing in popularity and should be considered in analytical chemistry. In general, two strategies are essential for accomplishing greener derivatization goals. The first is the search for and use of ecologically acceptable derivatizing reagents, solvents, and reaction conditions. The second is miniaturization and automation of analytical methods. This review discusses significant advances in separation-based analytical techniques, specifically enrichment approaches and detector signal improvement for pharmaceutical quantification in various matrices at very low concentration levels. As a result of improved analytical systems setup in drug assays, the possibility of high-throughput analyses was also highlighted.

The general-purpose Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) at CERN includes the hadronic calorimeter to register the energies of the charged and neutral hadrons produced in the proton-proton collisions at the LHC at a center of mass energy 13.6 TeV. The calorimeter is located inside the superconducting solenoid of 6 m in diameter and 12.5 m in length creating the central magnetic flux density of 3.8 T. For operating optimally in the high pileup and high radiation environment of the High Luminosity LHC, the existing CMS endcap calorimeters will be replaced with a new high granularity calorimeter (HGCal) comprising of an electromagnetic and a hadronic section in each of the two endcaps. The hadronic section of the HGCAL will include 44 stainless steel absorber plates with a relative permeability value well below 1.05. The volume occupied by 22 plates in each endcap is about 21 m3. The calculation of the axial electromagnetic forces on the absorber plates is a crucial element in designing the mechanical construction of the device. With a three-dimensional computer model of the CMS magnet, the axial forces on each absorber plate are calculated and the dependence of forces on the central magnetic flux density value is presented. The method of calculation and the obtained results are discussed.

The distributed acoustic sensing (DAS) has great potential for monitoring natural-resource reservoirs and borehole conditions. However, the large volume of data and complicated wavefield add challenges to processing and interpretation. In this study, we demonstrate that seismic interferometry based on deconvolution is a convenient tool for analyzing this complicated wavefield. We extract coherent wave from the observation of a borehole DAS system at the Brady geothermal field in Nevada. Then, we analyze the coherent reverberating waves, which are used for monitoring temporal changes of the system. These reverberations are tirelessly observed in the vertical borehole DAS data due to cable or casing ringing. The deconvolution method allows us to examine the wavefield at different boundary conditions. We interpret the deconvolved wavefields using a simple 1D string model. The velocity of this wave varies with depth, observation time, temperature, and pressure. We find the velocity is sensitive to disturbances in the borehole related to increasing operation intensity. The velocity decreases with rising temperature, which potentially suggests that the DAS cable or the casing are subjected to high temperature. This reverberation can be decomposed into distinct vibration modes in the spectrum. We find that the wave is dispersive, and the the fundamental mode propagate with a large velocity. The method can be useful for monitoring borehole conditions or reservoir property changes. For the later, we need better coupling than through only friction in the vertical borehole to obtain coherent energy from the formation.

Objective: With just ten questions, the Berlin questionnaire (BQ) stands out as one of the simplest and most widely implemented non-invasive screening tools for detecting subjects at high risk for Obstructive Sleep Apnea (OSA), a still underdiagnosed syndrome characterized by partial or complete obstruction of the upper airways during sleep. The main aim of this study was to enhance the diagnostic accuracy of the BQ through Machine Learning (ML) techniques. Methods: A ML classifier (hereafter, ML-10) was trained using the ten questions of the standard BQ. A simplified variant of the BQ, BQ-2, which comprises only two questions out of the total of ten, was also assessed in a ML context. A 10-fold cross validation scheme was used. Ground truth was provided by the Apnea-Hypopnea Index (AHI) measured by Home Sleep Apnea Testing. Model performance was determined comparing ML-10 and BQ-2 with the standard BQ by the Receiver Operating Characteristic Curve (ROC), Area Under the Curve (AUC), sensitivity, specificity, and accuracy. Results: ML-10 demonstrated superior performance in predicting the risk for OSA compared to the standard BQ and was also capable in classifying OSA with two different AHI thresholds (AHI>=15, AHI>=30), typically used in clinical practice. Remarkably, BQ-2 was also better in sensibility to assess moderate to severe OSA (AHI≥ 15) compared to the ML-10. Conclusions: The study underscores the importance of integrating ML techniques for early OSA detection, suggesting a direction for future research to improve diagnostic processes and patient outcomes in sleep medicine.

According to its inner property, a crisis in the financial market can be considered as a collective behavior phenomenon. Through the prism of collective behavior, the crisis does not happen if the companies are independent of each other. In this work, cooperative movement processes in a stock market are investigated in a manner similar to that Vicsek first described collective behavior for self-propelled entities. To this end, a phase space is defined as the one in which the return of volume of transactions versus return of price is represented with each share in each day corresponding to a unique point in the space. The findings of the observation show that during times of crisis, the phase space is limited with the vector velocity of shares in the same direction. In contrast, on a regular day, the phase space is entirely accessible, with vector velocity aligned randomly. Moreover, in line with the Vicsek model, an order parameter is introduced, which evaluates the cooperative effects for the shares so that the higher the value of this parameter, the stronger the collective behavior of the shares.

Hydrokinetic energy constitutes a significant promise as a renewable energy source, yet many regions experience flow velocities insufficient to support conventional turbine installations. According to this, the present work proposes a vertical axis hydrokinetic turbine aimed to environments where conven-tional turbines may not be feasible due to insufficient water velocity. This turbine is based on a biologi-cally inspired design which improves the traditional Savonius turbine. This innovative configuration is inspired by the Fibonacci spiral. The turbine's performance was analyzed through Computational Fluid Dynamics. A notable improvement was found in comparison with the traditional Savonius turbine, providing a 15.1% increment in the power coefficient.

In this study is considered the design and implementation of pilots in desalination plants, in a systematic way, to evaluate energy improvements in water desalination processes. Pilot tests with the latest generation of reverse osmosis membranes in large desalination plants are becoming increasingly common, as small-scale experiments are carried out on the operation of new generation membranes with better salt rejection and lower energy consumption, to achieve the best water quality required for each case at the lowest possible cost. In this way, pilot tests are being carried out before the decision is made whether or not to change the reverse osmosis membranes, as this represents a significant investment and we minimize the risk of making a mistake in order to ensure the best results in terms of energy consumption, operating costs and reducing the environmental impact while complying with the required water quality. In this case, the Pareto analysis is used to highlight the 2 or 3 most significant causes whose treatment affects more than 80% of the possible energy improvement to be implemented. In this sense, figure 3 below shows the Pareto diagram for the energy improvement of N plants in a given territory. A series of factors that can affect this energy improvement have been numbered from 1 to 7, such as the seawater intake, the decision on the membranes, the energy recuperators, the high-pressure pump, the decisions on the pre-treatment, the feed pump or the post-treatment. The Pareto diagram has been studied at territorial level with a total of 180 seawater desalination plants. The novelty of this article is to apply Pareto diagram to a study of a total of 180 seawater desali-nation plants at territorial level in Canary Islands.

The polysaccharide-based chiral stationary phase (CSP) columns for high-performance liquid chromatography (HPLC) were applied in the separation of liquid crystalline racemic mixtures obtained based on (R,S)-2-octanol and were found to be highly effective in recognizing isomers of minor structural differences. The mobile phase (MP), which consists of acetonitrile (ACN)/water (H2O) at different volume ratios, was used. All measurements were made at room temperature. The order of the elution was also determined. The chromatographic parameters such as resolution - (Rs), selectivity - (α), and the number of theoretical plates, i.e., column efficiency - (N), were determined. The polysaccharide-based CSP columns have unique advantages in separation technology, and this study has shown the potential usefulness of the CSP columns in separating liquid crystalline racemic mixtures belonging to the same homologous series.

Melanoma, one of the most aggressive forms of skin cancer, is also one of the most diagnosed cancer types. While surgical excision of the lesions is the primary treatment for melanoma, not all cases are candidates for surgical procedures. New treatments and complementary options are necessary, given the increasing diagnosis rate. In the present study, a norcantharidin-containing nanoemulsion was developed and evaluated in vivo using a syngeneic graft murine model. Our model contemplates an amputation surgery simulating the standard treatment and the role of the nanoemulsion as a potential adjuvant therapy. We observed a significant decrease in the growth rate of the melanoma lesion in the treated groups compared to the control group, both at the 20th and 30th days of treatment. Moreover, we evaluated the drug bioavailability in serum samples, and the results showed that norcantharidin was detectable in a range of 0.1 to 0.18 mg per mL in the treated groups. Furthermore, histopathological analysis was performed on the amputated tumors, where significant differences were found regarding size, mitosis rate, lymphocytic infiltration, and multispectral quantitative image analysis compared to the control group. The norcantharidin-containing nanoemulsion could be a potential alternative or adjuvant therapy if more clinical studies are conducted. Topical nanosystems can become or complement standard therapies, which is needed as melanoma affects not only in terms of mortality but also the patient's morbidity and life quality.

The urban heat island (UHI) and its intensity is one of the phenomena that are of determining importance for the comfort of living in cities and their sustainable development in the face of deepening climate change. The study is objectively difficult due to the large dynamics like land cover and the considerable diversity of land use patterns in urban areas. Various approaches and methods are used in research practice, based both on ground measurements and in the use of remote methods and satellite information. However, most of these approaches provide information with problematic spatial and temporal resolution, making them difficult to apply for sustainable urban planning purposes, which require adequate information provision. This paper presents the results of the application of thermal photogrammetry based on the use of unmanned aerial systems (UAS), combined with geographic information systems (GIS), in the study of surface urban heat island intensity (SUHI), at the local level for the largest housing complex in Bulgaria - Lyulin district of the capital of Sofia city. The studies were carried out during a heat wave in July 2023. A difference of 16.5oC was found between locations with SUHI occurrence and suburban areas. The information benefits of locally addressed data and their direct applicability are discussed to support decision-making processes in the planning and management of urban areas, including their climate adaptation and sustainable development.

Dysferlinopathies comprise a spectrum of muscular dystrophies characterized by progressive muscle weakness and degeneration due to mutations in the DYSF gene, which encodes the dysferlin protein critical for muscle membrane repair. This review delves into the clinical spectra of dysferlinopathies, their molecular mechanisms, and the spectrum of emerging therapeutic strategies. We explore the phenotypic heterogeneity of dysferlinopathies, highlight the incomplete understanding of genotype-phenotype correlations, and discuss the implications of various DYSF mutations. Furthermore, we examine the utility of animal models in elucidating disease mechanisms and the potential of symptomatic, pharmacological, molecular, and genetic therapies in mitigating the disease's progression. We also consider the roles of diet and metabolism in managing dysferlinopathies, as well as the impact of clinical trials on treatment paradigms. By culminating the complexities inherent in dysferlinopathies, this article emphasizes the need for multidisciplinary approaches, precision medicine, and extensive collaboration in research and clinical trial design to advance our understanding and treatment of these challenging disorders.

This review discusses a broad range of recent advances (2013-2017) of chemical imaging using electrochemical methods, with a particular focus on techniques that have been applied to study cellular processes, or techniques that show promise for use in this field in the future. Non-scanning techniques such as microelectrode arrays (MEAs) offer high time-resolution (< 10 ms) imaging, however at reduced spatial resolution. In contrast, scanning electrochemical probe microscopies (SEPMs) offer higher spatial resolution (as low as a few nm per pixel) imaging, with images collected typically over many minutes. Recent significant research efforts to improve the spatial resolution of SEPMs using nanoscale probes, and to improve the temporal resolution using fast scanning have resulted in movie (multiple frame) imaging with frame rates as low as a few seconds per image. Many SEPM techniques lack chemical specificity or have poor selectivity (defined by the choice of applied potential for redox-active species). This can be improved using multifunctional probes, ion-selective electrodes and tip-integrated biosensors, although additional effort may be required to preserve sensor performance after miniaturization of these probes. We discuss advances to the field of electrochemical imaging, and technological developments which are anticipated to extend the range of processes that can be studied. This includes imaging cellular processes with increased sensor selectivity and at much improved spatiotemporal resolution than has been previously customary.

The dietary pattern that characterizes the Western diet is strongly associated with obesity and related metabolic diseases, but biological mechanisms supporting these associations remain largely unknown. We argue that the Western diet promotes inflammation that arises from both structural and behavioral changes in the resident microbiome. The environment created in the gut by ultra-processed foods, a hallmark of the Western diet, is an evolutionarily unique selection ground for microbes that can promote diverse forms of inflammatory disease. Recognizing the importance of the microbiome in the development of diet-related disease has implications for future research, public dietary advice as well as food production practices. Research into food patterns suggests that whole foods are a common denominator of diets associated with a low level of diet-related disease. Hence, by studying how ultra-processing changes the properties of whole foods and how these foods affect the gut microbiome, more useful dietary guidelines can be made. Innovations in food production should be focusing on enabling health in the super-organism of man and microbe, and stronger regulation of potentially hazardous components of food products is warranted.

: Controlling the distribution of water and wastewater between industrial processes is very vital in rationalizing water and preserving the environment. In this paper, a mathematical technique is proposed to optimize water-wastewater networks; a nonlinear program is introduced to minimize the consumption of freshwater, and consequently, the flowrate of the wastewater dis-charge will be also minimized. A general mathematical model, able to handle industrial plants containing up to eight sources and eight sinks, is developed using LINGO optimization software to facilitate dealing with complex case studies. The introduced model can handle single contam-inant networks as well as multiple contaminants cases. The optimal water network is synthe-sized through two steps; the first step is to introduce the case study data to the developed mathematical model. The second step considers using the optimal solution produced after run-ning the developed LINGO model as feed data for a pre-designed Excel sheet able to deal with these results and simultaneously draw the optimal water-wastewater network. The proposed mathematical model is applied to two case studies; The first case study includes actual data from four fertilizer plants located in Egypt; the water resources and requirements are simultaneously integrated to obtain a sensible cutting in both freshwater consumption (lowered by 52.2%) and wastewater discharge (zero wastewater discharge). The second case study is for a Brazilian pet-rochemical plant; the obtained results showed noticeable reductions in freshwater consumption for the second case study by 12.3 % while the reduction percentage of wastewater discharge is 4.5%.

Majority of the studied MADS box members are linked with flowering and fruit traits. However, higher volumes of studies on the type II of their two types so far suggest that florigenic effect of the gene members could just be tip of the iceberg. In current study we used a systematic approach to have general overview on the MADS box members their cross-trait and multifactor associations as well as their pleiotropic potentials based on local reference database curated for MADS box members. While doing so, we screened for the co-occurrence of the terms of interest within the title or abstract of each reference with threshold of 3 hits. Analysis results showed that our approach can retrieve multi-faceted information on the subject of study (MADS box gene members in current case) which could otherwise have been skewed depending on the authors’ expertise and/or volume of literature reference base. Overall, our study discusses on the roles of MADS box members in association with plant organs and traits-linked factors among plant species. Our assessment showed that plants with majority of the MADS box member studies include tomato, apple, rice etc., after Arabidopsis. Furthermore, based on the degree of their multi-trait associations, FLC, SVP, SOC1, etc. are suggested to have relatively higher pleiotropic potential among others in plant growth, development and flowering process. The approach devised in this study is expected applicable for having basic understanding on any study subject of interest regardless of depth prior knowledge.

Metabolic dysfunction associated fatty liver disease (MAFLD), a globally prevalent chronic liver disease, has only limited pharmacological options. Both metformin and Ganwei, a botanical supplement containing a potent inducer for glycine N-methyltransferase (GNMT), have shown great promise for MAFLD. We conducted a 4-arm, randomized, placebo-controlled trial to investigate the safety and efficacy of metformin and Ganwei for MAFLD. Between September 2021 and March 2023, 64 patients with MAFLD were received metformin alone, Ganwei alone, combination of metformin and Ganwei, and placebo for 6 months. The primary MAFLD outcome was assessed by control attenuation parameter (CAP) and kilopascal (kPa) scores for liver steatosis and fibrosis, utilizing FibroScan. At 6 months after treatment, significant MAFLD improvements were observed in patients treated with Ganwei alone (repeated measures ANOVA test, p = 0.048 for both CAP and kPa scores). By employing a 6-point steatosis grade scale, the Ganwei alone arm exhibited a statistically better improvement than the placebo-controlled arm (mean score 1.1±1.9 vs. 0.1±0.7, p &lt; 0.01). In addition, patients in the Ganwei alone arm showed significant quality-of-life improvements. There was no serious adverse events observed. Our study demonstrated that Ganwei can be an effective treatment option for MAFLD patients. Clinical trial registration: NCT06244550.

NiV2O6 thin films were grown on glass slides with varying thicknesses using the nebulizer spray pyrolysis. The impact of thickness on the thin films' optical, structural, morphological, and electrical characteristics was systematically investigated. The X-ray diffraction and micro-Raman analysis confirmed the formation of the triclinic NiV2O6 system. Surface morphology and roughness variations in the as-deposited NiV2O6 films were studied using scanning electron microscopy (SEM) and a profilometer. Optical properties, including optical band gap (Eg), extinction coefficient (k), absorption coefficient (α), and refractive index (n), were determined through optical reflectance and transmittance measurements. The optical energy gap of the as-deposited NiV2O6 films decreased from 2.3 eV to 1.9 eV with increased layer thickness. Furthermore, the photo-detectivity of the films demonstrated an enhancement corresponding to the prolonged spray time. The sensitivity values obtained for visible irradiation are 328, 511, and 433 for samples S1, S2, and S3, respectively. The obtained results are imputed to the specific porous microstructure.

Fetal Alcohol Spectrum Disorders (FASD) represent a continuum of lifelong impairments resulting from prenatal exposure to alcohol, with significant global impact. The “spectrum” of disorders includes a continuum of physical, cognitive, behavioral, and developmental impairments, which can have profound and lasting effects on individuals throughout their lives impacting their health, social interactions, psychological well-being and every aspect of their lives. This narrative paper explores the intricate relationship between oxidative stress and epigenetics in FASD pathogenesis and its therapeutic implications. Oxidative stress, induced by alcohol metabolism, disrupts cellular components, particularly in the vulnerable fetal brain, leading to aberrant development. Furthermore, oxidative stress is implicated in epigenetic changes, including alterations in DNA methylation, histone modifications, and microRNA expression, which influence gene regulation in FASD patients. Moreover, mitochondrial dysfunction and neuroinflammation contribute to epigenetic changes associated with FASD. Understanding these mechanisms holds promise for targeted therapeutic interventions. This includes antioxidant supplementation and lifestyle modifications to mitigate FASD-related impairments. While preclinical studies show promise, further clinical trials are needed to validate these interventions' efficacy in improving clinical outcomes for individuals affected by FASD. This comprehensive understanding of the role of oxidative stress on epigenetics in FASD underscores the importance of multidisciplinary approaches for diagnosis, management, and prevention strategies. Continued research in this field is crucial for advancing our knowledge and developing effective interventions to address this significant public health concern.

In this article, we propose a machine learning based fine-grained vehicle classification method VehiClassNet, which effectively solves the challenges of precise vehicle recognition and classification in intelligent transportation systems by combining multi-scale feature extraction, cross modal fusion, and attention mechanisms. Our model demonstrated excellent performance on the Stanford Cars-196 dataset. The innovation of this study lies in its advanced multi-scale feature extraction, cross modal fusion, and attention mechanism. It not only improves classification accuracy, but also lays the foundation for further research on intelligent transportation systems and autonomous vehicle technology.

Embryonic development proceeds through a series of differentiation events. The mosaic version of this process (binary cell divisions) can be analyzed by comparing early development of Ciona intestinalis and Caenorhabditis elegans. To do this, we reorganize lineage trees into differentiation trees using the graph theory ordering of relative cell volume. Lineage and differentiation trees provide us with means to classify each cell using binary codes. Extracting data characterizing lineage tree position, cell volume, and nucleus position for each cell during early embryogenesis, we conduct several statistical analyses, both within and between taxa. We compare both cell volume distributions and cell volume across developmental time within and between single species and assess differences between lineage tree and differentiation tree orderings. This enhances our understanding of the differentiation events in a model of pure mosaic embryogenesis and its relationship to evolutionary conservation. We also contribute several new techniques for assessing both differences between lineage trees and differentiation trees, and differences between differentiation trees of different species. The results suggest that at the level of differentiation trees, there are broad similarities between distantly related mosaic embryos that might be essential to understanding evolutionary change and phylogeny reconstruction. Differentiation trees may therefore provide a basis for an Evo-Devo Postmodern Synthesis.

Dye-sensitized solar cells (DSSCs) belong to the group of thin-film solar cells which have been extensively researched for over two decades due to their low cost of fabrication, flexibility and ability to function under low-light conditions. Some issues have to be addressed in terms of energy losses, material integration, weak photocurrent generation and stability to improve the performance of DSSCs. One of the approaches to enhance the performance of DSSCs is through luminescent materials. These are materials which can absorb light and re-emit at different wavelengths which can be used to convert ultraviolet (UV) and near infra-red (NIR) light that are not efficiently utilized by the DSSCs, into visible light that can be absorbed. The main objective of this article is to provide a comprehensive review on the impact of luminescent materials in DSSCs. Research interest in luminescent materials particularly, down conversion, up conversion and quantum dots were analyzed from the “Web of Science”. It reflected a remarkable number of over 200, 000 publications in the past decade. Therefore, the state of the art of luminescent materials for enhancing the performance of the solar cells were reviewed, which showed significant potential in enhancing the performance of DSSCs.

White oak mortality is a significant concern in forest ecosystems due to its impact on biodiversity and ecosystem functions. Understanding the factors influencing white oak mortality is crucial for effective forest management and conservation efforts. In this study, we aimed to investigate the spatial pattern of white oak mortality rates at different locations of the eastern US and explore the underlying processes involved with the spatial distribution patterns. Multicycle Forest Inventory and Analysis data were compiled to capture white oak plots across the eastern US. White oak mortality data were collected across plot systems that utilized Diameter at Breast Height between two periods. Ripley’s K function was used to study the spatial pattern of white oak mortality rate. Results found clustered patterns at both local and broad scales and random patterns at broad scales only across the southern and northern regions. However, the central region indicated both clustered and random patterns at local scale only. We found uniform patterns at a broad scale across all regions. Our findings demonstrated the importance of the spatial pattern of the white oak mortality rate and the underlying factors associated with it. This research can inform forest management practices for the conservation of white oak populations at varying scales. Future research is needed for a comprehensive assessment of biotic and abiotic factors for forest management strategies at various spatial scales aimed at mitigating white oak mortality.

The Double Diaphragm Forming (DDF) process uses vacuum pressure and heat to pre-shape composite reinforcements between flexible diaphragms. Accurate modelling of heat transfer within DDF requires knowledge of the thermophysical properties of the constituent materials. This study investigates the specific heat and thermal conductivity of silicone diaphragms, carbon fibers (chopped and powdered), thermoplastic veils, and their combinations. Experiments measured the specific heat of each material using differential scanning calorimetry and the thermal conductivity of silicone and carbon fabric preforms using the transient plane source method and modified transient plane source method respectively. The influence of reheating cycles on specific heat of carbon fiber-veil samples and the effect of compaction on thermal conductivity of silicone and carbon fabrics were explored. While silicone exhibited linear specific heat behavior, the thermoplastic veil showed non-linearity due to phase change. The combined carbon fiber-veil samples demonstrated slight non-linear specific heat variations depending on reheating cycles. Thermal conductivity of the fabric preforms decreased with the addition of thermoplastic veil and under vacuum-compaction conditions. The established temperature-dependent property relationships provide valuable data for optimizing DDF preforming parameters and enhancing energy efficiency in composite manufacturing.

This paper reviews the multiple roles of machine learning in improving the resilience of power grids, especially in applying new energy storage technologies. Energy storage technologies, such as compressed air energy storage, flywheel energy storage, and superconducting coil energy storage, significantly improve the power grid's ability to respond to load fluctuations and emergencies through intelligent control and optimisation of machine learning algorithms. This not only helps to optimise energy dispatch and improve the operational efficiency and flexibility of the grid but also significantly improves the stability and reliability of the grid so that it is better able to meet future challenges and needs.

Research indicates that farmers’ demographic characteristics and production practices have safety and health implications. However, current systems do not identify organic farmers independently from conventional farmers, and literature on how organic and conventional farmers compare is very limited. We conducted a secondary analysis of 2012 Census of Agriculture data to compare organic and non-organic farms and principal operators (POs) in New Mexico (NM). Organic farms were smaller in size, and POs of farms with organic sales were significantly younger (55.8±9.5 vs. 60.5±5.5 years) and less experienced (19.5±6.8 vs. 25.2±6.8 years). Significant differences were also found in POs ethnicity, race, and primary occupation. More farms with organic sales had a female PO compared to farms with non-organic sales (27% vs. 19%). Other significant differences related to work arrangements, household income, living conditions, and access to Internet. National surveys and regional studies may not accurately typify and describe the local organic producer, which is essential in order to advance policy, develop health interventions, and properly address occupational safety and risk among organic farmers. This study makes a unique contribution to understanding the importance of surveillance and collecting place-based data that are specific to the organic producer.

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

This brief report presents a comparative analysis to examine differences in the mean dose of nitrate supplementation between studies with positive and negative results on exercise performance. Protocols of at least six days that had positive results used lower nitrate doses (~7.7 mmol), whereas those with negative results used higher doses (~10.2 mmol), showing a statistically significant difference (p < 0.05). These findings suggest long-term supplementation with higher doses (~10.2 mmol) may not improve exercise performance. Further research is essential to understand the underlying mechanisms.

Video capsule endoscopy (VCE) is increasingly used to decrease the discomfort among patients owing to its small size. However, VCE has a major drawback of not having narrow band imaging (NBI) functionality. The current VCE has the traditional white light imaging (WLI) only, which has poor performance in the computer-aided detection (CAD) of different types of cancer compared with NBI. Specific cancers, such as esophageal cancer (EC), do not exhibit any early biomarkers, making their early detection difficult. In most cases, the symptoms are unnoticeable, and EC is diagnosed only in later stages, making its 5-year survival rate below 20% on an average. NBI filters provide particular wavelengths that increase the contrast and enhance certain features of the mucosa, thereby enabling early identification of EC. However, VCE does not have a slot for NBI functionality because its size cannot be increased. Hence, NBI image conversion from WLI can be achieved only in post-processing at present. In this study, a complete arithmetic assessment of the decorrelated color space was conducted to generate NBI images from WLI images for VCE of the esophagus. Three parameters, namely, structural similarity index metric (SSIM), entropy, and peak-signal-to-noise ratio (PSNR), were used to asses the simulated NBI images. Results show the good performance of the NBI image reproduction method, with SSIM, entropy difference, and PSNR values of 93.215%, 4.360, and 28.064 dB, respectively.

Abstract: Patients with Cleft Lip Palate (CLP) require comprehensive treatment, so the people who take care of them must constantly provide care and support, along with continuous care and support from their caregivers, often extending until youth. The purpose was to compare the Quality of Life (QoL) of caregivers of children with CLP with those without the condition. A cross-sectional paired study was conducted at the Dental Clinic of the Escuela Nacional de Estudios Superiores (ENES León), National Autonomous University of Mexico (UNAM), from May to December 2021. Primary caregivers of patients with CLP (n=70) were included as well as those of the patients without CLP (n=70) of the same institution. Sample size was calculated considering an OR=2.5, establishing a convenience selection. Caregivers answered the WHOQoL Bref instrument to assess QoL. Of the total sample analyzed, 88.6% of the caregivers were female (p>0.05). Caregivers of patients with CLP reported poor QoL (64.8%), compared to caregivers in the control group (p

Salmonella can cause acute and chronic infections in humans. Salmonella species are known to cause food poisoning and other diseases in developing countries. Their role in the pathogenesis of these diseases has received increased international attention. Despite numerous advances in sanitation, they still can infect humans and cause outbreaks in developed countries. For example, Salmonella causes about 1.2 million illnesses in the US each year with over 450 deaths. Additionally, Salmonella outbreaks cause significant losses to chicken producers globally. The Salmonella species is also prone to acquiring resistance to various classes of antibiotics. Hence, the need for a paradigm shift from antibiotics to bacteriophages to manage, control and treat bacterial infections. The ɛ34 phage belongs to Podoviruses and categorized into the P22-like phages. The P22-like phages include ɛ34, ES18, P22, ST104, and ST64T. In this work, we investigated the antibacterial property of ɛ34 phage tailspike protein against Salmonella newington (S. newington). We demonstrate here that, the phage’s tailspike protein enzymatic property as a LPS hydrolase synergizes with Vero Cell culture supernatant in killing S. newington. Using decellularized cartilage scaffold as an ex vivo tissue model, the ɛ34 TSP protected the scaffold from S. newington biofilm formation. Computational analysis of the ɛ34 TSP interaction with membrane proteins of S. newington demonstrated a higher probability (0.7318) of binding to ompA of S. newington, and when docked to ompA extracellular component, it produced a high free energy of -11.3kcal/mol. We also demonstrate the resistance/sensitivity of the tailspike to the digestive enzyme trypsin. The data obtained in this work indicates that the trypsin resistant tailspike protein of Ɛ34 phage can be formulated as a novel antibacterial agent against S. newington.

Direct competitions involve competitors that mutually contend for the same resource or objective. A controlled, well-documented, and data-rich field involving direct competition is represented by sports. Usually the winner of sports competitions is believed to be the one with the greatest talent, however, there are other factors that contribute to the outcome of a competition, there are in fact random, unpredictable events that can change the outcome of a competition. For this reason, if one wants to understand the properties of a competition is necessary to have a method to evaluate the importance of the talent or aptitude of a competitor, as opposed to the importance of chance, in determining the outcome of the competition itself. In this work we study the sport of tennis using the data obtained from the Association of Tennis Professionals (ATP) tournaments. We construct an agent-based model that is able to produce data analogous to the real one; this model depends on three parameters, the talent, the action of chance, and the weight of talent. In particular, we don't fix the values of these parameters and we fit the model results using a genetic algorithm, in this way, we study all the possible combinations of parameters, in the parameter space, that are able to reproduce the real systems data. We show that the model fits well the real data only for limited regions of the parameter space. On these limited regions of the parameter space are possible further optimization of the model results, limiting the values of parameters. In this way, our agent-based model, by means of this genetic algorithm calibration, is able to provide us with useful information without any a priori constraints.

The repeated application of herbicides has led to the development of herbicide resistance. Models are useful for identifying key processes and understanding the evolution of resistance. This study developed a spatially explicit model at a landscape scale to examine the dynamics of Lolium rigidum populations in dryland cereal crops and the evolution of herbicide resistance under var-ious management strategies. Resistance evolved rapidly under repeated herbicide use, driven by weed fecundity and herbicide efficacy. Although fitness costs associated with resistant plants re-duced resistance evolution, they did not affect the speed of spread. The most effective strategies to slow resistance involved diversifying cropping sequences (e.g., crop rotation) and the herbicide applications (e.g., rotating different herbicide modes of action). Pollen flow was the main disper-sal vector, with seed dispersal also contributing significantly. Strategies limiting seed dispersal effectively decreased resistance spread. However, the use of a seed-catching device at harvest could unintentionally enrich resistance in the area. It would be beneficial to optimise the move-ment of harvesters between fields. The model presented here is a useful tool that may assist in the exploration of novel management strategies within the context of site-specific weed management at the landscape scale, as well as in the advancement of our understanding of resistance dynamics.

Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 extracted articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, ISPRS Journal Of Photogrammetry And Remote Sensing as the leading journals. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by USA (11.76%), France (5.88 %), Spain (4%) and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most commonly used data sources. Benchmark datasets such as ISPRS Vaihingen &amp; Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder-decoder, and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue, and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions.

The additional sex combs-like (ASXL) family, a mammalian homolog of the additional sex combs (Asx) of Drosophila, has been implicated in transcriptional regulation via chromatin modifications. Abnormal expression of ASXL family genes leads to myelodysplastic syndromes and various types of leukemia. De novo mutation of these genes also causes developmental disorders. Genes in this family and neighbor genes are evolutionary conserved in humans and mice. This review provides a comprehensive summary of epigenetic regulations associated with ASXL family genes. Their expression is commonly regulated by DNA methylation at CpG islands preceding transcription starting sites. Their proteins primarily engage in histone tail modifications through interactions with chromatin regulators (PRC2, trithorax complex, PR-DUB, SRC1, HP1α, and BET proteins), and with transcription factors, including nuclear hormone receptors (RAR, PPAR, ER, and LXR). Recently, non-coding RNAs have been identified following mutations in the ASXL1 or ASXL3 gene, along with circular ASXLs and microRNAs that regulate ASXL1 expression. The diverse epigenetic regulations linked to ASXL family genes collectively contribute to tumor suppression and developmental processes. Our understanding of ASXL-regulated epigenetics may provide insights into the development of therapeutic epigenetic drugs.

The present study aims at investigating the relationship between Initial Public Offerings (IPOs) variables mainly underpricing on one hand, and the brand value measures on the other. Our final data set is 104 international brand companies. We implement empirical approach using hierarchical OLS regression and descriptive statistics. We show that underpricing is positively related to brand value which emphasizes the marketing role of going public and underpricing in enhancing brand equity through the product market, which additionally confirms some information asymmetry models. We also find that on average brand companies had not been recognized as brands at the IPO time. Moreover, we show the positive role of private equity in enhancing brand value, additionally, the non-linear association between underpricing and brand value is not evident. Finally, we draw some policy implication and suggestions for future research.

Ixodes scapularis is a blood-feeding obligate ectoparasite responsible for transmitting the Lyme disease (LD) agent, Borrelia burgdorferi. During the feeding process, I. scapularis injects B. burgdorferi into the host along with its saliva, facilitating the transmission and colonization of the LD agent. Tick calreticulin (CRT) is one of the earliest tick saliva proteins identified and is currently utilized as a biomarker for tick bites. Our recent findings revealed elevated levels of CRT in the saliva proteome of B. burgdorferi-infected I. scapularis nymphs compared to uninfected ticks. Differential precipitation of proteins (DiffPOP) and LC-MS/MS analyses were used to identify the interactions between Ixs (I. scapularis) CRT and human plasma proteins and further exploring its potential role in shielding B. burgdorferi from complement killing. We observed that although yeast expressed recombinant (r) IxsCRT binds to the C1 complex (C1q, C1r, and C1s), the activator of complement via the classical cascade, it did not inhibit deposition of the membrane attack complex (MAC) via the classical pathway. Intriguingly, rIxsCRT binds intermediate complement proteins (C3, C5, and C9) and reduces MAC deposition through the lectin pathway. Despite inhibition of MAC deposition in the lectin pathway, rIxsCRT did not protect a serum sensitive B. burgdorferi strain (B314/pBBE22Luc) from complement-induced killing. As B. burgdorferi establishes a local dermal infection before disseminating to secondary organs, it is noteworthy that rIxsCRT promotes the replication of B. burgdorferi in culture. We hypothesize that rIxsCRT may contribute to the transmission and/or host colonization of B. burgdorferi by acting as a decoy activator of complement and by fostering B. burgdorferi replication at the transmission site.

Prostate cancer lung metastasis represents a clinical conundrum due to their implication of advanced disease progression and the complexities they introduce in treatment planning. As the disease progresses to distant sites such as the lung, the clinical management becomes increasingly intricate, requiring tailored therapeutic strategies to address the unique characteristics of metastatic lesions. This review seeks to synthesize the current state of knowledge surrounding prostate cancer metastasis to the lung, shedding light on the diverse array of clinical presentations encountered, ranging from subtle radiological findings to overt symptomatic manifestations. By examining the diagnostic modalities utilized in identifying this metastasis, including advanced imaging techniques and histopathological analyses, the review aims to provide insights into the diagnostic landscape and the challenges associated with accurately characterizing lung metastatic lesions in prostate cancer patients. Moreover, the review delves into the nuances of therapeutic interventions employed in managing prostate cancer lung metastasis, encompassing systemic treatments such as hormonal therapies and chemotherapy, as well as metastasis-directed therapies including surgery and radiotherapy.

Ferroptosis is implicated in viral neuropathogenesis and may underlie HIV-associated neurocognitive impairment (NCI). Emerging data also suggest differences in brain iron transport by sex. We hypothesized that circulating ferritins that inhibit ferroptosis associate with neurocognitive function and NCI in people with HIV (PWH) in a sex-biased manner. Serum ferritin heavy-chain-1 (Fth-1), ferritin light-chain (Ftl), and urinary F2-isoprostanes (uF2-isoPs, specific lipid peroxidation marker) were quantified in 324 PWH (including 61 women) with serial global (NPZ4) and domain-specific neurocognitive testing. Biomarker associations with neurocognitive test scores and NCI were evaluated by multivariable regression; correlations with uF2-isoPs were also assessed. Higher Ftl and Fth-1 associated with less NCI in all PWH (adjusted odds ratios 0.53, 95% Confidence Interval (95% CI) 0.36-0.79 and 0.66, 95% CI 0.45-0.97, respectively). In women, higher Ftl and Fth-1 associated with better NPZ-4 (Ftl: adjusted beta (β)=0.15, 95% CI 0.02-0.29; Ftl-by-sex βinteraction=0.32, p=0.047) and domain-specific neurocognitive test scores. Effects on neurocognitive performance persisted for up to 5 years. Levels of both ferritins correlated inversely with uF2-isoPs in women (Ftl: rho=-0.47, p<0.001). Circulating Ftl and Fth-1 exert sustained, sex-biased neuroprotective effects in PWH, possibly by protecting against iron-mediated lipid peroxidation (ferroptosis). Larger studies are needed to confirm the observed sex differences and further delineate underlying mechanisms.

Colorectal liver metastases (CRLM) patients combined with hepatic lymph node metastases was a negative prognostic factor associated with outcomes. Up to now, there was still lack of a reliable method to identify the status of hepatic lymph nodes. The aim of this study was to investigate the predictive ability of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) models to distinguish between benign and malignant hepatic lymph nodes in CRLM patients who underwent neoadjuvant chemotherapy prior to surgery. In total, 97 CRLM patients with pathologically proved hepatic lymph node status were included. Various quantitative parameters, including the apparent diffusion coefficient (ADC) from mono-exponential model, D, D*, and f derived from intravoxel incoherent motion (IVIM) model, as well as DDC and α from stretched-exponential model (SEM), were measured. Multivariate analysis revealed that the pre- treatment DDC value and the short diameter of the largest lymph node after treatment were independent predictors of metastatic hepatic lymph nodes. A nomogram combining these two factors demonstrated excellent performance in distinguishing between benign and malignant lymph nodes in CRLM patients, with an area under the curve (AUC) of 0.873. Therefore, the nomogram can serve as a preoperative assessment tool for determining the status of hepatic lymph nodes and aiding in the decision-making process for surgical treatment in CRLM patients.

Puerto Rico's geographic and climatic conditions render it highly susceptible to natural disasters, particularly hurricanes. The devastation caused by Hurricane Maria in 2017 highlighted the fragility of the island's centralized energy infrastructure, which led to prolonged power outages and severe disruptions in essential services. This review explores the potential of biomass energy to enhance resilience in post-disaster scenarios, focusing on Puerto Rico as a case study. Biomass energy, derived from organic materials like agricultural residues, woody biomass, and municipal solid waste, offers a renewable and locally sourced power solution that can be rapidly deployed. Key biomass conversion technologies, including direct combustion, gasification, anaerobic digestion, and pyrolysis, are examined for their applicability and benefits in emergency energy provision. The implementation of biomass-powered microgrids in Puerto Rico, such as those by Casa Pueblo and Arensis, demonstrates the feasibility and effectiveness of decentralized energy systems in disaster recovery. These microgrids provide reliable power to critical infrastructure, reduce dependence on fossil fuels, and support waste management efforts. The review also discusses ongoing research and development in advanced biomass technologies, such as second-generation biofuels, algae-based biomass, and biomass-to-hydrogen conversion, which can further enhance energy efficiency and sustainability. Additionally, the economic, environmental, and social impacts of biomass energy are evaluated, highlighting its role in job creation, waste reduction, emission control, and community resilience. Policy recommendations include streamlining permitting processes, enhancing financial incentives, developing feedstock supply chains, and incorporating biomass into disaster recovery plans. The findings suggest that integrating biomass energy into Puerto Rico’s energy strategy can significantly improve its capacity to respond to and recover from natural disasters, promoting long-term sustainability and resilience.

Electrifying aircraft, a crucial advancement in the aviation industry, aims to cut pollutive emissions and boost energy efficiency. Traditional aircraft depend on fossil fuels, which contribute significantly to greenhouse gas emissions and environmental pollution. Despite progress in electric propulsion and energy storage technologies, challenges such as low energy density and integration issues persist. This paper provides a comprehensive thematic and bibliometric analysis to map the research landscape in aircraft electrification, identifying key research themes, influential contributors, and emerging trends. The study applies natural language processing to unstructured bibliographic data and cross-sectional statistical methods to analyze publications, citations, and keyword distributions across various categories related to aircraft electrification. The findings reveal significant growth in research output, particularly in energy management and multidisciplinary design analysis. Collaborative networks highlight key international partnerships, with the United States and China being key research hubs, while citation metrics highlight the impact of leading researchers and institutions in these countries. This study provides valuable insights for researchers, policymakers, and industry stakeholders, guiding future research directions and collaborations.

We introduce a new generalized inverse which is a natural generalization of (pseudo) right core inverse in a Banach *-algebra. We characterize this generalized inverse by using right core and quasi-nilpotent decomposition. We then present its polar-like characterization and investigate related algebraic properties. Finally, the right core-EP inverse is characterized by certain new ways.

The group delay response of chirped fiber Bragg gratings (CFBG) written in polarization-maintaining fiber can be measured using the beat spectrum of the reflections from the two polarization axis, with a resolution and reproducibility better than 50 fs. The method is theoretically described, and we analyze the effect of group delay ripples (GDR) on the performance of a stretcher-compressor pair used in chirped pulse amplification systems. Ripples with a spectral period of the order of the pulse bandwidth are the most deleterious. Those are readily captured with our method, despite the limit on resolution due to the fiber polarization beat length. As a rule of thumb, the GDR should not exceed half the pulse duration. The reconstructed phase of the CFBG is also used to predict the pulse compression performance for a given device. Measurement of GDR as low as 161 fs is experimentally demonstrated.

Natural language Processing (NLP) has significantly transformed in the last decade, especially in the field of Language Modeling. Large Language Models (LLMs) have achieved SOTA performances on Natural Language Understanding (NLU) and Natural Language Generation (NLG) tasks by learning language representation in self-supervised ways. This paper provides a comprehensive survey to capture the progression of advances in Language Models. In this paper, we examine the different aspects of Language Models, which started with a few million parameters but have reached the size of a trillion in a very short time. We also look at how these LLMs transitioned from task-specific to task-independent to task-and-language-independent architectures. This paper extensively discusses different pre-training objectives, benchmarks, and transfer learning methods used in LLMs. It also examines different fine-tuning and In-Context learning techniques used in downstream tasks. It also explores how LLMs can perform well across many domains and datasets if sufficiently trained on a large and diverse dataset. Next, it discusses how, over time, the availability of cheap computational power and large datasets have improved LLM’s capabilities and raised new challenges. As part of our study, we also inspect LLMs from the lens of scalability to see how their performance is affected by the model’s depth, width, and data size. Lastly, we provide an empirical comparison of existing trends and techniques and a comprehensive analysis of where the field of LLM currently stand.

Weathering and erosion transport minerals and organics toward the sea or lake bottoms over geologic time. The finest solids are deposited in lower, less turbulent areas, such as lake bottoms and continental shelves. They sometimes stack up to thicknesses of kilometers, and begin compacting. These sediment sections are called shales, and as initially deposited in water, shales can have porosities up to 50-80{\%} water, As they are buried, many alteration products from oil to slate are produced due to overburden and temperature increasess, making them important to study. A preceeding paper \cite{1} showed that pressure solution is the primary mechanism for porosity reduction, with possible mechanical compaction at shallow depths. Without naming the mineral(s) involved, it postulated that the greater the product of the water and pore interfaces, the faster the reaction would proceed. This term is $ \varphi^{4m/3}(1-\varphi)^{4n/3}$ , where $\varphi$ is porosity and m and n are numbers close to unity. The large exponents, {\em 4/3}, recognize that the reaction occurs at the molecular scale at which the surfaces are rough. A second term, $\exp^{(-E/RT)}$ , indicates that the reaction is impeded by a quantum energy barrier, E, with diminished impeding power as increased available thermal energy, represented by the absolute temperature, T, becomes available at greater depths in the Earth. These two factors combine to allow porosity $\varphi$ to reduce with time, or equivalently for the fraction of solids, $(1-\varphi)$, to increase with time,$\left. \frac{\partial(ln(1-\varphi) ) }{\partial t } \right |_{\sigma} = (\varphi )^{4m/3}( 1-\varphi )^{4n/3}Ae^{-E/(RT)}$. { \em Here it is shown that this equation can cover quantitatively any actual deposition rates which may have been experienced by the six sections studied, the actual deposition rates being unknown for these cases. Hence a time-depth depositional history for any new shale section, known in detail, would allow determination of the parameters m, n, E and a lumped proportionality constant A. This was accomplished by showing that, for a wide range of depositional rates, r, the range of E for any of the studied sections is small compared to laboratory measurements of quartz solution\cite{2}, 24+-15kJ/mol. Results were obtained over this range of r's using the previously determined m and n, and porosity and temperature profiles. The presently existing porosity profiles necessarily incorporate any overpressure or underpressure conditions that may have existed in the past or currently, as the net difference between overburden and pore pressure is a primary driving force for pressure solution. $SiO_2$ usually comprises 20-50{\%} of shales. In conclusion, pressure solution of quartz can account for vertical compaction of shales quantitatively in the studied examples. Separately, a quasi-eqivalent method for discussing reduced porosities in Macron 2 versus Macron 1 as increased vertical stress, rather than additional horizontal stress, is illustrated. Experiments are suggested to clarify the pressure solution mechanism. The role of horizontal forces is discussed. From a global point of view, shale compaction by pressure solution is a quantified step in the cycling of continental rocks, beginning with rifting.