Minimal/mild endometriosis (MME) is independently associated with reduced fecundity rates. In this review article, we discuss the role of laparoscopic surgery in enhancing fertility outcomes of patients with MME. Laparoscopic management of MME enhances fecundity and increases chances of spontaneous conception in appropriately selected cases. However, laparoscopy cannot be routinely recommended in asymptomatic patients with the sole purpose of diagnosing and treating potentially present MME. Equally, based on existing evidence, laparoscopic management of MME cannot be routinely recommended prior to in-vitro fertilisation (IVF) attempts, as robust evidence of benefit is lacking. As an overlap between unexplained infertility and MME cases likely exists, the development of reliable, widely available, non-invasive tests for the diagnosis of MME may revolutionize the management of cases currently classified as unexplained infertility. In a disease as diverse as endometriosis, management decisions should be based on a multitude of factors. Future studies should focus on reporting outcomes of interventions for MME on fertility and obstetric outcomes, clearly differentiating between disease stages and phenotypes.

Discoloration of endodontically treated teeth poses a significant aesthetic concern for many individuals, impacting their confidence and self-image. Ceramic veneers have emerged as a popular solution for the aesthetic restoration of such teeth. This paper explores the role of ceramic veneers in addressing tooth discoloration, examining their efficacy, durability, and aesthetic outcomes, via a clinical case. Various factors influencing the selection of ceramic veneers as a treatment modality, including shade matching, preparation techniques, and adhesive bonding, are discussed. Additionally, advancements in materials and techniques have enhanced the versatility and aesthetic appeal of ceramic veneers, making them a valuable option for achieving natural-looking and durable aesthetic restorations in individuals with discolored endodontically treated teeth.

This research evaluates the technical and financial feasibility of using Inhibitor A for brine treatment in geothermal energy production, comparing it with traditional brine acidification. Conducted at a geothermal site in Central Java, Indonesia, the study uses Decision Tree Analysis and risk Expected Monetary Value (EMV) to assess both methods. Inhibitor A demonstrates a 70% reduction in scaling compared to untreated brine, with laboratory tests indicating a 10 ppm dosage as optimal for minimizing silica scaling. Despite higher initial costs, Decision Tree Analysis shows that Inhibitor A has a lower overall risk profile than acidification. EMV analysis confirms its cost-efficiency when considering potential risks and benefits. This research advocates for the adoption of Inhibitor A, highlighting its long-term benefits and sustainability over traditional methods. The findings emphasize the importance of innovative solutions in enhancing geothermal energy systems’ efficiency and sustainability. By integrating technical performance with financial analysis, this study provides a comprehensive evaluation to inform decision-making in the geothermal sector, promoting more effective and sustainable brine treatment practices.

In recent years, 360o videos have gained the attention of researchers investigating their educational potential. Given the ongoing debate about their impact on learning, particularly with young students where existing research is minimal, a comprehensive study was conducted to evaluate their effectiveness relative to other media forms. The study involved 44 kindergarten students learning about wild animals over nine sessions. The 360o videos were compared to printed materials and conventional videos to measure knowledge acquisition, motivation, enjoyment/positive feelings, immersion, and ease of use. The findings suggest that, compared to printed materials, 360o videos enhance knowledge acquisition. However, this advantage does not extend to comparisons with conventional videos. Similarly, while enjoyment and motivation were higher for 360o videos compared to printed materials, they did not surpass those associated with conventional videos. Despite their potential, 360o videos were deemed the least easy to use, likely due to the employment of low-cost HMDs. Immersion emerged as the only factor where 360o videos excelled, demonstrating a notable influence on learning outcomes. The implications of the study are also discussed.

PARK7, also known as DJ-1, plays a critical role in protecting cells by functioning as a sensitive oxidation sensor and modulator of antioxidants. DJ-1 acts to maintain mitochondrial function and regulate transcription in response to different stressors. In this study, we showed that cell lines vary based on their antioxidation potential at basal conditions. The transcriptome of HEK293 cells was tested following knockdown (KD) of DJ-1 using siRNAs which reduced the DJ-1 transcripts to only 12% of the original level. We compared the expression levels of 14k protein-coding transcripts, and 4.2k non-coding RNAs relative to control cells treated with non-specific siRNAs. Among the coding genes, ~200 upregulated differentially expressed genes (DEGs) signified a coordinated antiviral innate immune response. Most genes were associated with regulation of type 1 interferons (IFN) and induction of inflammatory cytokines. About a quarter of these genes were also induced in cells treated by non-specific siRNAs that were used as a negative control. Beyond the antiviral response, 114 genes were specific to KD of DJ-1 with enrichment in RNA metabolism and mitochondrial functions. A smaller set of downregulated genes (58 genes) were associated with dysregulation in membrane structure, cell viability, and mitophagy. We propose that KD of DJ-1 diminish its protective potency against oxidative stress, rendering the cells labile and responsive to dsRNA signal by activation of a large number of genes, many of which drive apoptosis, cell death, and inflammatory signatures. The KD of DJ-1 highlights its crucial role in regulating genes associated with antiviral responses, RNA metabolism, and mitochondrial functions, apparently through alteration in STAT activity and downstream signaling. Given that DJ-1 is highly expressed in metastatic cancers, targeting DJ-1 could be a promising therapeutic strategy where manipulation of DJ-1 levels may reduce cancer cell viability and enhance the efficacy of cancer treatments.

Incorporating ChatGPT into the classroom has several benefits that improve the educational experience for both teachers and students. ChatGPT seems useful in preparing students for success in the digital age, from personalized learning and increased instructional content to fostering collaboration, enabling bilingual classrooms, and encouraging creativity. This review offers insights regarding the perks of using ChatGPT in education as a tool for students. Among the many benefits of implementing ChatGPT in the classroom are enhanced accessibility, enhanced personalized learning, development of critical thinking skills, administrative support for educators, promotion of collaborative learning, and preparedness for a technologically advanced future. These advantages demonstrate how AI technologies have the capacity to drastically change teaching strategies and outcomes. While problems remain, the benefits of integrating ChatGPT in teaching are enormous. By integrating AI technology, educators may develop more dynamic and individualized learning environments that respond to the demands of 21st-century learners.

We present a partition of quantum observables in an open quantum system which is inherited from the division of the underlying Hilbert space or configuration space. It is shown that this partition leads to the definition of an inhomogeneous continuity equation for generic, non-local observables. This formalism is employed to describe the local evolution of the von Neumann entropy of a system of independent quantum particles out of equilibrium. Crucially, we find that all local fluctuations in the entropy are governed by an entropy current operator, implying that the production of entanglement entropy is not measured by this partitioned entropy. For systems linearly perturbed from equilibrium, it is shown that this entropy current is equivalent to a heat current, provided that the system-reservoir coupling is partitioned symmetrically. Finally, we show that any other partition of the coupling leads directly to a divergence of the von Neumann entropy. Thus, we conclude that Hilbert-space partitioning is the only partition of the von Neumann entropy which is consistent with the Laws of Thermodynamics.

This paper is showing the results of a study focused on the occurrence of failures in heterogeneous structural materials, especially on the issue of modelling the formation and propagation of cracks. Attention is paid to the application of the finite element method to various types of materials in order to find critical parameters determining the behaviour of materials during the damage process. Application of damage mechanisms and approaches to modelling the origin of crack propagation by modifications in FEM systems are presented. Furthermore, some practical applications are tested. The main effort is devoted to composites and searching for procedures when using cohesion models for their more accurate modelling, especially near the stress concentrators, or before the crack tip. The use of the cohesive approach of elements that represent the physical nature of energy release in front of the crack front has proven to be promising not only in the direct use of cohesive elements, but also in combination with modified methods of standard finite elements. The authors of this article use their long-term experience in the given issue, which they consistently confront with developments in this area in recent years.

The many limitations of implementing anticancer strategies under the term “precision oncology” have been extensively discussed. While some authors propose promising future directions, others are less optimistic and use phrases such as illusion, hype and false hypotheses. The reality is revealed by practicing clinicians and cancer patients in various online publications, one of which has stated that “in the quest for the next cancer cure, few researchers bother to look back at the graveyard of failed medicines to figure out what went wrong.” The message is clear: novel therapeutic strategies with catchy names (e.g., synthetic “lethality”) have not fulfilled their promises despite decades of extensive research and clinical trials. The main purpose of this review is to discuss key challenges in solid tumor therapy that surprisingly continue to be overlooked by the nomenclature committee on cell death and numerous other authors. These challenges include: the impact of chemotherapy-induced genome chaos (e.g., multinucleation) on resistance and relapse, oncogenic function of caspase 3, cancer cell anastasis (recovery from late stages of apoptosis), and pitfalls of ubiquitously used preclinical chemosensitivity assays (e.g., cell “viability” and tumor growth delay studies in live animals) that score such pro-survival responses as “lethal” events. Studies outline herein underscore the need for new direction in the management of solid tumors.

With the escalation of size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of ML in generating behavioral telemetry data using Long-Short Term Memory network and spoofing requests for the analyzed traffic to look legitimate. For this research, a custom testing environment was built, that listens for mouse and keyboard events and analyzes them accordingly. While the economic feasibility of this attack currently limits its immediate threat, advancements in technology could make it more cost-effective for attackers in the future. Therefore, proactive development of countermeasures remains essential to mitigate potential risks and stay ahead of evolving attack methods.

Solar energetic particles (SEPs) are bursts of high-energy particles that originate from the Sun and can last for hours or even days. The aim of this study is to understand how the characteristics of energetic particles was affected by the characteristic parameters of corotating interaction regions (CIRs). In particular, the particle intensity distribution with time and space in different CIRs are studied. The propagation and acceleration of particles are described by the focused transport equation (FTE). We used a three-dimensional magnetohydrodynamic (MHD) model to simulate the background field including CIRs. By changing the inner boundary conditions, we constructed CIRs with different solar wind speeds, angles between the polar axis and rotation axis, and the azimuthal widths of the fast streams. Impulsive particles are injected at the inner boundary. We then study the SEPs in different backgrounds. The results show that the CIR widths are related to the solar wind speed, tilt angles, and the azimuthal widths of the fast stream. The acceleration of particles in the reverse and forward compression regions is mainly influenced by the solar wind speed difference and the slow solar wind speed, respectively. Particles with lower energy (sub-MeV) are more sensitive to the parameters of CIRs. Different CIR parameters also greatly affect the spatio-temporal distribution of particle intensities in different ways. These results indicate that, it is crucial to consider the conditions of large-scale structure in interplanetary space when studying or predicting energetic particles. Additionally, our findings provide empirical guidance for forecasting the characteristics of SEPs.

In breast cancer, epithelial-mesenchymal transition (EMT) is positively associated with programmed death ligand 1 (PD-L1) expression and immune escape, and TWIST1 induces EMT and cancer metastasis. However, how TWIST1 regulates PD-L1 and immune evasion is unknown. This study analyzed TWIST1 and PD-L1 expression in breast cancers, investigated the mechanism for TWIST1 to regulate PD-L1 transcription, and assessed the effects of TWIST1 and PD-L1 in cancer cells on the cytotoxicity of CD8+ effector T cells. Interestingly, TWIST1 expression is associated with high-level PD-L1 in triple negative breast cancers (TNBCs). Overexpression and knockdown of TWIST1 robustly upregulates and downregulate PD-L1 expression, respectively. TWIST1 binds to the PD-L1 promoter and recruits the TIP60 acetyltransferase complex in a BRD8-dependent manner to promote PD-L1 expression, which drives immune escape of breast cancer cells by accelerating the exhaustion of CD8+ T cells. Accordingly, knockdown of TWIST1 or BRD8 or blockade of PD-L1 with a PD-L1 antibody significantly enhances CD8+ T cells to inhibit breast cancer cell growth. These results demonstrate that TWIST1 directly induces PD-L1 expression in breast cancer cells to drive immune evasion. Targeting TWIST1, BRD8 or PD-L1 in TNBC should facilitate CD8+ T cell-mediated immune therapy. TWIST1 may also serve as a biomarker for anti-PD-L1 immunotherapy.

Sorghum-nematode interactions were previously purported to produce chemicals that confer pre-infectional nematode resistance to the infective second-stage juveniles (J2) of the root-knot (Meloidogyne species) nematodes in the rhizosphere, without any contact to J2 bodies. We tested the null hypothesis in four sorghum cultivars with a wide range of pre-infectional nematode resistance relative to the susceptible tomato cultivar. Post a 56-day exposure of Meloidogyne enterolobii, J2 from soil and root samples were subjected to step-wise fixative solutions and then mounted on slides. Length-related and diameter-related morphometrics were measured using an OMAX microscope – equipped with a digital measuring software. The study was conducted over two seasons and the seasonal interactions on morphometrics were not significant. Relative to the tomato cultivar, sorghum treatments generally increased the length-related morphometrics, but decreased the diameter-related variables in both soil J2 and root J2. The null hypothesis was therefore rejected in favour to the assumption that upon exposure of J2 bodies to botanical chemicals in both soil and root samples, with certain morphometrics being adjusted to ameliorate the potential damage of hydrostatic pressure on internal organs of the infective juveniles. In conclusion, in pre-infectional nematode resistance, J2 bridge chemicals in the rhizosphere and enter the root system, where chemicals still have effects on morphometrics of nematodes.

Cellular Technology came into existence in Japan in 1979. From that time, we have seen many advances in technology. Currently, most of the person uses either 4G or 5G, among which 5G is a newer technology. 4G network was the first network to use LTE. LTE stands for Long Term Evaluation. Most people get confused between 4G and LTE, they assume both to be the same. In reality, LTE has evolved on the 4G technology. It was first introduced in 2008 to provide higher efficiency and data rates to the cellular network. It was designed to evolve the performance of the networks which includes the evolution of hardware components, optimization of software, power and cost efficiency. This work presents a study on LTE, LTE-A and VoLTE technology. We also discussed about the several packet scheduling algorithms that are being used for transmission of packets. It brings out new opportunities for the researchers to work on increasing the efficiency of those algorithms by modifications.

Artistic image completion is fundamental for the preservation and restoration of invaluable art paintings and it has experienced significant progress through the implementation of deep learning methodologies. Despite these advancements, challenges persist, even with sophisticated approaches like Generative Adversarial Networks (GANs), particularly in achieving optimal results for high-resolution paintings. Small-scale texture synthesis and the inference of missing information from distant contexts present persistent issues, leading to distortions in lines and unnatural colors, especially in art paintings with complicated structures and textures. Concurrently, patch-based image synthesis has evolved by incorporating global optimization on the image pyramid to enhance structural coherence and details. However, methods relying on gradient-based synthesis encounter obstacles related to directionality, inconsistency, and the heavy computational burdens associated with solving the Poisson equation in non-integrable gradient fields. This paper introduces a groundbreaking approach, integrating Weighted Similarity-Confidence Laplacian Synthesis, to comprehensively address these challenges and advance the field of artistic image completion. This proposal addresses challenges not only in high-resolution artistic image completion but also makes a significant contribution to the broader field of patch-based synthesis by utilizing the Laplacian pyramid for enhanced edge-aware correspondence search. Experimental result confirms the effectiveness of our approach, offering promising outcomes for the preservation and restoration of art paintings with complicated details and irregular missing regions.

A general methodology for loan amortization under arbitrary discount functions is discussed. It is shown that it is always possible to uniquely define a scheme for constructing the loan amortization schedule with an arbitrary assigned discount function. It is also shown that even if the loan amortization is carried out from the sequence of principal payments and the sequence of accrued interest, the underlying discount function can be uniquely determined at the maturities corresponding to the installment payment dates. As a special case of the proposed approach, we derive the amortization method according to the law of simple interest.

The water resources policy under climate change impacts water security, which should ensure enough water of good quality for humans and nature. Integrated water management is the state-of-the-art policy support model that, after 20 years in use, failed to achieve in many countries its primary goal of water bodies' good ecological status. Here, we show that this is due to the substantial externalities the anthropocentric character of the model generates. From the historical analysis, three main results are obtained: 1) the nature-human interplay is always composed of the coexistence of two opposite behaviors of conflict and cooperation; 2) this contradiction is a dialectical, general ontological attribute; 3) on the balance of power between nature and humans, three clusters are identified: i) naturalistic; ii) dualistic; iii) anthropocentric. A novel behaviorist dialectical conflict resolution model can resolve the differences between stakeholders and natural laws. The harmonic symbiosis of humans with nature removes environmental externalities and can lead to sustainable water security. Three case studies from the literature illustrate the merits of the new dialectical model.

Intra Voxel Incoherent Motion (IVIM) imaging provides non-invasive perfusion measurements, eliminating the need for contrast agents. The aim of this work was to explore the feasibility of IVIM in whole brain perfusion studies, where isotropic 1mm voxel is widely accepted as a standard. This study treats on validity of time limited, precise, segmentation ready Whole Brain IVIM imaging protocol suitable for clinical reality. To assess the influence of SNR on the estimation of S0, f, D* and D IVIM parameters a series of measurements and simulations under MATLAB environment were performed for three estimation techniques, utilizing known “ground truth” and noised data. Scanner specific SNR was estimated based on healthy subject IVIM MRI study in 3T scanner. Measurements were conducted for 25,6x25,6x14,4 cm FOV with 256x256 in plane resolution and 72 slices resulting in 1x1x2 mm voxel size. Simulations were performed for 36 SNR levels around measured SNR value. Single voxel grid search estimation method mean relative error for S0 f D* D estimates at measured SNR level were: 5.00%,81.91%, 76.31%, 18.34% respectively. Analysis has shown that high resolution IVIM is possible, although there is significant variation in estimation accuracy and precision depending on SNR level and chosen estimation method.

Technology-enabled maintenance via sensors and the internet of things (IoT) technologies is seen as the “new maintenance strategy”. The aim is to use these technologies to focus on unnecessary breakdowns and increase levels of asset availability where the existing of a dedicated team to maintain or data on asset condition is limited, useless or full of noise. This paper presents the integration of various sensing nodes within an automated laser cutting machine for monitoring of critical components’ conditions. The integrated system provides a centralised platform for data collection from various sensors and provides early failure detection mechanism based on specified set points with alert mechanisms utilising a custom dashboard for data visualisation. The research highlights the required stages for the identification of the appropriate components and sensory devices for the development of efficient monitoring environment.

Hydraulic fracturing in shale gas reservoirs has usually resulted in complex fracture network. The results of micro-seismic monitoring showed that the nature and degree of fracture complexity must be clearly understood to optimize stimulation design and completion strategy. This is often called stimulated reservoir volume (SRV). In the oil & gas industry, stimulated reservoir volume has made the shale gas exploitation and development so successful, so it is a main technique in shale gas development. The successful exploitation and development of shale gas reservoir has mainly relied on some combined technologies such as horizontal drilling, multi-stage completions, innovative fracturing, and fracture mapping to engineer economic completions. Hydraulic fracturing with large volumes of proppant and fracturing fluids will not only create high conductivity primary fractures but also stimulate adjacent natural fractures. Fracture network forming around every hydraulic fracture yields a stimulated reservoir volume. A model of horizontal wells which was based on a shale gas reservoir after volume fracturing in China was established to analyze the effect of related parameters on the production of multi-fractured horizontal wells in this paper. The adsorbed gas in the shale gas reservoir is simulated by dissolved gas in the immobile oil. The key to simulate SRV is to accurately represent the hydraulic fractures and the induced complex natural fracture system. However, current numerical simulation methods, such as dual porosity modeling, discrete modeling, have the following limitations: 1) time-consuming to set up hydraulic and natural fracture system; 2) large computation time required. In this paper, the shape of the stimulated formation is described by an expanding ellipsoid. Simplified stimulated zones with higher permeability were used to model the hydraulic fracture and the induced complex natural fracture system. In other words, each primary fracture has an enhanced zone, namely SRV zone. This method saves much developing fine-grid time and computing time. Compared with the simulation results of fine-grid reference model, it has shown that this simplified model greatly decreases simulation time and provides accurate results. In order to analyze the impacts of related parameters on production, a series of simulation scenarios and corresponding production performance were designed. Optimal design and analyses of fracturing parameters and the formation parameters have been calculated in this model. Simulation results showed that the number of primary fractures, half length, SRV half-width and drop-down have great effects on the post-fracturing production. Formation anisotropies also control the production performance while the conductivity of the primary fractures and SRV permeability do not have much impact on production performance. The complexity of stimulated reservoir volume has strong effect on gas well productivity. Fracture number mainly affects the early time production performance. The increase of SRV width cannot enlarge the drainage area of the multi-fractured horizontal wells, but it can improve the recovery in its own drainage region. Permeability anisotropies have much effect on production rate, especially the late time production rate. The results prove that horizontal well with volume fracturing plays an irreplaceable role in the development of ultra-low permeability shale gas reservoir.

This paper presents an upcoming nonvolatile memories (NVM) overview. Non-volatile memory devices are electrically programmable and erasable to store charge in a location within the device and to retain that charge when voltage supply from the device is disconnected. The non-volatile memory is typically a semiconductor memory comprising thousands of individual transistors configured on a substrate to form a matrix of rows and columns of memory cells. Non-volatile memories are used in digital computing devices for the storage of data. In this paper we have given introduction including a brief survey on upcoming NVM's such as FeRAM, MRAM, CBRAM, PRAM, SONOS, RRAM, Racetrack memory and NRAM. In future Non-volatile memory may eliminate the need for comparatively slow forms of secondary storage systems, which include hard disks.

The emergence of 6G communication technologies brings both opportunities and challenges for the Internet of Things (IoT) in Smart Cities. In this paper, we introduce an advanced network slicing framework designed to meet the complex demands of 6G Smart Cities IoT deployments. The framework development follows a detailed methodology that encompasses requirement analysis, metric formulation, constraint specification, objective setting, mathematical modeling, configuration optimization, performance evaluation, parameter tuning, and validation of the final design. Our evaluations demonstrate the framework's high efficiency, evidenced by low Round-Trip Time (RTT), minimal packet loss, increased availability, and enhanced throughput. Notably, the framework scales effectively, managing multiple connections simultaneously without compromising resource efficiency. Enhanced security is achieved through robust features such as 256-bit encryption and a high rate of authentication success. The discussion elaborates on these findings, underscoring the framework's impressive performance, scalability, and security capabilities.

This study aimed to evaluate the effectiveness of decellularized nerves for promoting nerve regeneration by transplanting them from the sciatic nerve of a rabbit to that of a rat. The Sciatic nerves were harvested from New Zealand White Rabbits (n=4). A 1-cm segment of each nerve was decellularized by Sondell’s protocol and preserved at –4℃ in Roswell Park Memorial Institute (RPMI) 1640 medium for one week. Subsequently, a defect of a similar size was created in the sciatic nerves of rats. The decellularized nerves treated with detergent were then transplanted into these defects in the rats. The study included the three following groups: Group 1 (n=4) was the decellularized xenogeneic nerve group; Group 2 (n=7) was the negative control group in which the defect remained un-repaired; and Group 3 (n=5) was composed of the positive control group where the nerve was immediately transplanted from one rat to another. The sciatic functional index (SFI) was considered to evaluate the motor function by comparing it with the normal side at 1, 2, 3, and 4 weeks, and 1, 2, and 3 months postoperatively. Furthermore, electrophysiological and immunohistochemical assessments were conducted at 4 months. Immunohistochemically, at 16 weeks postoperative, the experimental group did not show statistically significant differences compared to controls in terms of schwann cell comparison, yet exhibited favorable outcomes when compared to the positive control group. Additionally, analysis of motor function test confirmed recovery of latency and nerve conduction velocity to levels similar to those of the normal side. While the nerve allograft demonstrated superior nerve regeneration in motor function test at 3 months, markedly low values were observed in all groups. When reconstructing peripheral nerves using detergent-based decellularized xenogenic nerves, analysis of immuno-histochemistry and motor nerve conduction study reveals a promising degree of nerve regeneration. However, when compared with fresh allograft group at 3 months after operation, the sciatic functional index was lower. Significantly lower values observed across all groups compared to previous assessments indicate the need for further research into potentially influential alternative factors.Histologically, at 16 weeks postoperative, the experimentally treated group did not show statistically significant differences compared to the controls in terms of a Schwann cell comparison, but exhibited favorable outcomes when compared to the positive control group. Additionally, an analysis of the motor function testing confirmed recovery of the latency and nerve conduction velocity to levels similar to those of the normal side. While the nerve allograft demonstrated superior nerve regeneration in the motor nerve examination at 3 months, notably low values were observed in all the groups. When reconstructing the peripheral nerves using detergent-based decellularized xenogeneic nerves, an analysis of immunohistochemistry and motor nerve conduction study reveals a promising degree of nerve regeneration. However, when compared with the fresh allograft group at 3 months post-operation, the sciatic functional index was lower. The significantly lower values observed across all groups compared to those of the previous assessments indicate the need for further research of potentially influential alternative factors.

The value of artificial intelligence and machine learning applications for use in heritage research is increasingly appreciated [1]. In specific areas, notably remote sensing, data sets have increased in extent and resolution to the point that manual interpretation is problematic and availability of skilled interpreters to undertake such work is limited. Interpretation of geophysical data sets associated with prehistoric submerged landscapes is particularly challenging. Following the last glacial, sea levels rose 120 metres globally, and vast, habitable landscapes were lost to the sea. These landscapes were inaccessible until extensive remote sensing data sets were provided by the offshore energy sector. In this paper, we provide results of a research programme centred on AI applications using data from the southern North Sea. Here, an area of c.188,000 km2 of habitable terrestrial land was inundated between c. 20,000 BP and 7,0000 BP, along with the cultural heritage it contained [2]. As part of this project, machine learning tools were applied to detect and interpret features of archaeological significance from shallow seismic data. The output provides a proof-of-concept model demonstrating verifiable results and the potential for further, more complex leveraging of AI interpretation for the study of submarine palaeolandscapes. Image: Adobe Firefly Generative AI using keywords - marsh landscape deep underwater, auroch skull half-buried in peat

The Estimation of systemic risk in India is still in its infancy stage. There are several methods which are available but none of the methods are fully compatible to forecast the systemic risk since under different circumstances the factors responsible for the risk differs. In this paper the systemic risk estimation in India being carried out based on spread in daily stock market price(Difference between the bid and ask price of a share) of the top 100 firms in India according to market capitalization for the period of July2007 to March 2016. The results were compared with the Financial Stability Report published by Reserve Bank Of India for the period of March 2010 to June 2016.The results clearly indicates that there exits relationship between market illiquidity represented by spread and risks associated with the Financial System. In most of the cases the Z score (deviation from the mean/Standard Deviation) of the spread has become negative which provides the spread which is farther from the mean, also a good indicator of volatility in market and risk to financial system. It is also seen that the Systemic Risk Survey conducted by Reserve Bank of India which started during October 2011 has supported the results.

Background: Effective identification and management in the early stages of sepsis are critical for achieving positive outcomes. In this context, neutrophil activation status (NEUT-RI) emerges as a promising and easily interpretable parameter. This study aimed to assess the predictive value of NEUT-RI in diagnosing sepsis and to evaluate its prognostic significance in distinguishing 28-day mortality outcomes. Materials: The study is a secondary retrospective observational analysis. Clinical data upon ICU admission were collected. A control group of critically ill patients without sepsis criteria was included. Patients were divided into subgroups based on renal function for biomarker evaluation, with 28-day outcomes reported for septic and non-septic patients. Results: 200 patients were included in the study. A significant difference between the "septic" and "non-septic" groups was detected in the NEUT-RI plasma concentration (53.80 [49.65–59.05] vs. 48.00 [46.00–49.90], p< 0.001, respectively). NEUT-RI and PCT distinguished between not complicated sepsis and septic shock (PCT 1.71 [0.42 - 12.09] vs 32.59 [8.83 - 100.00], < 0.001 and NEUT-RI 51.50 [47.80 - 56.30] vs 56.20 [52.30 - 61.92], p=0.005). NEUT-RI, PCT and CRP values were significantly different in patients with “renal failure”. NEUT-RI and PCT at admission in ICU in septic group were higher in patients who died (58.80 [53.85-73.10] vs 53.05 [48.90-57.22], p=0.005 and 39.56 [17.39-83.72] vs 3.22 [0.59-32.32], p=0.002, respectively). Both NEUT-RI and PCT showed high negative predictive value and low positive predictive value. Conclusions: The inflammatory biomarkers assessed in this study offer valuable support in the early diagnosis of sepsis. NEUT-RI elevation appears particularly promising for early sepsis detection and severity discrimination upon admission

Cold chains are essential in providing us humans with food and medicine across the globe. However, cold transportation, like all transportation, has massive negative impacts on the environment and human well-being, which especially culminate in cities. Thus, it is important to research the ways to reduce these impacts and reinvent urban logistics infrastructure. In this article, we articulate the massive problems the road-based transportation sector imposes on the environment and on the health of humans, especially in cities. We present algorithms that show promise in optimizing the whole road transportation sector as well as some innovations and alternative ways to model cities and the whole urban transportation infrastructure. We approach the issues discussed specifically from the point of view of refrigerated transportation, but many of the solutions can be applied to all the transportation sector in general.

Background: The study investigates patients' use of eHealth services, awareness of the availability of these services, and their intention to use them in primary care. It also examines patient characteristics and factors that influence their use. Methods: A cross-sectional design using questionnaire were conducted. Based on the unified theory of acceptance and use of technology (UTAUT), participants rated the two most common services. Descriptive analyses and linear correlation analyses were performed. Simple linear regression was conducted identify factors influencing participants' intention to use eHealth services. Results: 1203 participants were surveyed and their’ ages averaged 43.7 years. Participants usage rates varied, with the lowest at 2.4% for measuring vital signs and the highest at 47.4% for booking appointments. Intentions to use services ranged from 22.5% for video consultations to 46.6% for prescription refill requests. Approximately 20% of respondents were unaware of each service's availability. Positive associations were found between all constructs and the intention to use online services, with younger age being the most significant factor. Conclusions: Use of and intentions to use eHealth services varied greatly. Participants were often unaware of the availability of services. Promoting the availability and benefits of eHealth services could enhance patient engagement in primary care settings.

How infants engage with objects changes dramatically over the first year of life. While some infants exhibit a consistent hand preference for acquiring objects during this period, others have no identifiable preference. The goal of this study was to test whether lateralization confers an advantage in the development of early object skills. We examined whether lateralized infants show different rates of growth in how they interact with multiple objects as compared to infants without a hand preference. In a longitudinal design consisting of seven monthly visits from 6-12 months, 303 infants were assessed for hand preference and object management skill (i.e., holding up to three objects). Group-Based Trajectory Modeling (GBTM) identified three hand preference trajectory groups: Left, Right, and No Preference (NP). A Hierarchical Generalized Linear Model (HGLM) found that Left and Right infants differed in their linear and quadratic slopes and transitioned from holding one to two objects more quickly than NP infants. While all infants showed similar trends in object management skill across time, lateralized infants had an advantage. Further work is needed to determine if this early object skill advantage cascades to later more complex object handling.

Conventional tutoring approaches are confronted with limitations such as restricted availability, inconsistent pedagogical quality, and scalability con-straints. Furthermore, the exclusive use of Large Language Models (LLMs) like ChatGPT in education has its shortcomings, including the potential for incorrect responses and the lack of customized guidance aligned with specific course con-tent. This research proposes an innovative intelligent chatbot tutoring system, in-tegrating the Retrieval Augmented Generation (RAG) approach with a custom LLM. The developed system aims to overcome the limitations of traditional tu-toring and general-purpose LLMs by providing accurate, contextually relevant, and personalized assistance, thereby enhancing student understanding and en-gagement. The system, powered by an intelligent agent, retrieves relevant information from curated academic sources, incorporates interactive features for user feedback, and utilizes machine learning algorithms for ongoing performance enhancement, ensuring a robust and effective tutoring experience. The anticipated outcome is an enriched educational experience for university students, advance-ment in personalized learning, and improved student engagement, retention, and academic performance. Through continuous research and refinement, it is expected that the intelligent chatbot tutor will assume an important role in enhancing and supporting the educational journey of students.

Medical datasets may be imbalanced and contain errors due to subjective test results and clinical variability. The poor quality of original data affects classification accuracy and reliability. Hence, detecting abnormal samples in the dataset can help clinicians make better decisions. In this study, we propose an unsupervised error detection method using patterns discovered by the Pattern Discovery and Disentanglement (PDD) model, developed in our earlier work. Applied to the large data, the eICU Collaborative Research Database for sepsis risk assessment, the proposed algorithm can effectively discover statistically significant association patterns, generate an interpretable knowledge base for interpretability, cluster samples in an unsupervised learning manner, and detect abnormal samples from the dataset. As shown in the experimental result, our method outperformed K-Means by 38% on the full dataset and 47% on the reduced dataset for unsupervised clustering. Multiple supervised classifiers improve accuracy by an average of 4% after removing abnormal samples by the proposed error detection approach. Therefore, the proposed algorithm provides a robust and practical solution for unsupervised clustering and error detection in healthcare data.

Accurately assessing forest structure and maintaining up-to-date information about forest structure is crucial for various forest planning efforts, including the development of reliable forest plans and assessments of the sustainable management of natural resources. Field measurements traditionally applied to acquire forest inventory information (e.g., basal area, tree volume, and aboveground biomass) are labor-intensive and time-consuming. To address this limitation, remote sensing tech-nology has been widely applied in modeling efforts to help estimate forest inventory information. Among various remotely sensed data, LiDAR can potentially help describe forest structure. This study was conducted to estimate and map forest inventory information across the Talladega Na-tional Forest by employing ALS-derived data and aerial photography. The quality of predictive models was evaluated to determine whether additional remotely sensed data can help improve forest structure estimates. Additionally, the quality of general predictive models was compared to that of species-group models. This study confirms that quality level 2 LiDAR data was sufficient for developing adequate predictive models (R2adj. ranging between 0.71 and 0.82) when compared to the predictive models based on LiDAR and aerial imagery. Additionally, this study suggests that species-group predictive models were of higher quality than general predictive models. Lastly, landscape-level maps were created from the predictive models, and these may be helpful to plan-ners, forest managers, and landowners in their management efforts.

This study delves into the performance evaluation of an avant-garde technology in mobile communications known as Large Intelligent Surface (LIS), poised to be an integral component of the sixth-generation (6G) networks. This groundbreaking technology has the potential to markedly augment the Signal-to-Interference plus Noise Ratio (SINR) by orchestrating signals originating from the base station through a network of digitally controlled reflectors meticulously engineered to modulate the phase of incoming electromagnetic waves. A LIS can establish a Line of Sight (LoS) within channels that inherently do not have a LoS. In addition, it can also be optimized to increase transmission security by ensuring that the signal reaches only the target users of the message with better quality. In this study, analytical expressions were derived to calculate the distribution parameters, the bit error probability, and the secrecy outage probability. These calculations consider the presence of an eavesdropper link, highlighting the potential of the LIS to increase the confidentiality of digital communication systems. The accuracy of the proposed formulas was demonstrated by showing how the performance of the LIS varies with different design parameters and environmental fading.

Digital technologies have been present in manufacturing and industry for a long time, and today they represent the basis for the application of the Industry 4.0 model in practice. The transformation marked by the Industry 4.0 model is based on the application of a large number of models of communication technologies, solutions for connecting entities (machines, parts, ...) in the factory within Internet of Things (IoT), as well as the possibility of big data analysis (BDA) throught the artificial intelligence tools (AI/ML). These technologies provide great opportunities to create more flexible and profitable manufacturing processes based on data, and using the Internet. As more and more factories and their equipment are IoT enabled, the volume of data will only grow, hence the application of digital technologies is inevitable. To truly pave the way forward to Industry 4.0 and beyond, manufacturing must evolve into cognitive manufacturing, which is the base model of smart manufacturing. This paper provides an overview of the development of the concept of digital manufacturing, from different aspects, with examples of application in real manufacturing. The goal is to create a good practice of digital manufacturing with the elements of Industry 4.0, as a basis for the development and application of the smart manufacturing (SM) model.

Across decades of contemporary discussion on sustainable development, a core debate has concerned the relationship between the economic, social, and environmental dimensions of sustainability. In recent years, a debate on economic growth versus degrowth has been going on discussing whether economic growth can be sustainable in environmental terms and whether economic degrowth can be sustainable in social terms. This conceptual and theoretical article has used the Sustainability Window, or ‘SuWi’ method, to theoretically determine the sustainable window of economies, as upper and lower bounds of future change in GDP, that could be deemed in line with environmental and social sustainability. All theoretically possible scenario paths of development are considered by combining the economic, environmental, and social dimensions, with the environmental and social productivities of GDP. In moving to sustainability analysis through SuWi it is noted that only four of the logically possible scenario paths could be considered theoretically 'sustainable’, and only if adhering to strict conditions. Two of the hypothesised scenarios involve economic growth and two of economic degrowth. The approach, including calculation formulas, has many potential uses in providing analysis and support to 21st century sustainability policymaking, including the United Nations Sustainable Development Goals.

The article suggests a Value Proposition (VP) framework that enables the analysis of the beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP development activities. To develop such a framework, we examined existing management research publications to identify and extract assertions that could be used as a source of actionable insights for new growth-oriented companies. The extracted assertions were assembled into a corpus of texts that were subjected to topic modelling analysis – a machine learning approach to natural language processing that is used to identify latent themes in large corpora of text documents. The topic modeling resulted in the identification of seven topics. Each topic is defined by a set of most frequent words co-occurring in a distinctive subset of texts that could be interpreted in terms of activities constituting the core elements of the VP framework. We then examined each activity in terms of its potential to be enhanced by employing AI resources and capabilities. The interpretation of the topic modeling results led to the identification of seven topics: 1) Value created; 2) Stakeholder value propositions; 3) Foreign market entry; 4) Customer base; 5) Continuous improvement; 6) cross-border operations; and 7) Company image. The uniqueness of the adopted topic modeling approach consists in the quality of the assertions and the interpretation of the seven topics as an activity framework, i.e. in its capacity to generate actionable insights for practitioners. The additional analysis suggests that there is a potential for AI to enhance emerging the four core elements of the VP framework: Value created, Stakeholder value propositions, Foreign market entry, and Customer base. More importantly, we found that the greatest number of assertions related to activities that could be enhanced by AI are part of the Customer base topic, i.e., the topic that is most directly related to the growth potential of the companies. In addition, the VP framework suggests that a company’s customer base growth is continuously enhanced through a positive loop enabled by activities focused on the Continuous improvement of the activities and the amount of Value created, the alignment of Stakeholder value propositions, and companies’ Foreign market entry. Thus, Foreign market entry appears as a factor that helps in understanding the beneficial impact of AI on the VP development of new growth-oriented companies.

Lead-acid batteries utilised in electrical substations release hydrogen and oxygen when these are charged. These gases could be dangerous and cause a risk of fire if they are not properly ventilated. Therefore, this research seeks to design and implement a network control panel for heating, ventilation, and air-conditioning systems (HVACS). This is done using a specific range of controllers, which have more than thirty loops of proportional, integral and derivative (PID) control to achieve a cost-effective design. It performs the required function of extracting hydrogen, oxygen, and maintaining the desired temperature of the battery storage room within the recommended limits (i.e. 25°C ±1°C tolerance) without compromising quality as set out in the user requirement specification in Appendix-A. The system control panel allows the user to access control parameters such as changing temperature set-points, fan-speed, sensor database amongst others. The hardware is configured to detect extreme hydrogen and oxygen gas content in the battery room and ensure that the HVACS extract the gas content to the outside environment. The results of the system show that the network control panel effectively operates as per the recommended system requirements. Therefore, the effective operation of the HVACS ensures sufficient gas ventilation, thus mitigating the risk of fire in a typical battery storage room. Furthermore, the effective operation of HVACS enhances battery lifespan because of regulated operating temperature, which is conducive to minimise the effect of sulfation in lead acid Batteries (LABs).

In this paper we report the possibility of using the X-ray powder diffraction (XRPD) technique to detect the gamma-hydroxybutyric acid (GHB) as its sodium salt in different beverages but because it is not possible to freely buy them, beta-hydroxybutyric acid (BHB) and its sodium salt (NaBHB) were used as a model to fine tune an X-ray diffraction method for the qualitative analysis of the sodium salt of GHB. The method requires a small quantity of beverage, and an easy sample preparation, that consists only in the NaOH addition to the drink and a subsequent drying step. The dry residue obtained can be easily analyzed with XRPD using a single crystal X-ray diffractometer to exploit its high sensitivity that allows very fast patterns collection. Several beverages with different NaBHB:NaOH molar ratios were tested and the results show that NaBHB is detected in all drinks analyzed when NaBHB:NaOH molar ratio is 1:50 using characteristic peak at very low 2θ values that permits to detect its presence also in complex beverage matrices. Moreover, depending on the NaOH amount added, shifting and/or splitting of the characteristic peak of the NaBHB salt was observed, and the origin of this behavior was investigated.

The presence of cracks reduces egg quality and safety, which are easy to cause food safety hazards to consumers. Machine vision-based methods for crack egg detection have made significant success on in-domain egg data. However, the performance of deep learning models usually decreases under practical industrial scenarios, such as the different egg varieties, origins, and environmental changes. Existing researches which rely on improving network structures or increasing training data volumes, can not effectively solve the problem of model performance decline on unknown egg testing data in practical egg production. To address these challenges, a novel and robust detection method is proposed to extract max domain-invariant features to enhance the model performance on unknown testing egg data. Firstly, multi-domain egg data is built on different egg origins and acquisition devices. Then, a multi-domain trained strategy is established by using Maximum Mean Discrepancy with Normalized Squared Feature Estimation (NSFE-MMD) to obtain the optimal matching egg training domain. With the NSFE-MMD method, the original deep learning model can be applied without network structure improvements, which reduces the extremely complex tuning process and hyperparameter adjustments. Finally, robust crack egg detection experiments are carried out on several unknown testing egg domains. The YOLOV5 model trained by proposed multi-domain trained method with NSFE-MMD has a detection mAP of 86.6\% on the unknown test domain 4, and the YOLOV8 model has a detection mAP of 88.8\% on domain 4, which is an increase of 8\% and 4.4\% compared to the best performance of models trained on a single domain, and an increase of 4.7\% and 3.7\% compared to models trained on all domains. In addition, the YOLOV5 model trained by the proposed multi-domain trained method has a detection mAP of 87.9\% on egg data of the unknown testing domain 5. The experimental results demonstrate the robustness and effectiveness of the proposed multi-domain trained method, which can be more suitable for the large quantity and variety of egg detection production.

Fluorinated cytokinins have emerged as promising alternatives to traditional cytokinins in plant tissue culture, offering enhanced stability and bioactivity. However, their metabolic fate and impact on endogenous cytokinin profiles remain largely unexplored. This study investigated the cytokinin metabolism of Phalaenopsis orchids under varying exogenous cytokinin treatments (BA, FmT, FmTR) and culture conditions (open vs. closed containers). The results reveal distinct metabolic profiles associated with each treatment, highlighting the complex interplay between exogenous and endogenous cytokinin levels. Additionally, we proposed an alternative inactivation pathway involving the conversion of FmTR and BA to pT and pTR. These findings provide valuable insights into the intricate relationship between cytokinin metabolism and plant growth under in vitro conditions, with implications for optimizing micropropagation protocols

Lymphocyte collection by apheresis for CAR-T production usually does not include mobilized blood using Granulocyte Colony Stimulating Factor (G-CSF) due to the widespread knowledge that it causes a decrease in the number and functionality of lymphocytes. However, it is used for stem cell transplant, which is a common treatment for hematological malignancies. The growing demand for CAR therapies (CAR-T and NK-CAR), both in research and clinic, makes necessary to evaluate whether mobilized PBSC products may be potential candidates for use in such therapies. This review collects recent work that experimentally verifies the role and functionality of T and NK lymphocytes, and the generation of CAR-T, from apheresis after G-CSF mobilization. As discussed, T cells do not vary significantly in their phenotype, the ratio of CD4+ and CD8+ remains constant and the different sub-populations remain stable. In addition, its expansion and proliferation rate are invariant regardless mobilization with G-CSF, as well as the secretion of proinflammatory cytokines and the cytotoxic ability. Therefore, cells mobilized before apheresis are postulated as a new alternative source of T cells for adoptive therapies that will serve to alleviate high demand, increase availability, and take advantage of the substantial number of existing cryopreserved products.

The pointing accuracy of large radio telescopes is affected by antenna structure errors, installation errors, servo errors, gravity, and varying environmental factors. It is hard to estimate the pointing error of radio telescopes under solar radiation via the traditional multi-parameter static pointing models, due to the time-varying characteristics of the dynamic pointing error. There is still no effective way to estimate the dynamic pointing error of large radio telescopes under solar radiation up to now. To estimate the dynamic pointing error of large radio telescopes under solar radiation, a new way based on digital twin(DT) is tried in this paper. The operational framework of the DT system of large radio telescopes is introduced first. Then, the Wuqing 70-m radio telescope(WRT70) is taken as the research object, the digital models of which are created. Finally, the reliability of the WRT70's DT system is verified and the dynamic pointing error of WRT70(staying at the position of the elevation angle of 90° and the azimuth angle of 180°) on two sunny days in different seasons is estimated by the created DT system. The research results show that the pointing error of large radio telescopes under solar radiation fluctuates greatly and the seasonal differences are very striking. The maximum elevation pointing error of WRT70 under solar radiation around the winter solstice is over 40 arcsec, which should never be ignored. The way is feasible to estimate the dynamic pointing error of large radio telescopes staying at a specific position under solar radiation via constructing a DT system in this paper, but it is not yet able to estimate effectively the dynamic pointing error of large radio telescopes being in action. It is expected to establish a dynamic pointing error model and calibrate the dynamic pointing errors of large radio telescopes being in action by DT technology in the future.

Keywords: Energy harvesting, Hybrid, Medical Wearable Sensor Nodes, Health Monitoring, Power Management

This multi-center retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from 6 manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The di-agnostic performance of the device was compared with the ground truth established by the majority agreement of three US board-certified radiologists. Furthermore, the DL-algorithm's time-to-notification was evaluated to demonstrate clinical effectiveness. The study included 1,303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8% – 97.5%] and a specificity of 97.3% [95% CI: 96.2% - 98.1%]. The application classified positive cases by AD types with an accuracy of 99.5% [95% CI: 98.9% – 99.8%] for type A and 97.5 [95% CI: 96.4% – 98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 seconds. This deep learning-based application demonstrated strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.

In the last decades, numerous studies indicated the substantial role semiconductors could play in photocatalytic processes for environmental applications. Materials that contain a semi-conductor as a photo-catalyst have a semi-permanent capacity for removing harmful gases from the ambient air. In the present work, the focus is on TiO2. Heterogeneous photo-catalysis using TiO2 leads to the degradation of NO/NO2, benzene, toluene, and other priority air pollutants once in contact with the semiconductor surface. Preliminary evidence indicates that TiO2 containing construction materials and paints efficiently destroy the ozone precursors NO and NO2 up to 80% and 30%, respectively. Therefore, the development of innovative coatings containing TiO2 as a photo-catalyst was in the foreground of research activities with the aim to be used as building and construction materials mainly outdoors, e.g. on building facades in high traffic roads for the degradation of priority air pollutants (NOx and volatile organic compounds) in the polluted urban atmosphere. Though the advantages connected with the application of TiO2 due to its band gap of 3.2 eV are limited. TiO2 is effective only in the UV-region (ca. 5%) of the solar spectrum with wavelengths λ

Background: C-reactive protein (CRP) is a predictive biomarker of colorectal surgery (CRS) complications but has not been evaluated by comparing patients undergoing CRS with intracorporeal anastomosis (IA) or extracorporeal anastomosis (EA). Therefore, we aimed to describe postoperative CRP values between two groups of patients who underwent laparoscopic right hemicolectomy for right-side colon cancer with IA or EA. Methods: We retrospectively reviewed the data of patients affected by right-side colon cancers who underwent laparoscopic right-hemicolectomy with ileo-colic IA or EA between January 2016 and December 2022. Results: A total of 340 patients underwent laparoscopic right hemicolectomy. EA was performed in 238 (70%) and IA in 102 (30%) cases. Patients with IA showed significantly higher values of CRP in the second, third and fourth postoperative days (15.2 mg/dL vs 9.5 mg/dL p=

In developing his cosmological model of 1917, de Sitter theoretically predicted the phenomenon of cosmological redshift (the de Sitter effect), which he did long before the discovery of this phenomenon in observations. The de Sitter effect is gravitational by its nature, as it is due to differences between the coordinate systems of the observer and the distant source. However, the relationship between the redshift and distance derived from the de Sitter metric is at odds with observations, since this relationship is nonlinear (quadratic) for small redshifts, while the observed relationship between the same quantities is strictly linear. This paper discusses the possibility that cosmological redshift is gravitational by its nature, as in de Sitter’s 1917 model. At the same time, here, as in de Sitter’s model, an elliptical space is used, the main characteristic of which is the identification of its antipodal points. But, unlike de Sitter’s model, here, in order to ensure a strict linear dependence of the redshift on distance, the origin of the reference system is transferred to the observer’s antipodal point. The Schwarzschild-de Sitter metric used in this model allows you to estimate the curvature of space from observational data. To do this, a theoretical Hubble diagram is built within the framework of the model with the Schwarzschild-de Sitter metric, which is compared with observations from the Pantheon+ catalogue of type Ia supernovae and the Amati catalogue of gamma-ray bursts in the redshift range of 0&lt;z&lt;8. As a results of the comparison, the lower estimate of the radius of curvature of space turned out to be quite large: 2.3·1015 Mpc. This means that the observational data indicate a negligible curvature of space.

Although many physical activity (PA) interventions for older adults have proven ef-fective in controlled research settings, optimal implementation in real life remains challenging. This study identifies determinants perceived by stakeholders when im-plementing community-based PA interventions for older adults. We interviewed 31 stakeholders guided by the Consolidated Framework for Implementation Research (CFIR). Results showed that stakeholders are very specific about the role they can play in implementation making collaboration between stakeholders crucial. Barriers and motivators were identified in the CFIR intervention characteristics domain (relative advantage, complexity and costs, evidence quality and strength, and adaptability and trialability), in the outer setting domain (cosmopolitism, patient needs, and external policy and incentives), in the inner setting domain (implementation climate, relative priority, compatibility and organizational incentives and rewards) and in the individ-ual characteristics domain (knowledge and beliefs, and other personal attributes). An overarching theme was the stakeholders' emphasis on aiming for broad health goals in interventions, as they perceive PA as a means to reach these goals rather than an end in itself. Another overarching theme requiring attention in future implementation ef-forts is the need to tailor implementation efforts to the specific needs of older adults as the end users of the intervention.

The following article analyses the relationship between the mental health index and the variables of the Environment, Social and Governance-ESG model in the Italian regions between 2004 and 2023. The econometric analysis is aimed at investigating in detail the relationships between the mental health index and the individual components of the ESG model. The results are critically discussed

This study is dedicated to understanding and simulating the mechanical properties of concrete, with a specific focus on the mesoscopic scale. Investigating concrete at this level involves examining its composition as a heterogeneous amalgamation comprising mortar, aggregates, and the interfacial transition zone (ITZ). Such an approach provides a detailed examination of the material's behavior and attributes. Numerical models are utilized, leveraging the finite element method (FEM), to examine the behavior of concrete. The study employs MATLAB programming to develop three-dimensional models, subsequently subjected to FEM analysis. Various mesoscopic Representative Volume Elements (RVEs) are formulated, encompassing varied shapes and dimensions. The MATLAB framework generates input files for numerical FEM simulations, replicating compression strength tests. As complexity increases with the inclusion of the ITZ, prismatic RVEs are developed. The proposed mesoscopic model establishes a foundational framework for a numerical simulation methodology tailored to laboratory compression tests. It provides detailed insights into concrete behavior, elucidating deformation, and fracture mechanisms. Although not a complete substitute for experimental methods, these models offer a cost-effective and expeditious alternative, pinpointing vulnerable areas and exploring the implications of additional materials on concrete behavior. Experimental data and virtual tests pave the way for mitigating carbon footprint and improving concrete sustainability.

TAFRO syndrome (TS) is a rare variant of idiopathic multicentric Castleman's disease (iMCD) characterized by a confluence of symptoms: thrombocytopenia (T), anasarca (A), fever (F), reticulin myelofibrosis (R), and organomegaly (O). First described in Japan in 2010, the pathogenesis of the disease remains unclear. Due to its heterogeneous presentation and potential life-threatening delays in diagnosis, precise diagnosis is essential. According to the literature, no specific imaging modality has been recommended for the work-up of patients with suspected TS. Here, we report a case of TS and its management using 18F-FDG-PET/CT imaging as an attractive complementary diagnostic tool.

Continuous discovery and update of applications or their boundaries running in cloud environments in an automatic way is a highly required function of modern data center ops solutions. Prior attempts to address this problem within various products or projects were/are applying rule-driven approaches or machine learning techniques on specific types of data − network traffic as well as property/configuration data of infrastructure objects, which all have their drawbacks in effectively identifying roles of those resources. The current proposal (ADLog) leverages log data of sources, which contain incomparably richer contextual information, and native constructs of VMware Aria Operations for Logs in terms of Event Types and their distributions to group those entities, potentially automatically enrich with indicative tags, and recommend for further management tasks and policies. Our methods discriminate not only diverse kinds of applications, but also their specific deployments thus providing hierarchical representation of the applications in time and topology. For several applications under Aria Ops management in our experimental test bed, we discover those in terms of similarity behavior of their components with a high accuracy. The validation of the proposal paves the path for an AI-driven solution in the cloud management scenarios.

Keywords: Echocardiographic alterations; left ventricle; dog; Trypanosoma cruzi; flow patterns; dilated cardiomyopathy; Chagas disease

Algeria, the main fire hotspot on the southern rim of the Mediterranean Basin, lacks complete fire dataset with official fire perimeters, and the existing one contains inconsistencies. Preprocessed global and regional burned area (BA) products provide valuable insights into fire patterns, characteristics and dynamics over time and space, and into their impact on climate change. Nevertheless, they exhibit certain limitations linked with their inherent spatio-temporal resolutions. To address the need for reliable BA information in Algeria, we systematically reconstructed, validated and analyzed a 40-year (1984–2023) BA product (NEALGEBA; North Eastern ALGeria Burned Area) at 30-m spatial resolution in the typical Mediterranean Ecosystems of this region, following international standards. We used Landsat data and the BA Mapping Tools (BAMTs) in the Google Earth Engine (GEE) to map BAs. The spatial validation of NEALGEBA, performed for 2017 and 2021 using independent 10-m spatial resolution Sentinel-2 reference data, showed overall accuracies > 98.10 %; commission and omission errors < 8.20 %; Dice coefficients > 91.90 %, and relative biases < 3.44 %. The temporal validation, however, using MODIS and VIIRS active fire hotspots, emphasized the limitation of Landsat-based BA products in temporal fire reporting accuracy terms. The intercomparison with five readily available BA products for 2017, by using the same validation process, demonstrated the overall outperformance of NEALGEBA. Furthermore, our BA product exhibited the highest correspondence with the ground-based BA estimates. NEALGEBA currently represents the most extensive and reliable time series of BA history at fine spatial resolution for NE Algeria, offering a significant contribution for further national and international fire hazard and impact assessments and acts as a reference dataset for contextualizing future weather extremes, such as the 2023 exceptional heat wave, which we show not to have led to the most extreme fire year over the last four decades.

The need for industrial and commercial machinery to maintain high torque while accurately following a variable angular speed is increasing. To meet this demand, induction motors (IMs) are commonly used with variable speed drives (VSDs) that employ a field-oriented control (FOC) scheme. Over the last thirty years, IMs have been replacing independent connection direct current motors due to their cost-effectiveness, reduced maintenance needs, and increased efficiency. However, IMs and VSDs exhibit nonlinear behavior, uncertainties, and disturbances. This paper proposes a robust combined adaptive passivity-based control (CAPBC) for this class of nonlinear systems that applies to angular rotor speed and stator current regulation inside an FOC scheme for IMs´ VSDs. It uses general Lyapunov-based design energy functions and adaptive laws with σ-modification to assure robustness after combining control and monitoring variables. Lyapunov’s second method and Barbalat Lemma prove that the control and identification error tends to be zero over time. Moreover, comparative experimental results with standard Proportional Integer controller (PIC) and direct APBC, show the proposed CAPBC effectiveness and robustness under normal and changing conditions

This study aims the preparation of QCM sensor for the detection of bilirubin in human plasma. Bilirubin imprinted poly-(2-hydroxyethyl methacrylate-N-methacryloyl-L-tryptophan methyl ester) (PHEMATrp) nanofilm (MIP) on the gold surface of QCM chip was synthesized by molecular imprinting technique. Meanwhile, the non-imprinted PHEMATrp (NIP) nanofilm was synthesized by the same experimental technique to examine the imprinting effect. Characterization of MIP and NIP nanofilms on the QCM chip surface was achieved by atomic force microscopy (AFM), ellipsometry, fourier transform infrared spectrophotometry-attenuated total reflectance (FTIR-ATR) and contact angle measurements (CA). The observations indicated that the nanofilm was almost monolayer. Thereinafter, the imprinted and the non-imprinted QCM chipss were connected to QCM system to investigate kinetic and affinity properties. In order to examine the selectivity of the MIP-PHEMATrp nanofilm, competitive adsorption of bilirubin, cholesterol and estradiol was performed. Limit of detection (LOD) and limit of quantitation (LOQ) values were calculated as 0.45 μg/mL and 0.9 μg/mL, respectively.

Nano surface finishing can be achieved from the recently developed electrophoretic deposition-assisted polishing (EPDAP) process. This state-of-art work presents a comparison of the effect of surface roughness and material removal rate on the grit size, axial load, rotational speed, polishing time, etc. for SS304, SS316L and alumina ceramics. The EPDAP process can assess the surface's characteristics' quantity, and quality on different materials which have been tried on SS316L, SS304, inner bore of SS304, and Al2O3 in our laboratory. Uniform dispersion of homogeneous micro-sized abrasive grains was accomplished by electrophoretic deposition (EPD) to create the polishing tool employed in this process. Polishing time, axial loading, grit size, and speed of rotation of the polishing tool was selected as the control parameters. The polishing tool undergoes erosion of blunt-edged-abrasive grains due to abrasion during the EPDAP process. Therefore, the polishing tool was continuously revitalized by adding fresh abrasives. The analysis of the experiments is conducted using ANOVA and the evolutionary algorithm teaching-learning method. EPDAP process revealed improved surface reflection as well as improvement in surface finish. Thus, optimization of the polishing parameters was conducted for the experiments. The surface roughness achieved from the experiments is 0.04557 microns for SS316L, 0.038 microns for SS304, and 0.404 microns for Al2O3. Therefore, 93.92%, 97.64 %, and 73.75 % of improvement in the surface finish were achieved in SS316L, SS304, and Al2O3, respectively.

The utilization of Artificial Intelligence (AI) in biotechnology is widespread, addressing challenges such as elucidating disease pathophysiology and novel therapeutic approaches. AI plays a critical role in tasks such as drug manufacturing, chemical analysis, RNA and DNA sequencing, and enzyme studies by automating processes and expediting data collection and analysis. AI has particularly transformed the analysis of omics data for researchers, providing tools to handle complex and multifaceted datasets effectively. Through AI, integrative analysis of multi-omics data can be conducted in efficient ways. This article discusses the application of multi-omics and AI technologies in studying various diseases related to infection (COVID-19), mutation (cancer), and senescence (alopecia) to understand their pathophysiology. The applications of AI were performed as the aspect of the diagnosis, prognosis, and treatment for each disease. We introduce AI-driven analysis of pathophysiology can overcome traditional limitations in pathology, with diverse features showing potential in defining the causes of diseases, and potentially enhancing primary omics data in the distinguish the subtype of diseases. In this context, integrated approaches using multi-omics data and AI technology could provide fresh biological insights by comprehending the molecular mechanism of diseases and is instrumental in advancing precision medicine. We anticipate that the rapidly advancing AI-based multi-omics data analysis will significantly contribute to promoting healthy longevity.

The discovery of the quantum tunnelling effect---the transmission of particles through a high potential barrier---was one of the most impressive achievements of quantum mechanics made in the 1920s. Responding to the contemporary challenges, I introduce a novel deep neural network (DNN) architecture that processes information using the effect of quantum tunnelling. I demonstrate the ability of the quantum tunnelling DNN (QT-DNN) to recognise optical illusions like a human. Hardware implementation of QT-DNN is expected to result in an inexpensive and energy-efficient neuromorphic chip suitable for applications in autonomous vehicles. The optical illusions recognition tests developed in this paper should lay foundations for cognitive benchmarking tasks for AI systems of the future, benefiting the fields of sociophysics and behavioural science.

This paper explores the relationships between marketing strategies, social media platforms, the adoption and diffusion of products and services, and organizational resilience to gain an understanding of the ability of organizations to cope with disruptive events (e.g., COVID-19). This can be achieved through effective marketing strategies and the efficient use of social media platforms. In fact, resilience thinking can help organizations maintain their operations by increasing the adoption and diffusion of their products and services through the use of effective marketing strategies. To achieve this article’s objectives, a literature review is conducted to help develop a conceptual framework that highlights a clear relationship between the previously discussed concepts. In addition, the novel nature of the relationship between marketing strategies and organizational resilience is dissected and its significance for organizations is demonstrated. In fact, the findings of this study suggest that effective marketing strategies that consider the "what," "who," and "how" dichotomies and the efficient use of social media to promote the adoption and diffusion of products and services can significantly enhance the resilience of organizations. These relationships can serve as a starting point for further studies examining and empirically analyzing marketing strategies and their positive effect on strengthening organizational resilience.

To advance probiotic research, a comprehensive understanding of bacterial interactions with human physiology at the molecular and cellular levels is fundamental. Lacticaseibacillus rhamnosus LGG® is a bacterial strain that has long been recognized for its beneficial effects on human health. Probiotic effector molecules derived from LGG®, including secreted proteins, surface-anchored proteins, polysaccharides, and lipoteichoic acids, that interact with host physiological processes have been identified. In vitro and animal studies have revealed that specific LGG® effector molecules stimulate epithelial cell survival, preserve intestinal barrier integrity, reduce oxidative stress, mitigate excessive mucosal inflammation, enhance IgA secretion, and provide long-term protection through epigenetic imprinting. Pili on the cell surface of LGG® promote adhesion to the intestinal mucosa and ensure close contact to host cells. Extracellular vesicles produced by LGG® recapitulate many of these effects through their cargo of effector molecules. Collectively, the effector molecules of LGG® exert a significant influence on both the gut mucosa and immune system, that promotes intestinal homeostasis and immune tolerance.

This study explores the impact of regular physical exercise on the quality of life and body image among Para-athletes, emphasizing the necessity of such activities for physical, social, and psychological health. This qualitative study employs content analysis and ethnomethodology to evaluate seven Para-athletes from the Navy's Paralympic Project, using focus group interviews to assess their perceptions. The findings reveal that participation in structured sports programs positively influences body image and quality of life, leading to improved performance in training, social interactions, and competitive events. The analysis suggests that motivation and self-esteem are fundamental to the sporting success of Paralympic athletes. Both external and internal motivations are crucial for sustaining athletic careers and overcoming challenges. Self-esteem enables athletes to remain confident in their abilities, even after setbacks.

Cervical cancer is caused by a persistent and high-grade infection, caused by the Human Papilloma Virus (HPV), which, when entering cervical cells, alters their physiology and generates serious lesions. HPV 18 - is among those most involved in carcinogenesis in this region, but there are still no drug treatments that cause cure or total remission of lesions caused by HPV. Knowing that L-Asparaginase is an amidohydrolase, which plays a significant role in the pharmaceutical industry, particularly in the treatment of specific cancers, due to its antitumor properties, some studies have demonstrated its cytotoxic effect against cervical cancer cells. However, the commercial version of this enzyme has side effects, such as hypersensitivity, allergic reactions and silent inactivation due to the formation of antibodies. To mitigate these adverse effects, several alternatives have been explored, including the use of L-asparaginase from other microbiological sources, which is the case with the use of the fungus Aspergillus niger, a high producer of L-asparaginase. The study investigated the influence of the type of fermentation, precipitant, purification, characterization and in vitro cytotoxicity of L-asparaginase. The results revealed that semisolid fermentation produced higher enzymatic activity and protein concentration of A. niger. The characterized enzyme showed excellent stability at pH 9.0, temperature of 50ºC, resistance to surfactants and metallic ions, and an increase in enzymatic activity was also noted with the organic solvent ethanol. Furthermore, it exhibited low cytotoxicity in GM and RAW cells, and significant cytotoxicity in HeLa cells. These findings indicate that L-asparaginase derived from Aspergillus niger may be a promising alternative for pharmaceutical production. Its attributes, including stability, activity and low toxicity in healthy cells, suggest that this modified enzyme could overcome challenges associated with antitumor therapy.

Research on White Etching Cracks (WEC) apparent in multiple bearing applications has identified various drivers to cause them. Lubricants, Electricity combined with contact mechanics are proven to catalyze WEC significantly. However, none of them is solely causing WEC by its own, instead combinations discretize whether WEC appears or not. Hence, the WEC topic appears to be multidimensional, making WEC still unpredictable. The current paper is about a systematic study by the use of a DGBB test rig on how lubricant chemicals combined with electricity and by varying oil flow and pressure lead to WEC. It becomes evident, that even WEC critical conditions turn to be WEC free by simply increasing the oil flow and decrease the contact pressure.

Autonomous search is an ongoing cycle of sensing, statistical estimation and motion control with objective to find and localise targets in a designated search area. Traditionally, the theoretical framework for autonomous search combines the sequential Bayesian estimation with the information theoretic motion control. This paper formulates autonomous search in the framework of possibility theory. Although the possibilistic formulation is slightly more involved than the traditional, it provides means for quantitative modelling and reasoning in the presence of epistemic uncertainty. This feature is demonstrated in the paper in the context of partially known probability of detection, expressed as an interval value. The paper presents an elegant Bayes-like solution to sequential estimation, with the reward function for motion control defined to take into account the epistemic uncertainty. The advantages of the proposed search algorithm are demonstrated by numerical simulations.

Agriculture is an important sector of European Union in societal , economic and environmental dimensions. Sustainability of the sector require improvements in its key operations. Weeding is one of the crucial operation having impact on farms’ productivity, safety for farmers, food security and environmental state. To achieve EU policy goals of sustainable agriculture there are needed new technical and organizational solutions. One of the advanced techniques are laser based weeding technique. It is important to understand well the impacts of introduction of these tech-niques to the markets and agricultural practice in sustainability context. For that reason Social Life Cycle Assessment was performed. The method was based on participatory approach. The as-sessment was carried in three perspectives: general society, farmers, and business perspective re-lated to agriculture. Expert interviews based on questionnaires and workshops were realized to get the opinions on the impacts of a new laser technique on particular aspects of its implementation. The results show generally positive impact in all perspectives, especially in the perspective of farmers.

Adipose tissue undergoes changes with aging, leading to increased adiposity, inflammatory cell infiltration, reduced angiogenesis, heightened oxidative stress, and alterations in its metabolic function. Regular exercise has been recognized as a powerful intervention that can positively influence adipose tissue health and mitigate the effects of aging. However, the molecular mechanisms underlying the benefits of regular exercise on aging adipose tissue function remain poorly understood. Adipokines released through regular exercise play a potential role in mitigating adipose tissue aging, enhancing the metabolism of glucose and lipids, reducing inflammation and fibrosis, and promoting fat browning and thermogenesis. The goal of this review is to offer a comprehensive overview of the benefits of regular exercise in addressing the age-related decline in adipose tissue function. The significance of regular exercise in mitigating metabolic disorders associated with aged adipose tissue will be discussed.

Excretory-secretory products (ESPs) are the main research targets for investigating the hosts and helminths interaction. Parasitic worms can migrate to parasitic sites and avoid the host immune response by secreting this product. Angiostrongylus cantonensisis an important food-borne zoonotic parasite that causes severe neuropathological damage and symptoms, including eosinophilic meningitis or meningoencephalitis in humans. Benzaldehydes are organic compounds composed of a benzene ring and formyl substituents. This compound has anti-inflammatory and antioxidation properties. Previous studies showed that 3-hydroxybenzaldehyde (3-HBA) and 4-hydroxybenzaldehyde (4-HBA) can reduce apoptosis in A. cantonensis ESPs treated astrocytes. These results on the protective effect underlying benzaldehyde have primarily focused on cell survival. The study was designed to investigate the molecular mechanisms of endoplasmic reticulum stress (ER stress) and oxidative stress in astrocytes in A. cantonensis ESPs treated astrocytes and to evaluate the therapeutic consequent of 3-HBA and 4-HBA. First, we initially established the RNA-seq dataset in each group, including Normal, ESPs, ESPs+3-HBA, and ESPs+4-HBA. We also found that benzaldehyde (3-HBA and 4-HBA) can stimulate astrocytes to express ER stress-related molecules after ESP treatment. The level of oxidative stress could also be decreased in astrocytes by elevating antioxidant activity and reducing ROS generation. These results suggested that benzaldehyde may be a potential therapeutic compound for human angiostrongyliasis to support brain cell survival by inducing the expression levels of ER stress- and oxidative stress-related pathway.

Glioblastoma (GBM) is an aggressive brain cancer characterized by significant molecular and cellular heterogeneity, complicating treatment efforts. Current standard therapies, including surgical resection, radiation, and temozolomide (TMZ) chemotherapy, often fail to achieve long-term remission due to tumor recurrence and resistance. A pro-oxidant environment is in-volved in glioma progression, with oxidative stress contributing to the genetic instability that leads to gliomagenesis. However, the generation of reactive oxygen species (ROS) results in the dysregulation of antioxidant defenses in GBM cells, creating a vulnerability that can be thera-peutically exploited. Evaluating pro-oxidant therapies in brain tumors is crucial due to their po-tential to selectively target and eradicate cancer cells by exploiting the elevated oxidative stress levels inherent in these malignant cells, thereby offering a novel and effective strategy for overcoming resistance to conventional therapies. This study investigates the therapeutic potential of doxorubicin (DOX) and photodynamic therapy (PDT) with Me-ALA, focusing on their effects on redox homeostasis. Basal ROS levels and antioxidant gene expression (NFE2L2, CAT, GSR) were quantitatively assessed across GBM cell lines, revealing significant variability probably linked to genetic differences. DOX and PDT treatments, both individually and in combination, were ana-lyzed for their efficacy in inducing oxidative stress and cytotoxicity. An in silico analysis further explored the relationship between gene mutations and oxidative stress in GBM patients, providing insights into the molecular mechanisms underlying treatment responses. Our findings suggest that pro-oxidant therapies, such as DOX and PDT in combination, could selectively target GBM cells, highlighting a promising avenue for improving therapeutic outcomes in GBM.

In this study, the causal-effect between carbon dioxide emissions and forestry production and trade was investigated in Ghana by employing a data spanning from 1961 to 2014 by using the VECM and ARDL model. Evidence of the long-run equilibrium relationship in the VECM shows that, a 1% increase in veneer sheet production reduces carbon dioxide emissions by 1.47% in the long-run. There was evidence of a bidirectional causality between carbon dioxide emissions and veneer sheet production, carbon dioxide emissions and wood charcoal production, and a unidirectional causality running from carbon dioxide emissions to wood fuel production and plywood production to carbon dioxide emissions. Evidence from the long-run equilibrium relationship in the ARDL model shows that; a 1% increase in plywood production will increase carbon dioxide emissions by 0.17% in the long-run, a 1% increase in sawnwood production will increase carbon dioxide emissions by 0.17% in the long-run, a 1% increase in wood charcoal production will increase carbon dioxide emissions by 0.36% in the long-run and a 1% increase in wood fuel production will increase carbon dioxide emissions by 0.37% in the long-run.

Previous research on consumer perceptions of salmon has often neglected Indigenous rights within the Canadian salmon industry. This study acknowledges the decline in salmon farm licenses in British Columbia and aims to achieve three main objectives: (1) gain a comprehensive understanding of four consumer perceptions—environmental sustainability, economic considerations, Indigenous rights, and price increase—across diverse consumer profiles through a cross-national online survey; (2) identify factors influencing these four consumer perceptions; and (3) assess the impact of these perceptions, along with socio-demographic variables and consumer motivations, on seven purchasing behaviors related to Canadian salmon products. Data analysis employs the graded response model and cumulative link models. The results illustrate how consumer profiles influence the four perceptions and their key determinants. Additionally, the study quantifies the impact of environmental sustainability, economic considerations, Indigenous rights, and a price increase on consumer purchasing behaviors.

Background and Objectives: The trends in metabolic dysfunction-associated steatotic liver disease (MASLD) and related metabolic dysfunctions in Japan is unknown. Thus, we aimed to clarify the trends of these before the novel coronavirus disease 2019 pandemic in Japan. Materials and Methods: We included Japanese individuals aged 25-79 years who underwent health examinations at our center. We analyzed anthropometry, lifestyle-related disease, and nutritional intake in relation to MASLD trends from 2010-2019. Results: The prevalence of MASLD increased in all ages and body mass index (BMI) classes, reaching 30.3% in males and 16.1% in females, with MASLD accounting for 75% of steatotic liver cases and more than half of all type 2 diabetes mellitus (T2DM) and high waist circumference (HWC) cases. The increase in the prevalence of MASLD was due to an increase in that of steatotic liver itself, and there was no increase in the prevalence of other factors, such as overweight, T2DM, hypertension, and dyslipidemia. The prevalence of glucose metabolic disorders (GMDs) and hypertension decreased. National nutritional data showed an increase in energy intake, total fat, saturated fatty acids, monounsaturated fatty acids, and polyunsaturated fatty acids, which correlated with a decrease in GMDs. Salt intake also decreased, which correlated with hypertension. The MASLD group had a higher prevalence of all related metabolic factors than the non-MASLD group, especially HWC, T2DM, and hyperlipidemia. Conclusion: The prevalence of MASLD increased with that of steatotic liver, regardless of age or BMI. A relationship between increased dietary fat, increased steatotic liver, and decreased GMDs was suggested.

One of the most common solutions to access underground is through the construction of circular shafts. At preliminary design stage analytical expressions are often used to estimate the horizontal earth pressure acting on the lining. However, most of these solutions are based on active state conditions and do not consider the restrictions imposed on the shaft lateral displacements by the rigidity of its lining or by the construction method employed. To overcome this limitation a new methodology to account with the shaft lateral displacements in the determination of the horizontal earth pressure acting on circular shafts is developed and validated against the results of relevant physical scale models. The obtained results show good agreement with the measured data, reproducing adequately the reduction of the horizontal earth pressure from its initial value to an active state condition with the increase of the shaft lateral displacement. Following this approach more realistic predictions of the earth pressure acting on the lining of circular shafts can be performed.

This text deals with exploring random solutions for generalized nonlinear variational inequality problems. Using the Fan-KKM theorem and Aumann's measurable selection theorem, we are able to prove the existence and uniqueness of random solution sets under the conditions of monotonicity and convexity. Additionally, we use Minty's lemma to demonstrate the compactness and convexity of the random solution sets.

Machine Learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. This is beneficial, especially in pain recognition, where patient communication may be limited. However, ML models often face challenges such as memorization and sensitivity to adversarial examples. Regularization techniques like mixup, which trains models on convex combinations of data pairs, address these issues by enhancing model generalization. While mixup has proven effective in image, speech, and text datasets, its application to time-series signals like electrodermal activity (EDA) is less explored. This research uses ML for pain recognition with EDA signals from the BioVid Heat Pain Database to distinguish pain by applying mixup regularization to manually extracted EDA features and using a Support Vector Machine (SVM) for classification. Results showed that this approach achieved an average accuracy of 75.87% using leave-one-subject-out cross-validation (LOSOCV) compared to 74.61% without mixup. This demonstrates the mixup’s efficacy in improving ML model accuracy for pain recognition from EDA signals. This study highlights the potential of mixup in ML as a promising approach to enhance pain assessment in healthcare.

Critical phases in socio-technical systems (STS) design are the definition of functional requirements for automated or software supported human activities while also addressing social and human interaction issues. To define automation support for human operations, STS designers need to ensure that specifications will satisfy not only non-functional requirements (NFR) of the system but also of its human actors such as human reliability/workload. However, such human factors aspects are not addressed sufficiently with traditional STS design approaches which could lead to systems failure or rejection. This paper proposes a new STS design method that addresses this problem and introduces a novel type of requirements namely Task Support Requirements (TSR) that assists in specifying the functionality that the system should have to support human agents in undertaking their tasks by addressing human limitations. The proposed method synthesizes a requirements/software engineering approach to STS design with functional allocation and an HCI perspective which facilitate application of human factors knowledge in conceptual models and evaluation through VR simulation. A case study methodology is employed in this work that allows in-depth, multi-faceted explorations of the complex issues that characterize STS.

The majority of local IPv6 networks continue to remain insecure and vulnerable to neighbor spoofing attacks. The Secure Neighbor Discovery (SEND) standard and its concomitant Cryptographically Generated Addressing (CGA) scheme were accepted by large standards bodies to codify practical mitigations. SEND and CGA have never seen widespread adoption due to their complexities, obscurity, costs, compatibility issues, and continued lack of mature implementations. In light of their poor adoption, research since their standardization has continued to find new perspectives and proffer new ideas. The orthodox solutions for securing Neighbor Discovery have historically struggled to successfully harmonize three core ideals: simplicity, flexibility, and privacy preservation. This research introduces Voucher-Based Addressing, a low-configuration, low-cost, and high-impact alternative to IPv6 address generation methods. It secures the Neighbor Discovery address resolution process while remaining simple, highly adaptable, indistinguishable, and privacy-focused. Applying a unique concoction of cryptographic key derivation functions, link-layer address binding, and neighbor consensus on the parameters of address generation, resolved address bindings are verifiable without the need for complex techniques that have hindered the adoption of canonical specifications.

Abstract: The conundrum of Latin American countries in the green transition unveils new opportunities and old structural problems. There is a clear advantage in the energy grid for Argentina, Brazil, and Mexico, coming mainly from hydropower, solar, and wind energy. In 2022, Brazil had the largest solar photovoltaic energy generation capacity in Latin America and the Caribbean, with over 24 gigawatts. Mexico ranked second, with some 9 gigawatts, and Chile ranked third, with a capacity of around 6 gigawatts. The window of opportunity for the industrialized Latin American countries resides chiefly in greening the natural resources industries and agriculture and husbandry, mainly for Brazil and Argentina. Latin America faces the dual challenge of promoting renewable energy and upgrading its value chains while managing the socioeconomic dynamics inherent in its developmental stages. It poses an enormous challenge for Latin American countries because of the phenomenon of "time compression" (Whittaker et al., 2020). Developing country's innovation and technology-intensive segments coexist with informal labor and low-skilled workers. Far from disappearing with economic growth, such coexistence becomes reinforced through an increasingly wide array of temporary and insecure jobs. Time compression condenses different stages of industrial and economic development within the same space and time, directly affecting the indicators related to the reduction of environmental impacts. Instead of locking in one path for this transition, Latin American countries had better not cling to panaceas but consider the threats, the risks, and the need to experiment with multiple paths and solutions simultaneously. However, this change implies the formation of a genuine developmental and environmental coalition guided by rigorous strategic considerations not captured by immediate interests.

Recently, there appeared in this journal (Beh and Lombardo \textbf{2022}, {\it Symmetry}, 14, 1103) a paper that showed how to perform a correspondence analysis on a two-way contingency table where Bowker's statistic lies at the numerical heart of this analysis. Thus, we showed how this statistic can be used to visually identify departures from perfect symmetry. Interestingly, Bowker's statistic is a special case of the symmetry-version of the Cressie-Read family of divergence statistics. Therefore, this paper presents a new framework for visually assessing departures from perfect symmetry using a second-order Taylor series approximation of the Cressie-Read family of divergence statistics.

Tobacco mosaic virus (TMV) was the first virus to be studied in detail and for many years TMV and other tobamoviruses, particularly tomato mosaic virus (ToMV) and tobamoviruses infecting pepper (Capsicum spp.), were serious crop pathogens. By the end of the twentieth and for the first decade of the twenty-first century tobamoviruses were under some degree of control due to introgression of resistance genes into commercial tomato and pepper lines. However, tobamoviruses remained as important models for molecular biology, biotechnology and bio-nanotechnology. Recently, tobamoviruses have again become serious crop pathogens due to the advent of tomato brown rugose fruit virus, which overcomes tomato resistance against TMV and ToMV, and the slow but apparently inexorable worldwide spread of cucumber green mottle mosaic virus, which threatens all cucurbit crops. This review discusses a range of mainly molecular biology-based approaches for protecting crops against tobamoviruses. These include cross-protection (using mild tobamovirus strains to ‘immunize’ plants against severe strains), expressing viral gene products in transgenic plants to inhibit the viral infection cycle, inducing RNA silencing against tobamoviruses by expressing virus-derived RNA sequences in planta or by direct application of double-stranded RNA molecules to non-engineered plants, gene editing of host susceptibility factors, and the transfer and optimization of natural resistance genes.

This paper addresses the parameter identification of a one-link flexible manipulator based on the experimental measurement of the inputs/outputs, the finite element model, and the application of evolutionary algorithms. A novel approach is proposed to find the values of inertia, stiffness, and damping parameters by minimizing the difference between the numerical model’s outputs and the testbed’s outputs, considering the joint position and acceleration of the link’s tip. The dynamic model is initially obtained using the finite element method and the Lagrange principle. A prototype of a single one-link flexible manipulator is used in the experimental application wherein the servomotor applies the input torque, and the outputs are the joint angle and the link’s tip acceleration. Then, an optimization problem minimizes the difference between numerical and experimental outputs to determine the set of parameters using evolutionary algorithms. A comparative analysis to obtain the identified parameters is established using genetic algorithms, particle swarm optimization, and differential evolution. The proposed identification approach permitted the determination of the dynamic parameters based on the complete dynamic model of the flexible-link manipulator, which is different from the approaches reported in the literature that identify a simplified model. This information is essential for the design of the motion and vibration control laws.

Long-range HNCO NMR spectra for proteins show crosspeaks due to 1JNC, 2JNC, 3JNCg and h3JNC couplings. The h3JNC couplings are transmitted through hydrogen bonds and are correlated to hydrogen bond lengths. We collected long-range HNCO data at a series of temperatures for four protein states. P22i and CUS-3i are six-stranded beta-barrel I-domains from phages P22 and CUS-3 that share less than 40% sequence identity. The cis and trans states of the C-terminal domain from pore-forming toxin hemolysin ΙΙ (HlyIIC) arise from isomerization of a single G404-P405 peptide bond. For P22i and CUS-3i the hydrogen bonds detected by NMR agree with those observed in the corresponding domains from cryoEM structures of the two phages. Hydrogen bond lengths derived from the h3JNC couplings, however, are poorly conserved between the distantly related CUS-3i and P22i domains and show differences even between the cis and trans states of HlyIIC. This is consistent with hydrogen bond lengths being determined by local differences in structure rather than the overall folding topology. With increasing temperature hydrogen bonds typically show an apparent increase in length, which has been attributed to protein thermal expansion. Some hydrogen bonds are invariant with temperature, however, while others show apparent decreases in length suggesting they become stabilized with increasing temperature. Considering the data for the three proteins in this study and previously published data for ubiquitin and GB3, lowered protein folding stability and cooperativity correlates with a larger range of temperature responses for hydrogen bonds. This suggests a partial uncoupling of hydrogen bond energetics from global unfolding cooperativity as protein stability decreases.

The review emphasizes the importance of the petroleum refining industry in Iraq, highlighting the environmental obstacles it presents. The industry's significant role in the country's economy necessitates a crucial evaluation and improvement of the environmental impact of oil refining in Iraq. The study scrutinizes the progression of crude oil refining techniques, from basic distillation to advanced technologies like vacuum distillation, thermal cracking, and catalytic cracking, all of which are associated with environmental damage. The refining processes produce hazardous gases such as carbon dioxide, carbon monoxide, sulfur oxides, and nitrogen oxides. Refineries also consume substantial amounts of water and discharge effluent, causing environmental issues. It highlights that significant solid waste is produced throughout the refining process, contributing to the environmental difficulties caused by the petroleum industry in Iraq. The paper proposes several approaches to enhancing the environmental performance of Iraq's petroleum refining processes through a comprehensive environmental impact assessment, focusing on identifying problematic areas, measuring environmental damage, and proposing solutions. It provides insights into the types of waste generated in Iraqi refineries, such as polluted hydrocarbon liquids, and highlights the environmental impact of leaks and spills from oil and byproducts, affecting groundwater and soil quality. The paper suggests reusing consumable auxiliary agents and mineral elements in other industrial fields to minimize waste generation, as well as employing advanced technologies like artificial intelligence and machine learning for efficient refining processes and reduced effluents. By offering suggestions to policymakers, industry stakeholders, and environmental regulators in Iraq, the study aims to inform sustainable practices and environmental management strategies within the petroleum refining sector, contributing to improved environmental outcomes and sustainable development in the industry.

Improving the quality of petroleum products and refining processes through the use of artificial intelligence and machine learning techniques is the topic of this article. It shows that expert knowledge and conventional empirical models can't get you where you want to go in a refining process. To effectively capture complex interactions and forecast fuel qualities, machine learning techniques such as principal component analysis (PCA), support vector machines (SVM), artificial neural networks (ANN), and partial least squares (PLS) are suggested. Gasoline and other petroleum products, as well as property prediction, process control, product quality, and operational efficiency in refineries, can all be improved with the help of machine learning applied to spectral or distillation curve data. An exciting new direction in optimizing operations, meeting environmental norms, and precisely estimating gasoline quality is offered by advanced machine learning algorithms.

Understanding the genotype x environment (GE) interaction for traget trait is valuable for growers, agronomists, breeders and the seed industry. The objective of this study was to see and explore the genotype x environment interaction for grain yield of wheat to better guide genotype selection and breeding. The genotype main effect and GE interaction effect (GGE) biplots resolved the following three genotypes: 85 (ETW17-377), 82 (ETW17-374) and 13 (ETW17-281) for being the best yielding and stable. The Kulumsa site planted on 4^th of July 2017 (environment 7=E7) and the DebreZeit site on 20^th of July 2017 (E3) were the most effective environments in distinguishing the test genotypes and are thus suitable environments for future evaluation and selection with specific adaptation. Melkasa site planted on 4^th July (E9) was a representative environment where genotypes can be evaluated for wide adpaptation. The genetic covariate biplot indicated that thousand kernel weight (TKW) and plant height (PH) had significant positive associations with grain yield implying the possible concurrent indirect selection of both traits for exploiting GE interaction for grain yield of wheat.

Abstract Plant fungal pathogens are frequently found as one of limiting factors for crop production. More than 10,000 species of fungi can cause disease in plants. To control the diseases, many farmers are still rely on the use of chemical fungicides, however most synthetic fungicides can cause acute toxicity, and some cause chronic toxicity as well. Thus, an appropriate technological improvement towards a more effective use of natural resources is required in agriculture to develop environmentally friendly sustainable farming system. This paper highlights the potential of extracts of tropical plants as antifungal agent to control plant fungal diseases. Information and data presented in this paper are mainly derived from selected and related references that previously published in the scientific journals. Many higher plants of tropical origin with fungicidal activities and their potential for fungal disease control of agricultural crops have been studied, however most of the studies have been done under in vitro condition. Some plant extracts showed strong antifungal activities on in vitro as well as in vivo tests, but some plant extracts showed significant antifungal activities on in vitro test, but did not obvious on in vivo tests. A great variation in antifungal activities were shown by plants extracts of different species and plant parts, in one hand, and on the other hand, variation was also observed on the responses of different fungal species to the same plant extract. Since the purpose of the use of plant extract is to control plant fungal diseases, the field trial is needed to ensure the stability of efficacy of certain plant extract. In addition, isolation and identification of active substances in the extracts is needed to assess possible mode of action and side effect of their use.

Cutaneous mycoses are common infections whose treatment has become more complex due to increasing antifungal resistance and the need for prolonged therapies, hindering patient adher-ence and increasing the incidence of adverse effects. Consequently, the use of physical therapies, especially photodynamic therapy (PDT), has increased for the treatment of onychomycosis due to its antimicrobial capacity mediated by the production of reactive oxygen species. This study investigates the in vitro effect of the combination of blue light (448 nm) and red light (645 nm) with terbinafine on the cell viability of human keratinocytes and the production of reactive ox-ygen species. The combination of terbinafine and blue light exhibits a synergistic effect on the production of reactive oxygen species and a significant increase in the expression of superoxide dismutase (SOD), an enzyme involved in the reduction of superoxide ions (O2-) and the genera-tion of hydrogen peroxide (H2O2). Such effect could be useful in clinical practice to improve the response of cutaneous mycoses to pharmacological treatment, reduce their toxicity, and shorten their duration.

Sporotrichosis’ clinical manifestations, lesions in the cutaneous or subcutaneous tissues, it is caused by dimorphic fungi of the Sporothrix complex, is a cosmopolitan mycosis that affects humans and animals. This study aimed to identify prevalent species in Minas Gerais, Brazil, and characterize virulence profiles and antifungal susceptibility associated with Sporothrix isolates. Isolates were identified using macro and micro-morphological traits as well molecular biology techniques. Antifungal susceptibility was determined using the Minimal Inhibitory Concentration test, while, virulence factors such as growth at different temperatures, biofilm formation, melanin production, and urease activity were assessed. Our findings revealed S. brasiliensis as a predominant species isolated. Urease activity was a crucial factor associated with growth at 50°C in S. brasiliensis isolates, highlighting their high thermotolerance. Furthermore, strains isolated from humans produced a more robust biofilm compare to those from felines. Strains of S. brasiliensis isolated from Felis catus exhibited faster melanin production, decreased susceptible to antifungal agents tested, and superior growth capacity at 50°C to strains isolated from humans. Therefore, our study highlights the prevalence of S. brasiliensis causing sporotrichosis in both human and felines. However, it underscores the circulation of different strains, with varying levels of virulence and susceptibility to antifungal agents.

Potential permanent magnet (Fe0.7Co0.3)2B has large saturation magnetization and high Curie temperature but a moderate magnetocrystalline anisotropy (MCA) and displays relatively low coercivity. One way to improve coercivity is to combine the contribution from magnetocrystalline- and magnetic shape anisotropy by preparing (Fe0.7Co0.3)2B nanowires. We study the effect of size, morphology, and surface defect on the hard magnetic properties of nanowires using micromagnetic simulation. The hard magnetic properties of (Fe0.7Co0.3)2B nanowire bonded magnet have been estimated, including the role of inter-wire magnetostatic interaction. By considering the existence of local reduction of MCA energy up to 30% on the surface layer of nanowires, the anisotropic bonded magnet with 65% vol. of (Fe0.7Co0.3)2B nanowires displays typical magnetic properties of remanence Br=8.4 kG, coercivity Hci=9.9 kOe, and maximum energy product (BH)m=17.8 MGOe. Developing effective technology for synthesizing nanowires and fabricating corresponding bonded magnets is promising for manufacturing practical magnets based on magnetic phase with relatively low or moderate MCA, such as (Fe0.7Co0.3)2B.

Industry 4.0 is an industrial paradigm that is causing changes in form and substance in factories, companies and businesses around the world, and is impacting work and education in general. In fact, the disruptive technologies that frame the fourth industrial revolution have the potential to improve and optimize manufacturing processes and the entire value chain, which can lead to an exponential evolution in the production and distribution of goods and services. All these changes imply that the fields of engineering knowledge must be oriented towards the concept of Industry 4.0, for example Mechanical Engineering. The development of various physical assets that are used by cyber-physical systems and digital twins is based on Mechanics. However, the specialized literature on Industry 4.0 says little about the importance of Mechanics in the new industrial era and more importance is given to the evolution of Information and Communication Technologies and Artificial Intelligence. This article presents a frame of reference about the importance of Mechanical Engineering in Industry 4.0 and proposes an extension to the concept of Mechanics 4.0, recently defined as the relationship between Mechanics and Artificial Intelligence. For the analysis of Mechanical Engineering in Industry 4.0, the criteria of the four driving forces that defined Mechanics in the third industrial revolution was used. An analysis of Mechanical Engineering Education in Industry 4.0 is presented, and the concept of Mechanical Engineering 4.0 Education is improved. Finally, the importance of making changes in the educational models of engineering education is described.

This paper examines the impact of stringent regulations on personal data protection on customer perception of data security and online shopping behavior. In the context of the rapidly expanding e-commerce landscape, ensuring the security of personal data is a complex and crucial task. The study of several legal frameworks, including Malaysia's compliance with EU regulations and Indonesia's Personal Data Protection Law, provides valuable insights into consumer data protection. The challenges of balancing data safeguarding and unrestricted movement and tackling misuse by external entities are significant and require careful consideration. This research elucidates the pivotal role of trust in e-commerce environments and the deployment of innovative e-commerce models designed to minimize personal data sharing. By integrating advanced privacy-enhancing technologies and adhering to stringent regulatory standards such as the GDPR, this study demonstrates effective strategies for robust data protection. The paper contributes to the academic discourse by providing a comprehensive framework that synergizes legal, technological, and procedural elements to fortify data security and enhance consumer trust in digital marketplaces. This approach aligns with international data protection standards and offers a pragmatic blueprint for achieving sustainable data security in e-commerce.

This article focuses on the leader-following consensus of fuzzy fractional order singular perturbation multi-agent systems (FOSPMASs) with order in 0,2. By employing the T-S fuzzy modeling approach, a fuzzy FOSPMAS is constructed. Subsequently, a fuzzy observer-based controller is designed and the error system corresponding to each agent is derived. Through a series of equivalent transformations, the error system is decomposed into fuzzy singular fractional order systems (SFOSs). According to the admissibility of SFOSs, the consensus conditions of the fuzzy FOSPMAS are obtained based on linear matrix inequalities (LMIs) without equality constraint. Finally, the effectiveness of the criteria is verified through an RLC circuit model.

Electricity price signal is a direct reflection of the attributes of electric power commodities, but due to the influence of market supply and demand, power producers' offer, line capacity and other factors, electricity price signals often show a variety of abnormal forms. The identification and traceability of abnormal tariff signals is an important daily work of power trading centers at all levels. However, at present, it is common to rely on manual experience to analyze the causes of abnormal tariffs, which is inefficient and difficult to ensure the objective and scientific traceability of abnormal tariff causes. Since different types of abnormal tariff signals are caused by different dominant factors, if the key features of abnormal tariffs can be classified and matched, the identification efficiency can be effectively improved. For this reason, this paper proposes a traceability method of abnormal tariffs based on the comparison analysis of key features. First of all, based on the characteristics of historical tariff data, this paper completes the classification of tariff spike magnitude anomaly and tariff mean value anomaly, further establishes the key features of each type of abnormal tariff signal based on principal component analysis, and finally calculates the influence degree of each element within the key features one by one based on alternative algorithms, and then realizes the screening and traceability of the causes of abnormal tariffs based on the importance of the degree of influence ordering. The effectiveness of the proposed method has been verified in a large number of examples constructed based on the actual data of the electricity market, and the average correctness rate of the proposed method for the traceability of the causes of the average value and spike anomalies of the electricity price reaches more than 85%, which can reduce the labor cost in the process of the traceability of the causes of the anomalous electricity price.

This study compares the Melbourne Decision-Making Questionnaire (MDMQ) and the General Decision-Making Style (GDMS), two of the most widely used decision-making questionnaires in the literature, in a large age- and sex-weighted general population sample of 714 men (45.7%) and 848 women (54.3%) between 18 and 90 years old. The objective was to evaluate the convergent and construct validity between several aspects of decision-making styles questionnaires. The results indicate that the two questionnaires replicate the factorial structure of four and five factors reported in the original studies respectively, through exploratory and confirmatory procedures in our cross-cultural context. The domains of both questionnaires that represent a strong or large correlation are Vigilance with Rational (.50), Hypervigilance, Buck-passing, and Procrastination with Avoidant (.45, .52 and .60). A Structural Equations Model (SEM) between both questionnaires indicates that both latent factors formed by the domains of the MDMQ and the GDMS obtain a correlation of .96. It is concluded that the two questionnaires measure similar aspects of the decision-making construct.

This study presents the complexity and sensitivity of chaotic system dynamics in the case of the double pendulum. It applied detailed numerical analyses of the double pendulum in multiple computing platforms in order to demonstrate the complexity in behavior of the system of double pendulums. The equations of motion were derived from the Euler-Lagrange formalism, in order to capture the system's dynamics, which is coupled nonlinearly. These were solved numerically using the efficient Runge-Kutta-Fehlberg method, implemented in Python, R, GNU Octave, and Julia, while runtimes and memory usage were extensively benchmarked across these environments. Time series analyses, including the calculation of Shannon entropy and the Kolmogorov-Smirnov test, quantified the system's unpredictability and sensitivity to infinitesimal perturbations of the initial conditions. Phase space diagrams illustrated the intricate trajectories and strange attractors, as further confirmation of the chaotic nature of the double pendulum. All the findings have a clear indication of the importance of accurate measurements of the initial condition in a chaotic system, contributing to an increased understanding of nonlinear dynamics. Future research directions are faster simulations using Numba and GPU computing, stochastic effects, chaotic synchronization, and applications in climate modeling. This work will be useful for understanding chaos theory and efficient computational approaches in complex systems of dynamical nature.

Scheduling and optimization have a central place in the research area of computing because it is increasingly important to achieve fully automated production processes to adjust manufacturing systems to the requirements of Industry 4.0. In this paper, we demonstrate how an automated wet-etch scheduling problem for a semiconductor industry can be solved by constraint answer set programming (CASP) and, its solver called clingcon. The considered scheduling problem includes a robot for lot transfers between the baths. CASP is a hybrid approach in automated reasoning that combines different research areas such as answer set programming, Constraint Processing and Satisfiability Modulo Theories. For a long time, exact methods such as constraint programming have displayed difficulties in solving real large-scale problem instances. Currently, the performance of state-of-the-art constrains solvers is comparatively better than a decade ago, and some complex combinatorial problems can be optimally solved. These theoretical and technical achievements open new horizons for declarative programming applications such as logistics. A successful solution to this problem is achieved and we found that in most cases tested, CASP is faster and obtains in most cases similar makespan values than solutions based on mixed integer linear programming and constraint paradigms.

Thyroid hormones are essential for regulating the metabolic rate, growth, development, and maintenance of many physiological processes in vertebrates. They are synthesized by the thyroid gland, which is an ancient organ that evolved early in vertebrate evolution. This study aims to characterize the evolutionary path of polymorphic variants associated with the hypothalamus-pituitary-thyroid (HPT) axis by comparing ancient DNA (aDNA) sequences of Late Pleistocene-Holocene hominin populations with those of modern humans. We evaluated the genetic sequences of several populations, including Neanderthals, Denisovans, Palaeolithic hunter-gatherers from European Russia and Sunghir, and Neolithic hunter-gatherers/farmers from Anatolia focusing on genes involved in the development of the thyroid gland and thyroid hormone (TH) biosynthesis, secretion, and transport. Results showed interesting variants in the DIO2 (rs225014), TPO (rs4927611), and TG (rs2069556 and rs1133076) genes. All SNPs seemed to confirm that Neanderthals, and in part Denisovas, were physiologically hypothyroidic. Probably they had lower T3 levels due to defective production or peripheral conversion. From the moment that the most favourable alleles in terms of T3 production appear during the Paleolithic, we are inclined to assume that their selection was linked to environmental pressure.Our study supports insight into the evolutionary history of the endocrine system and can help in providing understanding into the evolution of physiological systems and their adaptation to changing environments. Understanding the evolution of thyroid hormones can also shed light on the mechanisms underlying thyroid disorders in humans.

Calicioids form a research field that has encompassed ascomycetous fungi with stalked ascomata similar to those of the lichen genus Calicium. Early generic circumscriptions in calicioid lichens and fungi were mainly based on morphological and secondary chemistry information. After the introduction of molecular data, taxonomy in the group has been reconsidered. Here, based on a broad geographical sampling, Coniocybe Ach. was revised using molecular and morphological features. Three loci (ITS, LSU and rpb1) were compared to infer its phylogenetic position, and a total of 52 new sequences (14 ITS, 24 LSU and 14 rpb1) were produced. Coniocybe was revised and emended to include, apart for its type C. furfuracea, also C. brachypoda and C. confusa. In addition, a new species Coniocybe eufuracea, was described, and a key to the species of Coniocybe provided.

Currently, agro-industrial residues have been used to obtain bioactive compounds. The fermentation process is an alternative for the use of these residues and to be able to accumulate metabolites that may be of interest to different industrial sectors. The aim of this study was to evaluate solid-state fermentation conditions using pineapple waste as substrate with Aspergillus niger spp., to release polyphenolic compounds using a Plackett-Burman exploratory design. Temperature, humidity, inoculum, NaNO3, MgSO4, KCl and KH2PO4 conditions in the fermentation process were evaluated. The results revealed that the Aspergillus niger HT3 strain reached a hydrolyzable tannins accumulation of 10.00 mg/g, while Aspergillus niger Aa20 reached a condensed tannins release of 82.59 mg/g. The KH2PO4 affect the release of condensed tannins with A. niger Aa20 and MgSO4 affect the release of hydrolyzable tannins with A. niger HT3. In addition, a positive antioxidant activity was demonstrated for the DPPH, ABTS and FRAP technique. The HPLC analysis was able to identify compounds in the fermented pineapple peel such as 3-feruloylquinic acid, caffeic acid, lariciresinol and 3-hydroxyphloretin 2'-O-xylosyl-glucoside, among others. The solid-state fermentation process is a biotechnological alternative for the release of bioactive compounds.