This paper analyzes the commercialization potential of an innovative multi-sided platform for personalized learning using a qualitative research approach based on both primary and secondary data. The data was collected through surveys and semi-structured interviews with high school students, university students, and employed individuals in need of personalized upskilling. Through case study methodology and the analysis and synthesis of the collected data, the results indicate that the monetization of the innovative platform is feasible and sustainable.

Online banking continues to grow in popularity due to its convenience, but banks face significant challenges in ensuring secure customer identity verification. Traditional authentication methods such as PINs, passwords, and one-time passwords have shown limitations, especially in the wake of the COVID-19 pandemic, which accelerated the demand for seamless and contactless solutions. Voice biometrics have emerged as a reliable alternative, offering enhanced fraud protection and a more user-friendly experience. In Malaysia, this technology enables customer verification without the need for PINs or security questions. This study proposes an advanced authentication approach that integrates keystroke dynamics and voice biometrics within a multi-factor authentication framework. By leveraging artificial intelligence and fuzzy logic, the system aims to deliver heightened security and a smoother user experience. The goal is to provide Malaysian online banking users with a safer and more secure digital environment.

Benzodiazepines are used for their anxiolytic, antiepileptic, muscle relaxant and hypnotic effects. In vitro, diazepam is predominantly metabolized to temazepam and nordiazepam (N-desmethyldiazepam). Since acetylcholinesterase is involved in the metabolism of diazepam, inhibition of the enzyme activity may have a significant effect on the therapeutic effect of the drug. To determine the inhibitory effect of 2,2,4-trimethyl-2,3-dihydro-1H-benzo[b][1,4]diazepine on acetylcholinesterase enzyme activity by conducting a comprehensive analysis that includes: measuring the enzyme activity in the presence of various concentrations of the inhibitor, determining the type of inhibition through kinetic studies, and assessing the potential therapeutic applications of the inhibitor in conditions associated with acetylcholinesterase dysfunction. In this study, the inhibitory properties of 2,2,4-trimethyl-2,3-dihydro-1-Hbenzo[ b][1,4]diazepine on the activity of the enzyme acetylcholinesterase were tested spectrophotometrically at three different temperatures of 25℃, 30℃, and 37℃. The substance was synthesized by a condensation reaction between o-phenyldiamine and acetone in the presence of phosphorus oxychloride on solid support (MgO). The solid product was obtained by crystallization from n-hexane. Each tested sample contained an appropriate concentration of the substrate acetylcholine iodide (AChI) in the range from 1.00 to 4.00 mmol·L-1; 5,5-dithiobis(2-nitrobenzoic acid) (DTNB) concentration 3 mmolL-1, phosphate buffer (KH2PO4/K2HPO4) pH value 8, tested substance concentration (17.70, 35.40, 53.10 mmol·L-1), and acetylcholinesterase solution (AChE) activity 0.54 UmL-1. Using the spectrophotometric method, it was concluded that the examined diazepine shows a competitive type of inhibition on the enzyme acetylcholinesterase. 30°C was determined to be the optimal assay temperature. The highest inhibition was observed at 25°C using 53.10 mmol·L⁻¹ of the inhibitor. As the temperature increases, the inhibition decreases. Based on the Lineweaver-Burk diagram, we gain insight into the type of inhibition exhibited by the synthesized compound. The intercept on the ordinate remains unchanged; the slope of the line increases, and the intercept on the abscissa decreases, indicating that it is a competitive inhibition. Considering the results obtained by spectrophotometric analysis, it was concluded that the enzyme acetylcholinesterase follows the Michaelis-Menten model. It has been proven that the synthesized compound exhibits inhibitory properties on the activity of acetylcholinesterase.

Open-source RISC-V CPU architectures provide FPGA developers with fine-grained control over resource utilization and performance. This work presents a case study in throughput maximization and PPA (power, performance, area) optimization for a minimal RISC-V core on FPGA, with an emphasis on structured SystemVerilog design practices. We propose a short, single-cycle pipeline architecture targeting resource-constrained deployments and systematically compare its PPA characteristics against similar performance-class implementations. FPGA-specific optimizations, including tailored Register File and ALU configurations, are employed to improve critical path timing and overall throughput. The resulting design, eduBOS5, achieves a 2× increase in DMIPS/MHz while reducing LUT utilization by 24% compared to PicoRV32 on the Gowin LittleBee FPGA. PPA metric scaling over different FPGAs was addressed by porting the design to Xilinx and Lattice devices.

Immune checkpoint inhibition (ICI) has revolutionised cancer care, but many patients do not mount anti-tumor activity and most develop autoimmune toxicity. Mechanisms and risk factors underlying ICI response and immune related adverse events (irAEs) are incompletely understood. Thus, patient stratification and targeted irAE treatments are significant unmet clinical needs. Here, we use high-throughput spectral cytometry with machine-learning based analytics to characterise longitudinal immune dynamics under ICI. 706 cryopreserved PBMC samples from 137 patients consented to the EXACT study (NCT05331066) were utilised. All patients received standard of care adjuvant or advanced ICI for skin or renal cancer. Best overall response was annotated per RECIST 1.1(Responders: CR, PR, SD > 6 months). Patients on adjuvant ICI were designated as no-relapse at > 6 months from ICI initiation. irAEs were graded per CTCAE v5 and grade ≥3 considered severe. PBMCs were stained with 3 antibody panels comprising 114 markers. Data was acquired on a Sony ID7000 spectral analyser. Systems-level characterisation of 23,906 discrete PBMC subsets per sample was performed using IMU Biosciences’ proprietary machine learning platform. Following data QC, feature selection was refined through titration, variance, and correlation filtering. Predictive PBMC signatures were derived at baseline(BL) and C2 using univariate feature selection with bootstrapping followed by stepwise logistic regression, then validated through 100 iterations of 80:20 cross validation. PBMC types associated with irAE onset(Dev), increasing severity(Inc), and resolution(Res) compared to non-irAE on treatment controls were determined (t-test in a linear mixed effects model). Using these cell types, we then repurposed the Slingshot pseudotime method to derive patient trajectories from BL to Dev, and progression to Inc and/or Res. Benefit(responder/no-relapse) prediction achieved AUCs (mean ± SD) of 0.814±0.11 (BL), and 0.85±0.10 (C2). Severe irAE prediction achieved AUCs of 0.84±0.08 (BL), and 0.82±0.13 (C2). Dev and Inc samples of severe irAEs showed significant enrichment of activated non-classical monocytes, CD4 T, CD8 T, gd T, and NK cells. Dev of non-severe irAEs was indistinguishable from controls. In pseudotime, we found a bifurcating trajectory from BL to severe Dev vs. non-severe Dev. A further bifurcating trajectory distinguished progression from BL to on-treatment, then Dev vs. BL to Dev, then Inc. Res represented a return towards on-treatment controls in both lineages. Here, we used high-content PBMC profiling to generate immune signatures predictive of ICI outcome with compelling accuracy. We additionally gain mechanistic insights into irAE development and progression to severity. Our findings highlight the transformative potential of machine learning-powered immune profiling to identify predictors and drivers of benefit and toxicity outcomes under ICI with clear implications for patient stratification and irAE management. Max Emmerich, Duncan McKenzie, Carla Castignani, Jack Bibby, Jennie Yang, Marija Miletic, Laura Marandino, Zayd Tippu, Jonathan Lim, Taja Barber, Stephanie Hepworth, Paul Rouse, Lilian Williams, Kim Edmonds, Justine Korteweg, Serena Vanzan, James Larkin, Tom Hayday, Adam Laing, Samra Turajlic. Comprehensive blood profiling for immunotherapy outcome prediction and longitudinal immune trajectory characterisation [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr A002.

Background The increasing global prevalence of mental disorders as well as a persistent stigma make mental disorders a public health priority. The aim of this study was to provide a comprehensive overview of psychotropic drugs utilization from 2006 to 2021 in the Republic of Serbia, examining both pre pandemic and pandemic-related changes. Methods To conduct this descriptive study, publicly available data on psychotropic drugs were retrieved from the official website of the Agency for Medicines and Medical Devices of Serbia (ALIMS). The linear and joinpoint regression were used in data analysis. Results A total of 54 psychotropic drugs use was analyzed from 2006 to 2021. There was an increase in the consumption of antidepressants, atypical antipsychotics, anxiolytics, sedatives, hypnotics, anti-dementia drugs and gabapentinoid-based drugs. The increase in the consumption of the psychotropic drugs was linear, with no differences between the pre-COVID-19 period and the COVID-19 pandemic. Contrary, a significant decrease in use was observed for some antidepressants (maprotiline, moclobemide, mianserin), antipsychotics (chlorpromazine, fluphenazine), psychostimulants and nootropic drugs (piracetam), anxiolytics (diazepam, prazepam), sedatives and hypnotics (midazolam). Conclusion The COVID-19 pandemic did not contribute to change in consumption of psychotropic drugs in Serbia. Still, the use of antidepressants, atypical antipsychotics, anxiolytics, sedatives, hypnotics, anti-dementia drugs and gabapentinoids increased from 2006 to 2021.

Obesity, a global health concern defined by excessive adiposity and persistent metabolic imbalance, has far-reaching implications that extend beyond standard metabolic and cardiovascular comorbidities. While the association between obesity and reproductive dysfunction is well-established, the precise molecular mechanisms underlying these associations remain incompletely understood, particularly as regards the distinction between obesity-specific effects and those mediated by dietary components or metabolic syndrome. The present review integrates currently available knowledge on the mechanisms through which obesity impairs reproductive function in both sexes, from gametogenesis to postnatal development. In males, obesity drives testicular inflammation, disrupts spermatogenesis, impairs sperm motility and DNA integrity, and alters key signaling pathways, with oxidative stress and metabolic endotoxemia as central mediators. In females, obesity induces ovarian dysfunction, alters steroidogenesis, compromises oocyte quality and disrupts follicular environments, leading to reduced fertility and adverse pregnancy outcomes. However, the relative contribution of obesity-induced inflammation vs. direct lipotoxic effects remains poorly characterized in both sexes. The present review further examines the impact of parental obesity on fertilization capacity, placental function and in utero development, highlighting sex-specific and intergenerational effects mediated by mitochondrial dysfunction and epigenetic modifications. Notably, maternal obesity impairs placental and fetal organ development, increases the risk of metabolic and reproductive disorders in offspring, and alters key developmental signaling pathways. While some studies suggest that lifestyle interventions and antioxidant therapies may partially reverse obesity-induced reproductive impairments, significant gaps remain in understanding the precise molecular mechanisms and potential for therapeutic rescue. By synthesizing findings from animal models and human studies, the present review highlights the pivotal role of oxidative stress as a mechanistic link between obesity and reproductive dysfunction. It emphasizes the need for further research to inform clinical strategies aimed at mitigating these adverse outcomes.

The Central/Eastern Europe (CEE) Quality of Care Centres (QCC) Survey evaluated the implementation of guideline‐directed medical therapies (GDMT) and device use at discharge after heart failure (HF) hospitalization in CEE, where GDMT underutilization remains a concern.

Hepatozoon spp. are common pathogens in dogs and other Carnivora in many parts of the world, especially in the tropics. There is considerable taxonomic debate concerning the Hepatozoon species infecting Carnivora. Morphological descriptions of several Hepatozoon species are inadequate and their validity is questionable. Additionally, different terminology has been used for the description of life cycle stages. Here, we provide a comprehensive review of the Hepatozoon species in the Carnivora, using a uniform terminology. Worldwide prevalence of clinical and subclinical Hepatozoon infections for the past century is tabulated and critically evaluated. We also review the epizootiology, clinical signs, diagnosis, and treatment of hepatozoonosis in the Carnivora. The morphology and life cycles of seven valid species with known merogonic stages (Hepatozoon americanum, H. canis, H. felis, H. martis, H. rufi, H. silvestris, H. ursi) are summarized in a table using standard terminology. Additional information on H. apri, H. martis, and H. silvestris life cycle stages is provided. Information lacking for H. procyonis, H. luiperdjie and H. ingwe is discussed. The relevance of H. mustelis, H. banethi and H. ewingi is discussed and they are considered as invalid species. For the benefit of future researchers, worldwide reports of prevalence, clinical disease, diagnosis, and treatment of Hepatozoon infections in domestic and wild Carnivora for the past century are summarized in tables alphabetically and chronologically for each country. Co-infections of H. canis, H. americanum, H. felis, and H. silvestris are summarized and discussed. The role of Hepatozoon infections causing clinical illness in wild Carnivora is discussed, particularly for red foxes, coyotes, and mustelids.

Digital transformation (DT) has become one of the most significant trends in higher education institutions (HEIs) in both EU and non-EU countries. Using Information and Communication Technologies (ICTs) to reinvent higher education is contingent upon several factors, including an institution’s development stage regarding the application and strategic integration of ICTs across its key activities and processes. In the extant literature, multiple frameworks of ICT development (maturity) paths have been developed. However, there is a lack of empirical studies on how well those models predict the DT success, and which of their dimensions are most relevant. In this paper, we use a research instrument, adapted from the HigherDecision research project, to capture the subjective assessments of academics and students at three public higher education institutions in Bosnia and Herzegovina and Croatia. Using seven dimensions of the DT construct, prescribed by the HigherDecision framework, we examine their contribution to the subjectively evaluated success of each HEI’s DT initiative and identify the most impactful dimension(s). Our results show that the digital infrastructure and academic teaching and learning are perceived as critical drivers of DT in the academic sector. Provided that the University of Mostar, as a mid-sized public university located in Bosnia and Herzegovina, currently represents one of the DT leaders in the Western Balkans (WB) region, we discuss implications for scaling its good practices in smaller HEIs across the region.

The combination of electrochemical, surface, and spectroscopic techniques revealed that Pseudomonas aeruginosa biofilm accelerated corrosion of 304 stainless steel (SS), leading to localized pitting with depths up to 3.75 μm. Such damage did not occur on 304 SS treated with P. aeruginosa in the presence of Artemisia annua L. extract, or in sterile seawater. Introducing A. annua into biotic seawater hindered biofilm development and prevented the formation of porous Fe(III) corrosion products. Instead, a compact Fe3O4 layer formed, indicating a shift in corrosion product morphology and stability. ATR-FTIR analysis confirmed phenolic groups from the extract were adsorbed onto the steel interface, supporting the dual inhibitory role of A. annua through both surface modification and antimicrobial action. A. annua extract demonstrated a 74.4 ± 4.4% reduction in MIC-induced corrosion of 304 SS in marine conditions.

The widespread deployment of autonomous systems in safety-critical environments such as urban air mobility hinges on ensuring reliable, performant, and safe operation under varying environmental conditions. One such approach, value function-based safety filters, minimally modifies a nominal controller to ensure safety. Recent advances leverage offline learned value functions to scale these safety filters to high-dimensional systems. However, these methods assume detailed priors on all possible sources of model mismatch, in the form of disturbances in the environment -- information that is rarely available in real world settings. Even in well-mapped environments like urban canyons or industrial sites, drones encounter complex, spatially-varying disturbances arising from payload-drone interaction, turbulent airflow, and other environmental factors. We introduce SPACE2TIME, which enables safe and adaptive deployment of offline-learned safety filters under unknown, spatially-varying disturbances. The key idea is to reparameterize spatial variations in disturbance as temporal variations, enabling the use of precomputed value functions during online operation. We validate SPACE2TIME on a quadcopter through extensive simulations and hardware experiments, demonstrating significant improvement over baselines.

For decades the strong-field approximation (SFA) has been a theoretical backbone for describing the strong-field related phenomena such as above-threshold ionization (ATI) and high-order harmonic generation, even though it is well-known that it cannot accurately account for the long-range Coulomb interaction between the liberated electron and residual atomic ion. In this paper, we theoretically investigate high-order ATI. We use numerical solutions of the time-dependent Schrödinger equation (TDSE) and an improved SFA that includes electron rescattering. The analysis is performed for atomic anions and neutral atoms exposed to elliptically polarized laser fields. To validate the SFA and test its applicability, we compare both theoretical approaches for various targets and laser field parameters. We also show that the improved SFA in which the final electron plane wave is replaced by the Coulomb distorted plane wave leads to a better agreement with the results obtained using the solutions of the TDSE.