One of the data science techniques is the data analysis, based primarily on various techniques and methods application in order to acquire, analyze, interpret and eventually visualize the data. The aim of these techniques is the translation of the raw data into the useful information. In this study, a comprehensive data analysis of accessible geological and hydrogeological settings of terrain in zones near “Zelenica” and “Gornje Polje” springs (alluvium of river Drina) near Loznica, has been performed. The lithologic-technical profiles of observation and of water-sampling objects have been analysed, and results, obtained by geophysical methods, subsequently interpreted. Besides, the grain size analysis test, aquifer tests, precipitations monitoring and water-level of Drina river monitoring at specified precipitation-/water-gauge were performed too. Obtained results are used as a starting point for creating the hydrogeological model (and hydrodynamic one afterwards) of the research area. The main tools used were Microsoft Excel software (for data preparation and analyzing) and Modflow code (for hydrogeological and hydrodynamic models creation).

The accelerated transformation of the urban landscape of Sarajevo Canton increasingly becomes a space of contention among various interest groups - citizens, planners, investors, heritage disciplines, and local authorities. Establishing a model that involves respecting the interests of each of these groups or increasing the level of inclusivity in the decision-making process will lead to humane and sustainable solutions for the future use and visual shaping of public spaces. Unlike other design disciplines, the specificity of designing public spaces lies in the imperative of participation, mediation, and balancing the interests of multiple actors, ranging from the public and private sectors to planners, designers, researchers, and citizens. Local governments in the municipalities of Sarajevo Canton and other Bosnian Herzegovinian (B&H) cities have still not adopted participatory approach to urban planning. This paper explains research project that tests but also proposes a methodological approach in the public space planning strategy (with emphasis on integrating culture and shift of educational approach) as a critical response to the mismatch between the existing institutional approach of "top-down" planning with the technological and social dynamics of the digital age, as well as the real needs of the local community. This approach promises a more inclusive, sustainable, and community-driven future for public space planning. Case study: Cultural district Sarajevo (the space between the future Ars Aevi Museum, the Historical Museum of B&H, and the National Museum of B&H) and "urban voids" of Grbavica1.

In this study, the use of terpene‐based hydrophobic deep eutectic solvents (HDESs) in the preparation of polymeric membrane ion‐selective electrodes is presented. HDES obtained from terpenes (menthol and thymol) and octanoic acid are used as a new component of polymeric membrane of potentiometric sensors sensitive to lead ions. Electrodes containing different amounts of HDES in the membrane (from 1 to 12 % wt./wt.) are prepared, and potentiometric measurements are carried out for these electrodes to determine the detection limit, the slope of the characteristic, and the response linear range. Based on the analysis of electrode performance, it is found that the optimum concentration of HDES in the membrane is 5 wt%. For such membranes, a more detailed study is carried out using a solid contact sensor. Selectivity toward interfering species as well as potential stability and reversibility, optimum pH range, effect of light, and presence of gases in the sample solution are investigated for such sensors. The obtained measurement results indicate that the tested sensor containing HDES in the membrane has good analytical parameters, and excellent selectivity (log K ≤ −4.4). It has been successfully used to determine lead in real environmental water samples after a brief pretreatment with XAD‐7 resin.

The recent hardware-accelerated microscaling 4-bit floating-point formats such as MXFP4 and NVFP4, supported on NVIDIA and AMD GPUs, promise to revolutionize large language model (LLM) inference. Yet, their practical benefits remain unproven. We present the first comprehensive study of MXFP4 and NVFP4 for post-training quantization, revealing gaps between their promise and real-world performance. Our analysis shows that state-of-the-art methods struggle with FP4, due to two key issues: (1) NVFP4's small group size provably neutralizes traditional outlier mitigation techniques; (2) MXFP4's power-of-two scale quantization severely degrades accuracy due to high induced error. To bridge this gap, we introduce Micro-Rotated-GPTQ (MR-GPTQ), a variant of the classic GPTQ quantization algorithm that tailors the quantization process to FP4's unique properties, by using block-wise Hadamard transforms and format-specific optimizations. We support our proposal with a set of high-performance GPU kernels that enable the MR-GPTQ format with negligible overhead, by rotation fusion into the weights, and fast online computation of the activations. This leads to speedups vs. FP16 of up to 3.6x layer-wise, and 2.2x end-to-end on NVIDIA B200, and of 6x layer-wise and 4x end-to-end on RTX5090. Our extensive empirical evaluation demonstrates that MR-GPTQ matches or outperforms state-of-the-art accuracy, significantly boosting MXFP4, to the point where it can near the accuracy that of NVFP4. We conclude that, while FP4 is not an automatic upgrade over INT4, format-specialized methods like MR-GPTQ can unlock a new frontier of accuracy-performance trade-offs.

This study explores the impact of female leadership on Environmental, Social, and Governance (ESG) performance in small and medium-sized enterprises (SMEs) in Bosnia and Herzegovina, a transitional economy. While prior research shows that gender-diverse boards improve corporate social responsibility (CSR) and ESG outcomes, most studies focus on large firms in developed markets. This study fills that gap by analyzing data from 131 SMEs that applied to the 2024 SDG Business Pioneers Award. Using descriptive statistics and correlation analysis, the research examines how women’s representation in management affects ESG priorities. Findings indicate a positive association between higher female participation and improved outcomes in ethics, innovation, productivity, and inclusivity. Companies with at least one-third women in leadership show stronger commitment to balanced and sustainable governance. In contrast, environmental practices are widely adopted across firms but appear less dependent on leadership composition. The study provides the first empirical evidence from Bosnia and Herzegovina on the gender–ESG link. It contributes to global debates on diversity and sustainability while offering practical insights for policymakers and managers. Promoting gender-inclusive leadership can enhance competitiveness, resilience, and alignment with international sustainability frameworks.

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.

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.

AIMS 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. METHODS AND RESULTS Between March 2024 and January 2025, 2251 patients (mean age 70.0 years, 60.4% male) were enrolled at discharge from 21 centres across 12 CEE countries. The patient population included HF with reduced ejection fraction (HFrEF) (55.5%), HF with mildly reduced ejection fraction (15.3%) and HF with preserved ejection fraction (27.9%). In the total population, from admission to discharge there was a increase in the use of angiotensin receptor-neprilysin inhibitor (ARNI) (17.1% to 34.3%), beta-blockers (69.4% to 92.4%), mineralocorticoid receptor antagonists (MRA) (44.0% to 82.1%) and sodium-glucose co-transporter 2 inhibitors (SGLT2i) (30.8% to 79.9%), with a reduction in angiotensin-converting enzyme inhibitor (ACEI) use (all p < 0.05). Similar trends were observed across HF phenotypes, including HFrEF (increased use of ARNI, 26.3% to 55.1%, beta-blockers, 69.8% to 95.3%, MRA 49.5% to 89.0%, and SGLT2I 36.2% to 79.8%, and lower ACEI use, all p < 0.05). At discharge, 53.5% of patients received quadruple therapy (63.9% with HFrEF), while ≥50% target doses of titratable drugs were achieved in 18.8% (17.8% in HFrEF). Predictors of GDMT underuse included older age, lower education, living alone, non-ischaemic HF, higher ejection fraction, chronic kidney disease, hypotension, hyperkalaemia, prolonged hospitalization, and residual oedema. Among eligible HFrEF patients, 21.3% were discharged with, or referred for implantable cardioverter-defibrillator, and 17.4% for cardiac resynchronization therapy. CONCLUSIONS The CEE-QCC Survey highlights substantial in-hospital GDMT implementation and up-titration, though device use remains limited. Targeted strategies are needed to enhance guideline implementation and ensure optimal HF care across the CEE region.

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.

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.