Warehouse Management Systems (WMS) employ advanced optimization techniques to enhance efficiency and streamline processes, from inventory positioning to order picking and packing. Among these, order picking represents the most time-consuming and resourceintensive operation. This paper presents a novel approach for monitoring worker efficiency in warehouses, focusing on estimating the complexity and time required for order picking. A variety of factors influence these estimates, including item location, quantity, dimensions and weight of items, picking sequence, and whether the location is in the stock or picking zone. Accurate estimation enables effective daily work planning, real-time monitoring of worker productivity, and overall warehouse efficiency. The proposed approach has been tested in real-world warehouse environments, demonstrating its practical applicability and potential to significantly improve worker performance, resource allocation, and operational management.

The Vehicle Routing Problem (VRP) is among the most complex optimization problems. Practical solutions require addressing real-world constraints such as time windows, vehicle capacities, delivery restrictions, driver working hours, and heterogeneous vehicle fleets. Solutions are often implemented in two stages: the first involves clustering customers, while the second focuses on incremental routing of these clusters to reduce complexity and improve solution control and explainability. However, the second stage heavily depends on the quality of the first, and clustering methods vary depending on client requirements. This paper explores various clustering methods and their impact on the final routing results, with a focus on real-world examples. The study includes diverse client scenarios, ranging from small-scale distribution systems with a limited number of customers to large-scale operations managing more than thousand of deliveries daily, covering both small and large orders. From fixed clustering and geographic partitioning to dynamic clustering algorithms and hybrid approaches, the advantages and limitations of each method are analyzed. The findings aim to provide actionable insights into selecting clustering methods that align with specific use cases, ensuring enhanced efficiency and adaptability in practical applications.

This paper addresses the challenge of analyzing CVs to parse their content into structured formats suitable for further processing and analysis. The proposed solution processes CVs provided as images or PDFs, handling diverse input formats, including free-form, multi-language, non-standardized layouts, and highly structured documents. Various heuristic approaches are employed for layout analysis, complemented by lightweight language models for extracting information. While multimodal models demonstrate strong performance, their cost and deployment complexity remain significant barriers. This study explores alternative methods optimized for computational efficiency, processing accuracy, and easier deployment. A comparative analysis of approaches is conducted on a standard dataset containing CVs from diverse clients and job roles, ranging from entry-level to specialized positions in various domains. The findings highlight the potential of these tailored, efficient solutions for scalable and secure CV parsing.

The single-diode model equation of a photovoltaic (PV) panel is a nonlinear equation that relates the current and voltage of the panel. During operation, the PV panel can be at any point along the $I-V$ characteristic defined by this equation. Catalogue data generally provide current and voltage values at three points: short-circuit, opencircuit, and maximum power under standard test conditions (STC) and nominal operating conditions (NOCT). For the purpose of PV plant modelling, the parameters $I_{\text{ph}}, I_{0}, V_{\mathrm{t}}$, $R_{\mathrm{s}}$, and $R_{\text{sh}}$ are typically estimated from catalogue data using various methods. The estimated parameters enable the determination of an $I-V$ curve that typically aligns well with measurements under real-world conditions, provided the panels meet their specified characteristics. However, due to usage or improper storage, panels may degrade, leading to changes in these parameters and deviations from those estimated from catalogue data. Existing methods for capturing $I-V$ curves often involve measuring currentvoltage pairs at numerous points, making them slow and hardware-intensive. This paper investigates the estimation of real panel parameters using current and voltage at just four points on the $I-V$ curve. The results show good agreement between the estimated parameters and the $I-V$ curves derived from them.

Future vehicles and infrastructure will rely on data from external entities such as other vehicles via V2X communication for safety-critical applications. Malicious manipulation of this data can lead to safety incidents. Earlier works proposed a trust assessment framework (TAF) to allow a vehicle or infrastructure node to assess whether it can trust the data it received. Using subjective logic, a TAF can calculate trust opinions for the trustworthiness of the data based on different types of evidence obtained from diverse trust sources. One particular challenge in trust assessment is the appropriate quantification of this evidence. In this paper, we introduce different quantification methods that transform evidence into appropriate subjective logic opinions. We suggest quantification methods for different types of evidence: security reports, misbehavior detection reports, intrusion detection system alerts, GNSS spoofing scores, and system integrity reports. Our evaluations in a smart traffic light system scenario show that the TAF detects attacks with an accuracy greater than 96% and intersection throughput increased by 42% while maintaining safety and security, when using our proposed quantification methods.

Vehicular communication via V2X networks increases road safety, but is vulnerable to data manipulation which can lead to serious incidents. Existing security systems, such as misbehavior detection systems, have limitations in detecting and mitigating such threats. To address these challenges, we have implemented a software prototype of a Trust Assessment Framework (TAF) that assesses the trustworthiness of received V2X data by integrating evidence from multiple trust sources. This interactive demonstration illustrates the quantification of trust for a smart traffic light system application. We demonstrate the impact of varying evidence coming from a misbehavior detection system and a security report generator on the trust assessment process. We also showcase internal processing steps within our TAF when receiving new evidence, up to and including the eventual decision making on the trustworthiness of the received V2X data.

Background/Objectives: This study aimed to investigate the skating determinants and differences between male and female bandy players in the spatiotemporal variables during acceleration and maximum sprint skating velocity. Methods: Seventy-four female bandy players (age: 18.9 ± 4.1 years; height: 1.67 ± 0.06 m; body mass: 63.2 ± 7.4 kg; training experience: 13.4 ± 3.9 yrs.; and 26 elite and 48 junior elite) and 111 male bandy players (age: 20.7 ± 5.0 years; height: 1.80 ± 0.05 m; body mass: 76.4 ± 8.4 kg; training experience: 13.8 ± 5.0 yrs.; and 47 elite and 66 junior elite players) performed linear sprint skating over 80 m. Split times were measured every ten metres by photocells to calculate velocities for each step and spatiotemporal skating variables (glide times and length, step length, and frequency) by IMUs attached to the skates. The first six steps (acceleration phase), the six steps at the highest velocity (maximal speed phase), and the average of all steps were used for analysing glide-by-glide spatiotemporal variables. Results: These revealed that male players exhibited higher acceleration and maximal skating velocity than female players. A higher acceleration in men was accompanied by shorter gliding time, longer step length, and higher step frequency. When skating at maximal speed, male players had a longer step length and gliding time and length. The sub-group analysis revealed that step frequency did not correlate with skating velocity, acceleration, or maximal speed phases. On the other hand, glide and step lengths significantly correlated with skating velocity in both phases (r ≥ 0.60). Conclusions: In general, for faster skating in bandy, it is generally better to prioritise glide and step length than stride frequency. Hence, players should be encouraged to stay low and have more knee flexion to enable a longer extension length and, therefore, a longer path and more horizontal direction of applied force to enhance their acceleration ability.

The increasing demand for lithium-ion batteries (LIBs) and their limited lifespan emphasize the urgent need for sustainable recycling strategies. This study investigates the application of tetrabutylphosphonium-based ionic liquids (ILs) as alternative leaching agents for recovering critical metals, Li(I), Co(II), Ni(II), and Mn(II), from spent NMC cathode materials. Initial screening experiments evaluated the leaching efficiencies of nine tetrabutylphosphonium-based ILs for Co(II), Ni(II), Mn(II), and Li(I), revealing distinct metal dissolution behaviors. Three ILs containing HSO4−, EDTA2−, and DTPA3− anions exhibited the highest leaching performance and were selected for further optimization. Key leaching parameters, including IL and acid concentrations, temperature, time, and solid-to-liquid ratio, were systematically adjusted, achieving leaching efficiencies exceeding 90%. Among the tested systems, [TBP][HSO4] enabled near-complete metal dissolution (~100%) even at room temperature. Furthermore, an aqueous biphasic system (ABS) was investigated utilizing [TBP][HSO4] in combination with ammonium sulfate, enabling the complete extraction of all metals into the salt-rich phase while leaving the IL phase metal-free and potentially suitable for reuse, indicating the feasibility of integrating leaching and extraction into a continuous, interconnected process. This approach represents a promising step forward in LIB recycling, highlighting the potential for sustainable and efficient integration of leaching and extraction within established hydrometallurgical frameworks.

We consider the problem of exact maximum likelihood estimation of potentially high‐order ( p>50 ) autoregressive models. We propose an extremely fast coordinate‐wise algorithm for fitting autoregressive models. This fast algorithm exploits several properties of the negative log‐likelihood when parameterised in terms of partial autocorrelations. We consider extensions to learning a single autoregressive model from multiple time series and to the more general case of regressions with autoregressive residuals. An implementation of the coordinate‐wise descent algorithm is shown to be the orders of magnitude faster than competing algorithms and appears to be the fastest known algorithm for maximum likelihood estimation of autoregressive models.

BACKGROUND Few questionnaires with established measurement properties can globally measure sleep in preschoolers and sleep-related family practices. OBJECTIVE To examine (1) concurrent validity of the SUNRISE parent questionnaire against an accelerometer for measuring sleep in preschoolers and (2) test-retest reliability of the questionnaire for sleep and related family practices. METHODS Sleep was measured using the questionnaire and Actigraph GTX3+ accelerometer using a decision-tree algorithm and the Sadeh algorithm in 1737 preschoolers (4.4±0.6years) from 30 countries. Concurrent validity was examined using correlation analysis (duration, timing, and quality), paired t test or the Wilcoxon signed-rank test, Bland-Altman plot (duration, timing), and analysis of sensitivity, specificity, and accuracy (variability). Test-retest variability was examined for sleep and family practice variables in a subsample of 163 participants (4.3±0.6years) from eight countries. RESULTS Questionnaire measures of sleep timing and duration were correlated with the accelerometer measures (r=0.43-0.75; p<.001). Although statistically significant mean differences were observed between questionnaire and accelerometer measures of sleep timing and duration variables, the difference in nighttime sleep duration had a small effect size (-14 min/d; Cohen's d=-0.2). The questionnaire was less able to provide adequate measurement for sleep quality and variability. High levels of reliability were observed for sleep (ICC=0.63-0.83; Kappa=0.53-0.62) and family practice (ICC=0.81-0.94; Kappa=0.73-0.86) variables. CONCLUSION The SUNRISE questionnaire appears reliable in assessing preschooler sleep characteristics and related family practices, particularly in disadvantaged settings. It could be used in global surveillance of nighttime sleep duration and in studies examining associations of sleep timing and duration with health indicators in preschoolers.

Background Non-ST-elevation myocardial infarction (NSTEMI) represents a prevalent form of acute coronary syndrome associated with substantial early risk of adverse outcomes. Inflammatory and metabolic disturbances are increasingly recognized as key contributors to the disease. Hematologic indices such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and pan-immune-inflammation value (PIV), along with the triglyceride-glucose index adjusted for BMI (TyG-BMI), have emerged as promising prognostic markers. However, their dynamic behavior in early NSTEMI remains insufficiently explored. Materials and methods This prospective study included 170 patients hospitalized for NSTEMI at the University Clinical Centre Tuzla between February 2022 and January 2023. Hematologic and metabolic indices were calculated at admission and repeated 24 hours later. Patients were followed for three months to document major adverse cardiovascular events (MACE), including cardiovascular death, reinfarction, and urgent revascularization. The median age was 67 years, and 60.6% of patients were male. Hypertension, hyperlipidemia, and diabetes mellitus were the most common comorbidities. Results Significant 24-hour reductions were observed in NLR, PLR, SII, SIRI, and PIV (all p < 0.01), while C-reactive protein (CRP) levels more than doubled (p < 0.001). Patients who developed MACE showed persistently elevated inflammatory indices and smaller declines in PIV and SIRI. Change in SIRI (ΔSIRI) demonstrated the strongest predictive value (AUC = 0.63), followed by SII and TyG-BMI. Notably, reduced resolution of PIV and persistently elevated TyG-BMI were significantly associated with adverse outcomes. Overall, MACE occurred in 51.2% of patients, including a 14.7% mortality rate. Conclusion Early changes in systemic inflammation and metabolic stress, particularly SIRI and TyG-BMI dynamics, offer valuable prognostic insight and may enhance early risk stratification in NSTEMI patients.

Background/Aim: Rheumatoid arthritis (RA) is an autoimmune inflammatory disease, characterized by the production of numerous pro-inflammatory cytokines, such as tumor necrosis factor α (TNF-α), interleukin-6 (IL-6), and interleukin-1β (IL-1β), which lead to pathophysiological changes in innate and acquired immunity. The existing evidence shows that pro-inflammatory cytokines in rheumatoid arthritis impact monoaminergic neurotransmission, neurotropic factors, and synaptic activity, which may lead to the development of depression. Materials and Methods: In our study, we explored the association between TNF-α and IL-6, disease activity, and the degree of depression in patients with RA. The association between TNF-α and IL-6 and the Beck and Hamilton depression scales was analyzed in a group of 116 RA patients with depression. We investigated the same correlation in 45 patients with primary depression who represented the control group. Results: A Spearman test showed that IL-6 levels had a positive association with the Beck and Hamilton scales (p < 0.05) and that TNF-α had a positive association with the Hamilton scale (p < 0.05). Also, the Hamilton depression scale was the more sensitive scale in the detection of depressive symptoms. Conclusions: Our study indicates that elevated values of pro-inflammatory cytokines are associated with the degree of depression in patients with RA. Future preclinical and clinical studies will contribute to a better understanding of the pathophysiological mechanism of depression in patients with RA and may serve as the basis for new treatment modalities. By detecting depression promptly, with the help of the HAM-D as the more sensitive scale, we could influence the future modality of treatment, and with a multidisciplinary approach, we could ensure an improvement in the quality of life of patients with RA.