Real-time monitoring of safety-critical interior states remains an open problem across energy, environmental, and industrial systems where direct instrumentation is infeasible. Existing approaches based on governing equations, discrete state vectors, or fixed sensor locations fail to provide mesh-independent, field-level reconstructions at arbitrary interior coordinates under real-time constraints. Here we introduce neural operator-based virtual sensing as a general framework for recovering inaccessible interior fields from sparse, heterogeneous measurements and operating conditions, instantiated with MIMONet, a multi-input, multi-output neural operator that fuses heterogeneous inputs and decodes coupled physical fields through a shared latent representation. Across three engineering-grade evaluations of escalating complexity, from confined recirculating flows to pressurized water reactor subchannels and compact power-system heat exchangers, MIMONet achieves ≤5% relative reconstruction error, while generating full-field predictions with sub-millisecond inference on an NVIDIA H200, at least five orders of magnitude faster than traditional high-fidelity CFD simulations, with calibrated uncertainty and noise resilience demonstrated on the heat-exchanger case. We validate the framework on three independent real-world datasets spanning electrochemical energy, atmospheric science, and physical oceanography: current-density mapping in a hydrogen fuel cell, wind-speed profiling on instrumented meteorological towers, and North Atlantic ocean-state from a global ocean reanalysis. MIMONet improves over classical virtual-sensing baselines by 72–84%: 84% on fuel-cell internal-field recovery from 10% sensor coverage and 83% on hub-height wind-speed recovery from lower-level tower sensors. Most notably, it recovers sea-surface height, a field with zero direct sensors, from its learned coupling to observed temperature and salinity at 4.1 × lower error than the best classical predictor, a cross-modal field recovery beyond the reach of classical interpolation. These results establish operator-based virtual sensing as a practical route to real-time field observability in systems where the states that matter most cannot be directly measured.

Exercise is increasingly positioned as an adjunct to oncology care, yet its clinical benefits and proposed antitumour mechanisms are often discussed in parallel rather than as a unified biological model. This focused mini review integrates 20 unique full-text sources spanning meta-analyses, systematic and narrative reviews, consensus guidance, and translational studies. The evidence supports a multiscale framework in which muscle contraction generates acute neuroendocrine and myokine signals, improves tumour perfusion and oxygenation, alters metabolite competition within the tumour microenvironment, mobilises cytotoxic immune cells, and preserves host muscle and functional reserve. Clinical evidence is strongest for improvements in cardiorespiratory fitness, strength, fatigue, physical function, and quality of life, whereas direct tumour control and enhancement of immunotherapy remain supported mainly by preclinical and early human mechanistic data. Important tensions include the context-dependent biology of interleukin-6, the distinction between vascular normalisation and tumour-promoting angiogenesis, the uncertain optimal exercise dose, and the risk that excessive energy expenditure may be inappropriate in severe cachexia. We propose a precision exercise oncology model that matches modality and dose to tumour phenotype, treatment phase, symptoms, physiological reserve, and dynamic monitoring. Exercise should be treated as evidence-based supportive care with plausible disease-modifying potential, not as a substitute for anticancer therapy.

Background: Cancer remains one of the leading global public health challenges, with a growing number of patients living during and after oncological treatment. Physical activity has increasingly been recognized as an important non-pharmacological strategy in cancer prevention, treatment support, rehabilitation, and survivorship care. Objective: The aim of this review is to examine the impact of physical activity on cancer prevention, treatment-related outcomes, and quality of life among individuals diagnosed with cancer. Methods: A structured literature search was conducted in Web of Science, PubMed, and Scopus databases. The search included studies published in English and focused on physical activity, exercise interventions, cancer prevention, cancer treatment, cancer-related fatigue, survivorship, and quality of life. Systematic reviews, meta-analyses, randomized controlled trials, cohort studies, and clinical guidelines were considered. Results: Previous evidence suggests that regular physical activity is associated with a reduced risk of several cancer types, especially breast, colorectal, and endometrial cancers. During and after treatment, exercise interventions may improve physical functioning, reduce cancer-related fatigue, support mental health, and enhance health-related quality of life. Conclusion: Physical activity represents a safe and effective supportive strategy across the cancer continuum. Its integration into oncology care may contribute to prevention, better treatment tolerance, rehabilitation, and improved quality of life among cancer patients and survivors.

While conversational AI agents (CAs) have the potential to support drivers through hands-free and eyes-free interaction, their role in long-haul trucking remains unexplored. This paper investigates how long-haul truck drivers perceive CAs and identifies design considerations for their successful deployment. We conducted ride-along observations and semi-structured interviews with five long-haul truck drivers during regular driving operations. The results show that drivers today regulate verbal communication according to driving demands and expect communication partners to adapt to changes in workload and attention. Participants were most receptive to CAs supporting driving-related tasks, including traffic information, rerouting and route planning. Trust, perceived complexity, and discomfort with conversational interaction emerged as key barriers to adoption. We conclude that CAs may be best suited to supporting strategic and selected tactical tasks, while being less appropriate for operational tasks. Effective CA design must adapt to context and workload, support interruptions, and provide transparent and trustworthy support.

Current AI-based conversational agents (CAs) largely lack situational awareness, limiting their ability to understand driving context, driver state, and immediate driver needs. Consequently, the use of CA may increase drivers’ cognitive load and impair safety and user experience. This study investigates the role of situationally aware CA in long-haul trucks and how such CA should be designed to support drivers in an efficient way. The insights were gathered through a Wizard-of-Oz driving simulator study exploring two CA concepts. The results show that CA are well-suited for strategic tasks, could be useful for some tactical tasks depending on the cognitive load, and are generally inappropriate at the operational level, where they interfere with core driving actions. Effective CA design must prioritize driving as the primary task, adapt interaction timing to drivers’ cognitive load, and ensure transparency and predictability to build trust.

Annotated medical imaging data remain scarce, and labels are often weak and noisy: in mammography, an examination comprises several high-resolution views, yet the diagnostic outcome is recorded only at the breast level. Such problems are naturally cast as Multiple Instance Learning (MIL), where the model must infer instance-level structure from bag-level labels alone. Although contemporary foundation encoders supply strong general-purpose embeddings, augmenting MIL data in this representation space remains an open problem as established techniques act on one instance at a time and ignore the statistical dependencies binding a bag together. We address this with SetFlow, a generative model that learns the distribution of complete MIL bags directly in a frozen encoder’s embedding space. SetFlow couples flow-matching training with a Set Transformer-inspired backbone, making it invariant to instance ordering while modeling intra-bag relationships. Generation is conditioned jointly on class label and per-instance scale, yielding coherent, semantically faithful bags rather than isolated vectors. Evaluating on two large public mammography datasets and two encoders, we assess distributional fidelity, nearest-neighbor behavior, and downstream augmentation utility. We show that generated bags reproduce real-data statistics and improve classification in certain configuration, with performance gains varying on the amount of synthetic data. An architecture ablation confirms each design choice contributes to performance.

Coal remains an important component of the electricity system of Bosnia and Herzegovina, while the country is simultaneously undergoing a gradual energy transition toward lower-carbon technologies. This study evaluates the quality of coal from major mining areas in Tuzla Canton using laboratory results for composite samples from Kreka, Đurđevik and Banovići. The principal indicators were total moisture, ash, combustible matter and lower heating value (LHV). Eight mining areas were compared using five analytical samples per area, giving 40 observations for the primary statistical analysis. An additional 15 observations from five Banovići separation products were evaluated separately. Marked differences were identified among mining areas. Mean LHV ranged from 7,836 kJ/kg at PK Dubrave to 19,794 kJ/kg at Jama Omazići. Lignite from the Kreka area had substantially higher mean total moisture than the analysed brown coals, while several brown-coal areas were constrained by high ash content. Across the 40 primary observations, LHV showed a moderate negative correlation with total moisture (r = -0.43) and with ash (r = -0.37). A multiple linear model using moisture and ash explained approximately 98.4% of the observed LHV variation, although the small number of samples and the compositional nature of the variables require cautious interpretation. The findings demonstrate that coal reserves alone are insufficient for evaluating the future role of coal: fuel quality, preparation efficiency, plant efficiency, emissions intensity, economic performance and regulatory conditions must be considered together. The results support a transition strategy that combines improved coal characterisation and combustion efficiency in the medium term with accelerated deployment of renewable generation, storage, grid modernisation and just-transition measures.

This paper evaluates the energy performance and lifecycle environmental impact of three retrofit pathways—conventional insulation, an extensive green roof, and rooftop photovoltaics—for two lamella-type residential buildings (three-story, five-story; over 40 years old) in Blocks 45 and 70, Novi Beograd, Serbia. The study builds on an architectural concept treating the flat roof as an active environmental surface. Energy demand was quantified via calibrated dynamic simulation for a baseline and three scenarios—insulation (S1), insulation with an extensive green roof (S2), and insulation with rooftop photovoltaics (S3)—with environmental performance assessed using cradle-to-use LCA (1 m2 heated floor area, 50-year period) covering global warming potential, cumulative energy demand, and Environmental Footprint 3.1 indicators. Insulation and the green roof achieve nearly identical reductions in heating demand (27–31%) and lifecycle GWP/CED (10–13%), the green roof performing marginally better while also offering evapotranspirative cooling and urban co-benefits. Photovoltaics deliver the largest lifecycle GWP reduction (26% three-story, 15% five-story) and cut acidification by up to 36%, but increase mineral resource depletion, water and land use. Benefits of both roof-level measures scale with roof-to-floor-area ratio, favoring lower-rise buildings, supporting prioritization of green roof and photovoltaic retrofit across the district lamella stock.

Aim: To compare changes in thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4), and thyrotropin receptor antibodies (TRAb) after antithyroid drugs, radioactive iodine, or total thyroidectomy in patients with Graves' disease. Methods: This retrospective comparative cohort study included 60 adults treated at the University Clinical Centre Tuzla between 2021 and 2025, with 20 patients in each treatment group. Laboratory values obtained before treatment and at the first documented follow-up were analysed. The primary analysis compared change scores among treatment groups; within-group changes and correlations were secondary analyses. Results: Baseline FT4 and TRAb differed among the treatment groups (p = 0.022 and p = 0.005, respectively). After thyroidectomy, FT3 and TRAb changed significantly (p = 0.004 and p < 0.001), whereas TSH and FT3 changed significantly within the radioactive iodine group (p = 0.014 and p = 0.027). No significant within-group changes were observed after antithyroid drug treatment. Change scores did not differ significantly among the groups for TSH, FT3, FT4, or TRAb. Changes in FT3 and FT4 were positively correlated (ρ = 0.649; p < 0.001), whereas changes in TSH were negatively correlated with FT3 and FT4. Conclusion: Selected laboratory parameters changed within the thyroidectomy and radioactive iodine groups, but change scores did not differ significantly among the three treatment groups. These comparisons should be interpreted in view of baseline imbalance, small group sizes, non-consecutive sampling, and non-standardised follow-up.

The excessive use of vaccines, antibiotics, and other preventive therapeutic agents in conventional broiler production has resulted in the frequent occurrence of antibiotic residues in edible tissues, posing risks to both animal and human health. These residues contribute to the global problem of antimicrobial resistance, highlighting the need for alternative approaches to poultry disease prevention. This study aimed to evaluate whether a comprehensive preventive programme encompassing biosecurity, hygiene, and an antibiotic-free prophylaxis regimen could eliminate the need for prophylactic antibiotic therapy, thereby enabling the production of broiler meat free of antibiotic residues. This approach was contrasted with the conventional production system, in which less rigorous preventive practices necessitate the prophylactic use of broad-spectrum antibiotics, ultimately resulting in detectable residues within the muscle and liver tissues of conventionally reared chickens. Ross 308 broilers were reared under field conditions. in. The control group was reared using conventional prophylactic protocols, including broad-spectrum antibiotics, vitamins, bio-stimulants, mineral supplements, and acidifiers via drinking water. The experimental group received no antibiotics; and instead were treated with hydro-soluble probiotic preparations containing vitamin C, lactose, fructose, and baker’s yeast, combined with continuous water disinfection throughout the fattening period. Microbiological analyses showed the presence of antibiotic residues in the soft tissues of conventionally reared broilers, while no residues were detected in the experimental group. Production performance indicators—mortality rate, vitality, final body weight, and overall cost—were similar to or superior to those in the antibiotic-free group, confirming that broiler meat production without antibiotics is both feasible and economically viable.

Aim: To identify clinical and inflammatory factors associated with adequate haemoglobin control among patients receiving maintenance haemodialysis and erythropoiesis-stimulating agent (ESA) therapy. Methods: This analytical cross-sectional study included 100 adults receiving maintenance haemodialysis. Among 80 patients receiving ESA therapy, adequate haemoglobin control was defined as haemoglobin ≥ 100 g/L and inadequate control as haemoglobin < 100 g/L at the cross-sectional assessment; 20 patients not receiving ESA therapy were described separately. Clinical characteristics and laboratory measurements, including C-reactive protein (CRP), were recorded. The erythropoietin resistance index (ERI) was calculated from weekly ESA dose, body weight, and haemoglobin concentration. Results: Eighty patients (80.0%) received ESA therapy, while 20 (20.0%) did not. Among ESA-treated patients, longer haemodialysis vintage was associated with higher odds of adequate haemoglobin control (OR = 1.02, 95% CI 1.01–1.04), whereas higher CRP was associated with lower odds (OR = 0.90, 95% CI 0.83–0.99). ERI was inversely associated with adequate control (OR = 0.004, 95% CI 0.001–0.066), although this finding is mathematically coupled to the haemoglobin-based outcome. The exploratory model was statistically significant (χ² = 45.4; p < 0.001). Conclusion: Longer haemodialysis vintage and lower CRP were associated with adequate haemoglobin control during ESA therapy. ERI findings require cautious interpretation because haemoglobin is included in the ERI denominator and was also used to define the outcome.