The deep energy method (DEM), a type of physics-informed neural network, is evolving as an alternative to finite element analysis. It employs the principle of minimum potential energy to predict an object’s behavior under various boundary conditions. However, the model’s accuracy is contingent upon choosing the appropriate architecture for the model, which can be challenging due to the high interactions between hyperparameters, large search space, difficulty in identifying objective functions, and non-convex relationships with the objective functions. To improve DEM’s accuracy, we first introduce random Fourier feature (RFF) mapping. RFF mapping helps during the model’s training by reducing bias toward high frequencies. The effects of six hyperparameters are then studied under static compression, tension, and bending loads in planar linear elasticity. Based on this study, a systematic automated hyperparameter optimization approach is proposed. Due to the high interaction between hyperparameters and the non-convex nature of the optimization problem, Bayesian optimization algorithms are used. The models trained using optimized hyperparameters and having Fourier feature mapping can accurately predict deflections compared to finite element analysis. Additionally, the deflections obtained for tension and compression load cases are more sensitive to variations in hyperparameters than bending.

This work introduces a new perspective for physical media sharing in multiuser communication systems by proposing a novel scheme that enables the recovery of the content of the transmitted message whenever collisions happen. An alarm monitoring system is taken as a merely illustrative toy-model, but indeed the framework is generally applicable. In this scenario, the alarm system is designed to first decide whether a predetermined event has happened over a certain period; if such an event has been identified, the decision node also needs to correctly classify from which sensor the message comes. Simulations corroborate the effectiveness of the proposed method in terms of the event transmission efficiency, when compared with variations of conventional methods like TDMA and slotted ALOHA.

This paper presents a Fuzzy Inference System (FIS) designed to comprehensively assess challenges, risks, and threats. In the realm of security and defense, defining these elements is inherently uncertain and complex. The paper addresses this challenge by integrating fuzzy logic into the model. As a pivotal instrument for decision-making, the model not only facilitates the precise identification of challenges, risks, and threats but also provides vital support for the strategic and doctrinal document development process. The methodology proves instrumental in reconciling divergent perspectives, aligning theoretical intricacies with practical applications. By effectively capturing the nuanced interplay between variables, the model offers a dynamic framework that enhances the accuracy and efficiency of security-related decision-making.

Purpose This study aims to investigate the relationship between age and entrepreneurial and intrapreneurial intentions considering the mediating role of individual entrepreneurial orientation (IEO) dimensions (risk-taking, innovativeness and proactiveness). Design/methodology/approach The data were collected from 782 individuals from Bosnia and Herzegovina’s working-age population using a cross-sectional survey design. Hypotheses were tested via structural equation modeling. Findings Younger individuals have significantly higher intentions for entrepreneurship and intrapreneurship than older individuals. The mediating role of IEO was partially supported in the case of the relationship between age and entrepreneurial intention as well as age and intrapreneurial intention. While risk-taking and innovativeness partially mediate the relationship, proactiveness does not. Originality/value This study takes a comprehensive approach when examining the relationship between age and entrepreneurial/intrapreneurial intentions in a developing economy while considering the indirect effects of IEO dimensions.

The role of the tumor microenvironment (TME) in immuno‐oncology has driven demand for technologies that deliver in situ, or spatial, molecular information. Compartmentalized heterogeneity that traditional methods miss is becoming key to predicting both acquired drug resistance to targeted therapies and patient response to immunotherapy. Here, we describe a novel method for assay‐agnostic spatial profiling and demonstrate its ability to detect immune microenvironment signatures in breast cancer patients that are unresolved by the immunohistochemical (IHC) assessment of programmed cell death ligand‐1 (PD‐L1) on immune cells, which represents the only FDA microenvironment‐based companion diagnostic test that has been approved for triple‐negative breast cancer (TNBC). Two distinct physiological states were found that are uncorrelated to tumor mutational burden (TMB), microsatellite instability (MSI), PD‐L1 expression, and intrinsic cancer subtypes.

Colorectal cancer (CRC) is the third leading cause of mortality worldwide. The Food and Drug Administration recently designated pembrolizumab, an immune checkpoint inhibitor (ICI) against a programmed death-1 receptor, as a breakthrough drug for the treatment of patients with mCRC whose tumors have deficient mismatch-repair gene expression (as evidenced by microsatellite instability-high) and patients with solid tumors with a high tumor mutational burden with ≥10 mutations/megabase. We present a patient with metastatic CRC having renal and adrenal gland metastases. Comprehensive molecular profiling performed on a site of metastatic CRC in the kidney revealed multiple genomic alterations characteristic of CRC and rare chromosome 9p24.1 amplification, resulting in a co-amplification of the PDL1, PDL2, and JAK2 genes. Although this genomic alteration may predict the response to ICI, the lack of pembrolizumab prevented the patient from receiving targeted treatment and succumbing to the disease.

The paper aims to answer the question if and under which conditions a talaq performed in an Islamic state may be recognised in European states. The authors provide an analysis of various forms of talaq performed in different Islamic states and reach conclusions on the effects that may be recognised in Europe, with an outlook towards a possible uniform approach. The recognition of talaqs in England and Wales, Germany and Bosnia and Herzegovina are used as examples for different solutions to similar problems before European courts. The EU legislator has not adopted a uniform approach to the application and recognition of talaqs in the EU. The CJEU got it wrong in Sahyouni II and missed the opportunity to contribute to a uniform EU policy but its subsequent decision in TB opens the door for the CJEU to overturn Sahyouni II if another case concerning a non-EU talaq divorce comes before them. The Hague Divorce Convention of 1970 is an international instrument that provides for appropriate solutions. Ratification by more states in which a talaq is a legally effective form of divorce and by more European states would provide the much-needed security for families moving from Islamic states to Europe.

This study aimed to assess the biodiversity of fungi colonizing the fine roots (diameter up to 2 mm) of 3-year-old silver fir saplings from areas of Międzylesie Forest District in Poland. It was hypothesized that quantitatively and qualitatively, mycorrhizal fungi would be the dominant fungi in root communities of silver fir. DNA extraction was performed using Plant Genomic DNA purification. The internal transcribed spacer1 (ITS1) rDNA region was amplified using specific primers, and the amplicons were purified and sequenced using sequencing by synthesis (SBS) Illumina technology. The obtained sequences were compared with reference sequences in the UNITE database (https://unite.ut.ee/) using the basic local alignment search tool (BLAST) algorithm to facilitate species identification. A total of 307,511 OTUs was obtained from each sample. There were 246,477 OTUs (80.15%) of fungi known from cultures. The genera Tuber spp. (7.51%) and Acephala spp. (3.23%) accounted for the largest share of the fungal communities on the fine roots of fir trees. Hence our results indicate the dominance of mycorrhizal fungi in these communities and reflect the excellent quality of the saplings that were assessed. Pathogenic fungi constituted a much smaller share of the fungal communities.

The classical (vertex) metric dimension of a graph G is defined as the cardinality of a smallest set S in V (G) such that any two vertices x and y from G have different distances to least one vertex from S: The k-metric dimension is a generalization of that notion where it is required that any pair of vertices has different distances to at least k vertices from S: In this paper, we introduce the weak k-metric dimension of a graph G; which is defined as the cardinality of a smallest set of vertices S such that the sum of the distance differences from any pair of vertices to all vertices of S is at least k: This dimension is"stronger"than the classical metric dimension, yet"weaker"than k-metric dimension, and it can be formulated as an ILP problem. The maximum k for which the weak k-metric dimension is defined is denoted by kappa(G). We first prove several properties of the weak k-metric dimension regarding the presence of true or false twin vertices in a graph. Using those properties, the kappa(G) is found for some basic graph classes, such as paths, stars, cycles, and complete (bipartite) graphs. We also find kappa(G) for trees and grid graphs using the observation that the distance difference increases by the increase of the cardinality of a set S. For all these graph classes we further establish the exact value of the weak k-metric dimension for all k<= kappa(G).

Abstract Introduction: With advancements in sensor and communication technologies, sleep monitoring is moving out of specialized clinics and into everyday homes. Extracting sleep-related data using far less complicated tools and procedures is possible than polysomnography. Respiratory and cardiovascular data are extracted from the signals such as the electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) to identify the aberrant respiratory events of apnea/hypopnea as well as to estimate sleep parameters. However, due to the different sleeping positions, such systems lack accuracy and/or complicated sensor network topology. In this work, we proposed an optimal topology of forcesensitive resistor (FSR) sensors to simplify the system design by identifying the region of interest for estimating cardiorespiratory parameters with minimal error. The sensors are deployed under the mattress and located on the bed frame. Methods: We proposed a low-cost, unobtrusive, non-invasive, and reliable solution with robust signal processing algorithms for cardiorespiratory measurements and automatic signal validation based on signal quality. The solution is established based on a multi-physical layer (MPL) and sensor interfaces coping with the embedded system’s specifications, and signal processing is performed onboard with two independent and simultaneous pipelines for heart rate and respiratory rate using discrete wavelet transform (DWT) and bandpass filter, respectively. Results: We identified the three most contributing FSR sensors forming a triangle shape covering the left upper side of the subject (in the supine position) as the region of interest. We reduced the mean absolute error (MAE) to as low as 3.94 and 2.35 for heart rate and respiratory rate. Conclusions: The approach with the topology of triangle-shaped performs appropriately in estimating the cardiorespiratory parameters in all four regular sleeping positions, i.e. supine, prone, left lateral, and right lateral.

This paper presents the energy and CO2 saving potential of existing district heating energy system. Analysed system fully rely on fuel oil, with significant energy losses, increased fuel consumption and CO2 emission resulting from outdated and oversized system and fuel with high greenhouse emission factor. Heat generation and thermal energy distribution systems efficiency are assessed, showing that overall system efficiency is 48.5%. System environmental impact is shown via absolute CO2 and specific CO2 emission per heated surface area and useful energy. The study proposes retrofit measures to improve system efficiency, reduce fuel consumption, introduce low-emission fuels, and lower the system’s environmental impact. The study finds that the implementation of these measures could reduce system energy consumption by 42.7%, absolute CO2 emissions by 52%, and specific CO2 indicators as well, highlighting the potential for reducing the environmental impact of district heating systems while meeting users energy needs.

Indoor air quality monitoring is vital for ensuring high-quality healthcare services and minimizing the presence of harmful pollutants and environmental factors that could potentially impact on the well-being of individuals in hospitals. To address this need, the authors developed the transparent robot (TR): an integrated sensorized platform designed for indoor environmental sensing. This Internet-of-Things (IoT)-based platform serves as a modular system that can be installed on robotic platforms, enabling both static and dynamic monitoring of indoor spaces. In the context of a smart hospital, the TR can be integrated with the hospital's software architecture. It collaborates to generate a secure dataset of monitored data and can promptly notify healthcare professionals about any parameters that fall outside acceptable level. By utilizing this IoT-based device's features, hospitals can ensure a safer environment. The system's effectiveness and usability were preliminary demonstrated, showcasing its potential for further development; for instance, by incorporating additional sensors and algorithms, the TR can provide a probabilistic estimation of the likelihood of certain conditions based on the sampled environmental parameters.