This paper explores the impact of performance expectancy (PE) and effort expectancy (EE) on the behavioral intention (BI) of managers to implement environmental, social, and governance (ESG) practices in small and medium-sized enterprises (SMEs) in Bosnia and Herzegovina. Us ing the Technology Acceptance Model (TAM), the study analyzes how these factors influence decision-makers’ engagement in sustainable business activities. A survey of 247 managers was conducted to assess their BI, PE, and EE for each ESG component. The collected data were ana lyzed using structural equation modeling (SEM) to construct a second-order latent model that measures the combined effects of these factors. The study fills a gap in the literature by examin ing sector-specific differences in the factors influencing ESG implementation. The results reveal that both PE and EE significantly affect an organization’s intention to adopt ESG practices, with varying impacts between the manufacturing and service industries. The findings highlight the different challenges and complexities each industry faces in implementing ESG practices. While focusing on PE and EE, it is acknowledged that other factors such as organizational culture, stakeholder pressure, and industry-specific dynamics also play roles in ESG adoption. The main contribution of this work is the use of a second-order latent construct within the TAM model for ESG practices, offering a unique perspective on understanding behavioral intention.

Introduction Renal angiomyolipomas (AMLs) are neoplasms that can rarely rupture, causing hemorrhagic shock as the most serious complication. This pathological condition, (referring to AML) is classified as a benign tumor arising from the proliferation of epithelioid cells, consisting of fat tissue, blood vessels, and smooth muscle. Wunderlich syndrome describes a spontaneous, nontraumatic bleeding into the subcapsular or perirenal space. Most individuals with renal AML exhibit no symptoms and are often diagnosed incidentally, however, some may experience life-threatening complications such as rupture, hemorrhage, and circumstantial hypovolemic shock. Case outline Description of the clinical presentation of AML with rupture in a female patient with a brief overview of other cases of AML in the literature. Female patient, 68 years old, admitted for examination due to sudden severe pain in the abdomen with propagation to the right lumbar region accompanied by nausea and fatigue. After a complete physical examination, an abdominal ultrasound, and a CT scan, surgery was performed during which the right kidney was removed alongside the hematoma and the kidney envelopes, which were sent for pathohistological analysis. The result of the histopathological analysis confirmed that it was AML. Conclusion AMLs are benign neoplasms with potentially serious complications. The most serious complication of AML is rupture, leading to retroperitoneal hemorrhage, with tumor size being a significant risk factor. Considering the clinical importance of this potential complication, it is important to establish a swift and accurate radiological diagnosis, with the aim of timely therapeutic intervention and reduction of potential additional complications.

Je li umjetna inteligencija, koja je danas pobudila sveopći znanstveni i javni interes, prilika koja se može pokazati korisnom za demokraciju ili je ona prijetnja koja donosi korjenite promjene i oko koje se trebamo duboko zabrinuti? S jedne strane, umjetna inteligencija može biti prilika za poboljšanje demokratskoga procesa u smislu jednostavnijega uključivanja u demokratsku raspravu i samim time u poboljšanju procesa kreiranja politika. S druge strane, rizici za demokraciju koje može generirati umjetna inteligencija mogu biti lažne informacije koje mogu izazvati različite društvene sukoba te kreirati mišljenja koja ne predstavljaju mišljenje javnosti. U oba slučaja, i kao prilika i kao prijetnja, umjetna inteligencija promijenit će mnoge aspekte demokracije, uglavnom na načine koje još ne možemo pojmiti. Ključne riječi: umjetna inteligencija; demokracija, izborni proces.

Timely and accurate defect detection is essential in the leather industry, as the quality of raw leather directly impacts both the usability and value of finished products. This paper provides a systematic overview of state-of-the-art solutions and proposes a novel approach for automated detection of leather surface defects using deep neural networks based on the Inception-V3 architecture. Five defect categories are introduced, focusing on their impact on leather quality. In addition, two deep neural network architectures were analyzed and implemented for defect detection and classification: a single-channel model and a multi-channel model with arbitration. The evaluation was carried out using a combination of a custom-developed dataset and publicly available datasets, assessed with standard performance metrics. Moreover, an image annotation tool was developed to facilitate precise defect labeling and the creation of variable-size datasets. Both models demonstrated promising results on the custom dataset, achieving accuracy rates exceeding 93%. The suggested methodology enhances the research domain of leather inspection automation by creating an openly accessible image dataset, performing a comparative analysis of detection models and creating software tools for data preparation. These contributions lay the foundation for further research in leather defect detection and potential industrial implementation.

Background: Legal certainty is a guiding principle in all European countries. One of the main tools for achieving legal certainty in Europe is the codification of law. In 2023, Saudi Arabia adopted its first codification of contractual and non-contractual obligations through the Civil Transactions Law (CTL), aiming to achieve greater legal certainty. This shift represents a major shift from a predominantly Shariah-based jurisdiction towards civil law. This research examines whether the enactment of the CTL has influenced the Saudi Commercial Court's interpretation of compensation claims. Methods: A mixed-methods approach was adopted to track citation trends over time and to examine case law documents to confirm the quantitative results. Qualitative empirical analysis, specifically document analysis, was utilised to identify and extract Shariah jurists’ opinions, providing depth to the statistical results. Quantitative empirical methods, including interrupted time series (ITS), were applied to assess whether the compensation provisions in the CTL led to significant shifts in compensation claims decisions. Overall, 2,913 cases decided before the enactment of the CTL and 61 decided under the CTL were analysed in this study. Results and conclusions: The pre-law analysis indicates that courts cited Shariah jurists or general legal principles to establish the liability for compensation. In contrast, post-law analysis suggests a discernible shift, with courts increasingly citing civil law provisions directly, notably Articles 120 and 720 of the CTL. This shift is supported by an increase in overall article citations within compensation judgments, rising from 36% to 62%, supported by the examination of cases decided based on these articles. These findings indicate that the enactment of the Civil Transactions Law has contributed to enhancing the legal certainty in Saudi commercial courts.

(BHS) Ovaj rad se fokusira na upotrebu mašinskog učenja i korištenje namjenskih baza podataka vještačke inteligencije u svrhu kreiranja rješenja zasnovanih na unaprijeđenom algoritmu za preventivno upravljanje rizicima i predikciju rizika u realnom vremenu. U radu se analiziraju postojeći standardi, njihovi nedostaci i moguća rješenja za unapređenje, kao i struktura i algoritamska osnova ovih sistema, te njihova integracija u postojeće sigurnosne arhitekture i platforme. Obuhvaćena je detekcija prijetnji na osnovu anomalija i analiza ustaljenog korisničkog ponašanja prema zadanim obrascima, procjena rizika i proaktivna detekcija napada. Pravovremena identifikacija i upravljanje rizicima postaju ključni faktori održivosti kompanija i sigurnosti poslovnih i informacionih sistema. Prediktivna analitika, zasnovana na vještačkoj inteligenciji, mašinskom učenju i analizi velikih skupova podataka, donosi transformacijske mogućnosti u oblastima poput industrije, finansija i zdravstva, koje su u savremenoj eri povezane sajber sigurnošću i predikcijom rizika, a koje pomažu donosiocima odluka da efikasnije upravljaju sistemima i zaštite ih. Integrativni pristup usklađivanju ovih tehnologija, posebno u kontekstu organizacione strukture i pravnog okvira, obuhvata pitanja pouzdanosti i transparentnosti modela, odgovornosti za automatizovane odluke, zaštite privatnosti i usklađenosti sa zakonodavstvom. Cilj rada je pružiti sveobuhvatan pregled tehnoloških i metodoloških inovacija u prediktivnoj zaštiti od sajber rizika, te identifikovati pravce budućeg razvoja sa posebnim fokusom na sigurnost, etiku i pouzdanost AI sistema. (ENG) This paper focuses on the use of machine learning and the use of dedicated AI databases to create solutions based on an improved algorithm for preventive risk management, and real-time risk prediction. The paper analyses the existing standard, its shortcomings and solutions for improvement, and the structure and algorithmic basis of these systems, as well as their integration into existing security architectures and platforms. The work includes the detection of threats based on anomalies and the analysis of established user behavior according to given patterns, risk assessment and proactive detection of attacks. Timely identification and management of risks are becoming key factors in corporate sustainability and security of business and information systems. Predictive analytics, based on artificial intelligence, machine learning and big data analytics, bring transformational opportunities in areas such as industry, finance, healthcare, which in the modern era are connected by cybersecurity and risk prediction that help decision makers to manage systems more efficiently and protect them. An integrative approach to harmonizing these technologies, especially considering the organizational structure and legal framework, includes issues of reliability and transparency of models, as well as accountability for automated decisions, privacy protection and compliance with legislation. The aim of the paper is to provide a comprehensive overview of technological and methodological innovations in predictive protection against cyber risks, and to identify directions for future development with a special focus on the security, ethics and reliability of AI systems.

Background: The basic model of a neural network is the physical structure of the human brain. The idea is simple: if a sufficiently powerful artificial network of synapses is created, the results of functioning similar to the human brain can be expected . Neural network or neurocomputer is one form of artificial intelligence system implementation, which represents a system consisting of a certain number of interconnected processors or nodes, or process elements that we call artificial neurons. Objective: The aim of this article was to draw attention to the necessity of taking about a detailed history of neural networks which are based on copying information processing in synaptic networks in neurons and synapses in the brain. Methods: The author searched scientific literature: the books and published papers deposited in index databases as source for description and explanation the architecture of a neural networks and its represents a specific connection of neurons into a single unit. Resuls and Discussion: The structure of a neural network differs in the number of layers. The first layer is called the input layer, and the last is the output layer, while the layers in between are called hidden layers. Most often, there are three of them. The first layer, or the input layer, is the only layer that receives data from the external environment, the next (hidden) layer forwards the relevant data to the third (output) layer. At the output of the third layer, we get the final result. More complex neural networks have more hidden layers. The layers are fully interconnected. Layers communicate by connecting the output of each neuron from the previous layer to the inputs of all neurons in the next layer. So, each node has several inputs and one output. The strength of the connections by which the neurons are connected is called the weight factor. Conclusion: In the past several books were written and several companies dealing with neurocomputers were founded. The computers were able to successfully adjust the weight coefficients, did not achieve significant practical results. Although neural networks have had an unusual history, they are still in an stage of development. Today, neural networks find a very wide range of applications in various practical areas.

Background: Relationship between preoperative anemia (PA) and postoperative delirium (POD) is not still completely clear and totally proven for surcigally treated patients. This study tries to unveil that connection in patient surgically treated for colorectal cancer (CRC). Objective: The aim of this study is to examine relationship between PA and POD in patients surgically treated for CRC and improve preoperative preparation and recognition of critical risk groups of patients for POD. Methods: Out of 62 patient were analysed in prospective method. All patient have been operated for CRC in Surgical clinic of University clinical centre Tuzla from june until december of 2024. Patients were divided in 2 groups depending on presence of PA. Presence of PA is defined as blood hemoglobin concentration (HGB) lower than 130 g/L (<13 g/dL) or hematocrite (HCT) lower than 39% in grown up men and HGB <120 g/L (<12 g/dL) or HCT<37% in grown up women. Incidence of POD was observed and noted postoperatively. POD was diagnosed and confirmed with CAM test (Confusion Assessment Method), which was done inside first 2 hours after surgery and patient extubation. Noted datas were analysed with descriptive and analytic statistic methods. Results: POD incidence was 27% (17/62) on the first postoperative day. After analysis statitically significant realtionship was found between PA and POD (ρ=0.324; p<0.05). Realised corellation is on the significance level of 0.05 (95%), it has positive direction and waek intensity. POD occurs 4,5 times more often in patients with PA. (OR= 4,500; 95% IP: 1.355–14.944). Conclusion: PA is connected with increased number of patients with POD who have been surgically treated for CRC. PA can be defined as one important preoperative risk factor that affect onset of POD . Identification of all preoperative risk factors and its correction represent best way of POD prevention.

Eryngium amethystinum L is a plant which grows in Balkan and Apennine peninsulas. The aerial parts, roots and fruits are used in ethnopharmacology of Italy and Western Balkan countries. Traditional preparations of E amethystinum were used in treatment of oedema, malaria and gastrointestinal diseases. Essential oils and extracts were isolated from aerial parts and roots of E amethystinum and examined by antimicrobial, antioxidative and cytotoxic assays. Gas-chromatography analysis showed predominance of germacrene D and spathulenol in essential oils of E amethystinum aerial parts, which have contributed to strong cytotoxic activity, while the methanolic extract exhibited strong antioxidative and antimicrobial activity. This article summarises all existing knowledge regarding E amethystinum, its chemical composition and pharmacological activity.

Background: Thyroid hormones are essential regulators of energy expenditure, thermogenesis, and body composition. Although overt thyroid dysfunction is well known to alter basal metabolic rate (BMR) and body mass, emerging evidence suggests that even hormonal variations within the reference range may exert measurable effects on metabolic and body composition profiles. Women of reproductive age represent a population particularly sensitive to hormonal oscillations due to the interplay between endocrine, reproductive, and cardiometabolic health. Objective: The study aimed to a) analyze thyroid hormone levels (TSH, FT3, FT4) alongside anthropometric and body composition parameters in women of reproductive age; b) examine thyroid hormone levels, BMR, and body composition parameters across age groups; and c) investigate associations of thyroid hormones with BMR, body composition components, and unfavorable body composition patterns (visceral adiposity, elevated metabolic age, obesity) as well as metabolic indicators.. Methods: A total of 117 women aged 18–45 years were included in this cross-sectional, observational study conducted in Bosnia and Herzegovina between September 2023 and November 2024. Thyroid hormone levels were measured using electrochemiluminescence assays, while body composition was assessed by bioelectrical impedance analysis. Statistical analyses included descriptive methods, Pearson’s correlation, and Chi-square testing, with significance set at p<0.05. Results: TSH showed significant positive associations with fat-free mass, muscle mass, and BMR (p<0.05). FT3 was inversely correlated with metabolic age and visceral fat, while FT4 demonstrated weak negative associations with fat-free mass and metabolic age (p<0.05). Significant age-related differences were observed in fat percentage, fat mass, BMI, visceral fat, and metabolic age, with the most unfavorable profiles in women aged 31–40 years. Conclusion: Thyroid hormones, even within the reference range, are associated with body composition and metabolic parameters in women of reproductive age. Their role as early indicators of unfavorable metabolic patterns highlights potential implications for reproductive and cardiovascular risk assessment.