Enforcement criminal law is a system of legal regulations that determine the procedure, manner and conditions for the execution of criminal sanctions. Serbia also implemented European standards in the execution of criminal sanctions, with special emphasis on the level of implementation of those standards in the area of execution of criminal sanctions, in its legislation. In addition to the standards related to the conditions of serving a prison sentence, the protection of the rights of persons deprived of their liberty, the manner of treatment of persons deprived of their liberty, protection against torture, inhumane or degrading treatment and punishment is particularly emphasized. In recent decades, the increasingly prevalent view is that criminal sanctions have a weak impact on reducing the crime rate, even less on the factors that cause and shape it. Prison sentences do not achieve objective effects in changing the criminal pattern of behavior of offenders, on the contrary, they have a greater effect on increasing the risk that the offender will repeat the crime. In the conditions of globalization, digitization and international legal harmonization, the system of execution of criminal sanctions requires a high degree of common international rules that define and regulate executive criminal legislation.

Kinetic energy from vibrations emerging from mechanical systems such as machines and vehicles has been thoroughly studied as a power source in the last two decades. Numerous kinetic energy harvesters have been built to convert human locomotion into electrical power but haven’t been implemented on a wide commercial scale. On the other hand, energy harvesters for farm animals haven’t been studied as much. In this paper, we present a three-dimensional electromagnetic induction based kinetic energy harvester optimized specifically for cattle wearable applications. All the device parameters are obtained with an empirical optimization procedure by considering specific cattle locomotion characteristics. The prototype is 3-D printed with low friction and impact resistant materials. Finally, the device is tested in a real free grazing scenario with live cattle. The kinetic energy harvester performed well and was able to power the load and transmit animal body temperature data over long distances for up to 7 times/h.

This work shows the performance of LincoSim, a web-based virtual towing tank enabling automated and standardized calm water computational fluid dynamics (CFD) data sampling, extending previous published applications to the case of a high-speed hull. The calculations are performed for a 1:10 scale model of a 43 ft powerboat hull form in the Froude number range from 0.3 to 2.0. The counterpart physical model is the experimental fluid dynamics (EFD) campaign performed at the University of Naples Federico II, where the resistance, sinkage and trim data have been measured. The EFD/CFD data comparison is performed and shown with a discussion of the spotted differences. The average percentage differences between the EFD and CFD data for the whole speed range are 1.84, 6.87 and 6.94 for the resistance, dynamic trim, and sinkage, respectively. These results confirm the maturity of the standardized and automated CFD modeling for calm water hydrodynamic analysis included in LincoSim, even at very high Froude numbers. The wetted length of the keel and chine and the wetted surface are calculated from numerical data using the advanced post-processing. Finally, as a work in progress, we test a first comparison for the same hull of the EFD and CFD data, considering two seakeeping conditions for head waves at a given wavelength for two velocity conditions. Also, this kind of analysis confirms the tight correlation between the measured and computed outcomes. This synergic interplay of EFD and CFD can link the advantages of both methods to support hull design but also requires experiment planning and final data analysis to obtain physical parameters not easily measurable in laboratory, such as the wetted surface, wetted lengths, proper viscous contribution, and pressure distribution both in calm water and in waves.

In The Post-Factual Polity: Ethical, Governance, Administrative, and Policy Crises in the Disinformation Era, Haris Alibašic presents a compelling and timely examination of the intricate challenges facing our society in the age of disinformation and misinformation. This groundbreaking book delves into the critical intersection of ethics, governance, and the pervasive influence of post-factual politics, offering a nuanced understanding of the issues that shape public policy and administration in the digital era.Haris Alibašic, with his extensive experience and scholarly insight, guides readers through the complexities of ethical governance amidst the rise of disinformation. His insightful analysis, covering topics from the history of factual politics to the role of AI in public discourse, is essential for academics, policymakers, and anyone invested in the future of ethical governance. This book is a beacon for those seeking to navigate and counteract the challenges of misinformation, aiming to foster a more informed, ethical, and resilient society.The Post-Factual Polity is more than a book; it is an urgent call to action, a roadmap for navigating the tumultuous landscape of modern governance, and a testament to the power of truth in an era of uncertainty. As an authoritative source in the field, it is poised to become a vital reference for those dedicated to upholding the principles of democracy and integrity in public policy and administration.

Noncoding RNAs (ncRNAs) are pivotal for various pathological processes, impacting disease progression. The potential for leveraging ncRNAs to prevent or treat atherosclerosis and associated cardiovascular diseases is of great significance, especially given the increasing prevalence of atherosclerosis in an ageing and sedentary population. Together, these diseases impose a substantial socio‐economic burden, demanding innovative therapeutic solutions. This review explores the potential of ncRNAs in atherosclerosis treatment. We commence by examining approaches for identifying and characterizing atherosclerosis‐associated ncRNAs. We then delve into the functional aspects of ncRNAs in atherosclerosis development and progression. Additionally, we review current RNA and RNA‐targeting molecules in development or under approval for clinical use, offering insights into their pharmacological potential. The importance of improved ncRNA delivery strategies is highlighted. Finally, we suggest avenues for advanced research to accelerate the use of ncRNAs in treating atherosclerosis and mitigating its societal impact.

Abstract Forensic obstetrics attracts much attention from forensic experts and the public owing to the professional, legal, public health, and not inconsiderable social-emotional aspects and directly correlates with maternal, fetoneonatal, and iatrogenic risk factors. Modern obstetrics and fetomaternal medicine must not be quantified and qualified based only on perinatal disease but also according to current obstetric problems that burden forensic obstetrics. Therefore, high-risk obstetrics as a significant medico-legal problem should be viewed from the point of view of the entire perinatal period with possible long-term consequences, hence the monitoring of complete perinatal and infant morbidity is of immediate importance for quality control and risk control in the profession. The task of forensic obstetrics is to assess the impact of risk factors on the occurrence of an adverse event and to assess whether it is an obstetric complication or obstetric malpractice. Acknowledging the mentioned facts is the only way we will develop high obstetric awareness, and we and pregnant women, birth attendants, midwives and patients, fetuses, and newborns will have professional safety with imminent but controlled obstetric risk and controlled expected complications. On the other hand, the fact that the statistics of perinatal (obstetrical) malpractice globally is not abating requires a change in obstetrical philosophy, especially the unreasonable epidemic increase of cesarean sections with a significant percentage of cesarean sections without medical indication and complications. It is necessary to introduce and maintain solid professionalism and bioethical norms in obstetrics with constant training of skills, which is emphasized by numerous authors and with which we fully agree. Forensic obstetrics is based on the principles of good clinical practice, professional guidelines of modern obstetrics, and ethical and deontological principles. It clearly shows the perfection and imperfection of biological systems that we can and cannot influence. However, we must act according to the rules of the clinical profession, deontological rules, and health laws to reduce clinical risk to the smallest possible extent. Attention should certainly be focused on reducing the disproportion between iatrogenic and maternal-fetoneonatal risk factors, which is the most common reason for litigation today.

The relationship between the civil and business sectors has intensified in the last few years, including cross-sectoral partnerships as part of corporate social responsibility. The authors examine the impact of these partnerships on the performance of 100 of the largest businesses in Bosnia and Herzegovina. Large companies were chosen because they are most likely to be involved with corporate social responsibility activities and cooperation with civil society organizations. Methodologically, the authors analyzed the effect of these partnerships on business performance using four Balanced Scorecard components - three non-financial and one financial performance. The research results show specific influences on non-financial business performance but not financial performance.

This document presents a summary of the 2023 Terrestrial Very-Long-Baseline Atom Interferometry Workshop hosted by CERN. The workshop brought together experts from around the world to discuss the exciting developments in large-scale atom interferometer (AI) prototypes and their potential for detecting ultralight dark matter and gravitational waves. The primary objective of the workshop was to lay the groundwork for an international TVLBAI proto-collaboration. This collaboration aims to unite researchers from different institutions to strategize and secure funding for terrestrial large-scale AI projects. The ultimate goal is to create a roadmap detailing the design and technology choices for one or more kilometer--scale detectors, which will be operational in the mid-2030s. The key sections of this report present the physics case and technical challenges, together with a comprehensive overview of the discussions at the workshop together with the main conclusions.

Automating network processes without human intervention is crucial for the complex Sixth Generation (6G) environment. Thus, 6G networks must advance beyond basic automation, relying on Artificial Intelligence (AI) and Machine Learning (ML) for self-optimizing and autonomous operation. This requires zero-touch management and orchestration, the integration of Network Intelligence (NI) into the network architecture, and the efficient lifecycle management of intelligent functions. Despite its potential, integrating NI poses challenges in model development and application. To tackle those issues, this paper presents a novel methodology to manage the complete lifecycle of Reinforcement Learning (RL) applications in networking, thereby enhancing existing Machine Learning Operations (MLOps) frameworks to accommodate RL-specific tasks. We focus on scaling computing resources in service-based architectures, modeling the problem as a Markov Decision Process (MDP). Two RL algorithms, guided by distinct Reward Functions (RFns), are proposed to autonomously determine the number of service replicas in dynamic environments. Our proposed methodology is anchored on a dual approach: firstly, it evaluates the training performance of these algorithms under varying RFns, and secondly, it validates their performance after being trained to discern the practical applicability in real-world settings. We show that, despite significant progress, the development stage of RL techniques for networking applications, particularly in scaling scenarios, still leaves room for significant improvements. This study underscores the importance of ongoing research and development to enhance the practicality and resilience of RL techniques in real-world networking environments.

As network complexity escalates, there is an increasing need for more sophisticated methods to manage and operate these networks, focusing on enhancing efficiency, reliability, and security. A wide range of Artificial Intelligence (AI)/Machine Learning (ML) models are being developed in response. These models are pivotal in automating decision-making, conducting predictive analyses, managing networks proactively, enhancing security, and optimizing network performance. They are foundational in shaping the future of networks, collectively forming what is known as Network Intelligence (NI). Prominent Standard-Defining Organizations (SDOs) are integrating NI into future network architectures, particularly emphasizing the closed-loop approach. However, existing methods for seamlessly integrating NI into network architectures are not yet fully effective. This paper introduces an in-depth architectural design for a Network Intelligence Stratum (NI Stratum). This stratum is supported by a novel end-to-end NI orchestrator that supports closed-loop NI operations across various network domains. The primary goal of this design is to streamline the deployment and coordination of NI throughout the entire network infrastructure, tackling issues related to scalability, conflict resolution, and effective data management. We detail exhaustive workflows for managing the NI lifecycle and demonstrate a reference implementation of the NI Stratum, focusing on its compatibility and integration with current network systems and open-source platforms such as Kubernetes and Kubeflow, as well as on its validation on real-world environments. The paper also outlines major challenges and open issues in deploying and managing NI.

The cleavage of C-S bonds represents a crucial step in fossil fuel refinement to remove organosulfur impurities. Efforts are required to identify alternatives that can replace the energy-intensive hydrodesulfurization process currently in use. In this context, we have developed a series of bis-thiolato-ligated CrIII complexes supported by the L2- ligand (L2- = 2,2'-bipyridine-6,6'-diyl(bis(1,1-diphenylethanethiolate), one of them displaying desulfurization of one thiolate of the ligand under reducing and acidic conditions at ambient temperature and atmospheric pressure. While only 5-coordinated complexes were previously isolated by reaction of L2- with 3d metal MIII ions, both 5- and 6-coordinated mononuclear complexes have been obtained in the case of CrIII, viz., [CrIIILCl], [CrIIILCl2]-, and [CrIIILCl(CH3CN)]. The investigation of the reactivity of [CrIIILCl(CH3CN)] under reducing conditions led to a dinuclear [CrIII2L2(μ-Cl)(μ-OH)] compound and, in the presence of protons, to the mononuclear CrIII complex [CrIII(LN2S)2]+, where LN2S- is the partially desulfurized form of L2-. A desulfurization mechanism has been proposed involving the release of H2S, as evidenced experimentally.

Large language models (LLMs) have revolutionized Natural Language Processing (NLP), but their size creates computational bottlenecks. We introduce a novel approach to create accurate, sparse foundational versions of performant LLMs that achieve full accuracy recovery for fine-tuning tasks at up to 70% sparsity. We achieve this for the LLaMA-2 7B model by combining the SparseGPT one-shot pruning method and sparse pretraining of those models on a subset of the SlimPajama dataset mixed with a Python subset of The Stack dataset. We exhibit training acceleration due to sparsity on Cerebras CS-3 chips that closely matches theoretical scaling. In addition, we establish inference acceleration of up to 3x on CPUs by utilizing Neural Magic's DeepSparse engine and 1.7x on GPUs through Neural Magic's nm-vllm engine. The above gains are realized via sparsity alone, thus enabling further gains through additional use of quantization. Specifically, we show a total speedup on CPUs for sparse-quantized LLaMA models of up to 8.6x. We demonstrate these results across diverse, challenging tasks, including chat, instruction following, code generation, arithmetic reasoning, and summarization to prove their generality. This work paves the way for rapidly creating smaller and faster LLMs without sacrificing accuracy.

Background and Objectives There is uncertainty whether patients with large vessel occlusion (LVO) presenting in the late 6-hour to 24-hour time window can be selected for endovascular therapy (EVT) by noncontrast CT (NCCT) and CT angiography (CTA) for LVO detection. We evaluated the clinical outcomes of patients selected for EVT by NCCT compared with those medically managed in the extended time window. Methods This multinational cohort study was conducted at 66 sites across 10 countries. Consecutive patients with proximal anterior LVO stroke selected for EVT by NCCT or medically managed and presenting within 6–24 hours of time last seen well (TSLW) from January 2014 to May 2022 were included. The primary end point was the 90-day ordinal shift in the modified Rankin Scale (mRS) score. Inverse probability treatment weighting (IPTW) and multivariable methods were used. Results Of 5,098 patients screened, 839 patients were included, with a median (interquartile range) age of 75 (64–83) years; 455 (54.2%) were women. There were 616 patients selected to undergo EVT by NCCT (73.4%) and 223 (26.6%) who were medically managed. In IPTW analyses, there was a more favorable 90-day ordinal mRS shift in patients selected by NCCT to EVT vs those who were medically managed (odds ratio [OR] 1.99, 95% CI 1.53–2.59; p < 0.001). There were higher rates of 90-day functional independence (mRS 0–2) in the EVT group (40.1% vs 18.4%, OR 3.31, 95% CI 2.11–5.20; p < 0.001). sICH was nonsignificantly higher in the EVT group (8.5% vs 1.4%, OR 3.77, 95% CI 0.72–19.7, p = 0.12). Mortality at 90 days was lower in the EVT vs MM group (23.9% vs 32.3%, OR 0.61, 95% CI 0.45–0.83, p = 0.002). Discussion In patients with proximal anterior LVO in the extended time window, there was a lower rate of disability and mortality in patients selected with NCCT and CTA to EVT compared with those who were medically managed. These findings support the use of NCCT as a simpler and more inclusive approach to patient selection in the extended window. Trial Registration Information This study was registered at ClinicalTrials.gov under NCT04096248. Classification of Evidence This study provides Class III evidence that for patients with proximal anterior circulation occlusion presenting with ischemic stroke from 6 to 24 hours, compared with medical management, those undergoing thrombectomy based on NCCT have reduced disability and mortality at 90 days.

With the increasing adoption of IoT devices and applications, significant research and development efforts have been centered around engineering novel ecosystems referred to as the IoT-edge-cloud compute continuum. In this article, we implement, analyze and present a case study for performance benchmarking of five well known and select open source MQTT broker implementations in an open-source compute continuum testbed. The proposed MQTT broker implementations are evaluated in terms of response time, different payload sizes and throughput. Measurements and results show that the hardware platform used, the message size, as well as the network parameters (latency, packet loss and jitter) have a significant impact on the resulting performance of various broker implementations and therefore have to be carefully considered in the selection process for the building blocks of the continuum. All implementations and measurements are made to be fully reproducible and free and open source.

As human medicine is developing at a galloping pace, continuously offering new medical products, diagnostic methods and preventive programmes, there is almost no time gap between their creation and application in medical practice. All these biomedical achievements are primarily intended to improve public health and the patient’s quality of life and health. Hence, it is important to define potential risks, side effects, and unwanted outcomes when applying a medical product/treatment before integrating it into healthcare. Unlike any other product/treatment intended for human use, medical products/treatments require prior clinical testing on human subjects (sick or sound). The authors of this paper have restricted their scientific interest to the participant (human subject) of a clinical study as one of the core elements of a clinical investigation, representing at the same time its means and its aim. By analyzing relevant international as well as national legal rules and ethical principles of the Republic of Srpska related to the participation of humans in clinical studies, it will be concluded that the participants’ safety and right to self-determination, integrity, and autonomy manifested through their independent right to either consent or refuse to participate in a clinical study supersedes the interests of science or society. However, clinical trial-related statistical data obtained from randomly chosen healthcare institutions in the Republic of Srpska will show certain derogations from prescribed ethical policies. Considering this fact, the authors have paid special attention to thematising the ethicality of recruiting participants for a clinical study based on partial or no information related to the purpose, methods, potential risks and side effects of the investigation in the name of the greater good for humanity. Such practice has accentuated the discretionary powers of ethical review committees on the one side and the uncertainty of the right to informed consent on the other.