Digital forensics, a crucial subset of cybersecurity, encompasses sophisticated tools and methodologies for the interpretation, analysis, and investigation of digital evidence, facilitating the identification and mitigation of cybercrimes and security breaches. With the advent of cryptocurrencies, an array of unique challenges has emerged in the domain of digital forensic investigations. This review elucidates the prevailing state of digital forensic practices vis-à-vis cryptocurrencies, emphasizing the obstacles and limitations inherent in probing decentralized and intricate technologies. Notable deficiencies in extant investigative practices were observed. Solutions proffered encompass the formulation of novel software applications tailored for cryptocurrency analyses, the integration of machine learning and artificial intelligence capabilities, and the employment of advanced analytics to discern patterns and irregularities within blockchain transactions. Furthermore, a pioneering methodology, merging traditional digital forensic strategies with blockchain-specific techniques, is posited for efficacious cryptocurrency inquiries. The analysis underscores the imperative for a renewed paradigm in digital forensic examinations to surmount the challenges integral to cryptocurrency probes. By forging novel methodologies and standardizing investigative procedures, support for legal enforcement endeavors can be enhanced, facilitating the efficacious detection and prosecution of cryptocurrency-associated misdemeanors.

Disruptive technologies such as the big data analytics, blockchain, Internet of Things, and artificial intelligence have each impacted how businesses operate. The most recent example of disruptive technology is artificial intelligence (AI), which has the most potential to revolutionize marketing completely. Practitioners worldwide are searching for artificial intelligence (AI) solutions most suited for their marketing functions. Artificial intelligence can provide marketers with assistance in a variety of ways to boost client satisfaction. This article looks at the exciting new developments in artificial intelligence (AI) and marketing that have been occurring recently, it examines the latest developments in marketing using artificial intelligence (AI). These breakthroughs encompass predictive analytics for analyzing customer behaviour, integrating chatbots to enhance customer support, and implementing AI-driven content personalization tactics. This article also covers the horizons and problems of artificial intelligence and marketing, the precise applications of AI in a range of marketing segments, and their impact on marketing sectors. Additionally, this article examines the particular applications of AI in marketing.

The impact of the peak to Average Power Ratio (PAPR) on the efficiency of an Orthogonal Frequency Division Multiplexing (OFDM) communication system is significantly mitigated through an innovative Reconfigurable Integrated Algorithm (RIA). In this study, the RIA combines the advantages of Partial Transmit Sequence (PTS) and Companding Transformation (CT) techniques, enhancing the overall efficiency while reducing the signal distortion inherent in linear transformation methods. A unique reconfiguration process enables integration of PTS and CT to minimize PAPR. This process considers key parameters including multi-channel inputs and delay attenuation factors. Comparison of the RIA with conventional methods such as PTS, CT, selective mapping (SLM), and Tone Reservation (TR) reveals superior performance, as evidenced by the Complementary Cumulative Distribution Function (CCDFs) curve. Implementations of the algorithm using MATLAB R2022a demonstrate significant improvements in PAPR performance, showing gains of 0.55dB and 0.656dB compared to the PTS and CT methods respectively. Moreover, the novel RIA methodology exhibits enhanced transmission rates and lower Bit Error Rates (BER) relative to conventional methods. In conclusion, the proposed RIA offers a promising approach for optimizing OFDM system performance through efficient PAPR reduction. Its implementation can drive the advancement of telecommunications technologies and further understanding of OFDM communication systems.

In decision-making scenarios, challenges often arise from closely knitted criteria or inherent uncertainties. Such uncertainties prominently pervade the realm of sustainable energy, particularly concerning hydrogen generation systems. A critical need is identified to elucidate the efficiency, costs, and environmental implications of these technologies as a shift towards a low-carbon economy is pursued. In this study, the interdependencies among decision-making variables were examined, revealing their collective influence and correlations. By utilizing the framework of Intuitionistic Hypersoft Sets (IHSSs), uncertainties were addressed, multi-criteria decision-making (MCDM) was harnessed, technological selection was facilitated, resource allocation was optimized, and environmental ramifications were assessed. The primary objective of this research was to decipher the conundrum of choosing among multiple hydrogen production methodologies. Such an approach fosters the adoption of environmentally conducive hydrogen production methods, heralding a shift towards a greener energy future. Notably, further research could probe into methodologies like AHP and TOPSIS in a neutrosophic context, offering tantalizing avenues for exploration.

Effective risk management remains pivotal to the success of any project, especially in the oil and gas industry. This study seeks to identify and quantify the potential risks in Oil and Gas Construction Projects (OGCP) within Pakistan. An exhaustive literature review is undertaken to elucidate various risk classifications and factors. Nine risk classifications emerge from this scrutiny: Health, Safety and Environment (HSE), political, legal, regulatory and bureaucratic, labor and human resources, logistics, economic and financial, technological and technical, and security and management. The novelty of this research lies in the adoption of a quantitative approach, a questionnaire rooted in Failure Modes and Effects Analysis (FMEA), asking respondents to quantify risk factors based on severity, occurrence, and detection. The results obtained from the modified FMEA questionnaire indicate that the highest average risks are associated with logistics, health, environment and safety, and legal, regulatory and bureaucratic factors. Meanwhile, political, human resource, management, and technical and technological factors register as the second-highest risks. Security risk records the least average Risk Priority Number (RPN). The most significant risk factors identified include the lack of a disaster management system, depletion of hydrocarbon resources, corruption, contractual breaches, delays in customs clearance, logistic provider complications, design flaws, technical limitations, and contractor incompetence. This research endeavors to provide academia and industry with expansive knowledge related to the risks inherent in these complex projects.

In this study, the precision of anatomical models and surgical guides pertaining to the knee joint, fabricated using the mSLA technique, was critically examined. The ITK-SNAP program was employed for the segmentation and reconstruction of knee joint anatomical structures, while surgical guide modelling was executed using the Siemens NX program. Subsequent fabrication of the models was accomplished with the Anycubic Photon Mono 4K MSLA 3D printer. An MCA II articulated arm equipped with a laser head, in conjunction with a TalyScan 150 profilometer, was utilized to gauge both the geometrical fidelity and surface roughness of the resulting models. Results indicated that the geometrical precision of these models remained within a tolerance of +/-0.3 mm. With regard to surface roughness, the Sa parameter was observed to lie between 2 and 2.5 µm.

In modern mobile machines, mechatronic systems have been integrated, enabling: a) the automation and robotization of machine tasks, b) the regulation of drive system parameters, and c) the transfer and processing of signals pertaining to machine management and monitoring. This study presents an in-depth analysis of mechatronic systems responsible for drive system regulation, transmission automation, and robotization of mobile machine manipulators. Criteria and objectives for regulation and automation are delineated, based on which application software has been developed. Through these mechatronic systems, efficient, ergonomic, and ecologically sound operations of mobile machines are facilitated.

Wood, notably in the forms of sawn lumber and glued laminated (glulam) timber, serves as a prevalent structural material for lightweight constructions and bridges with short spans. Over time, timber structures might experience deterioration due to factors such as biological attack, ageing, and escalated service loads. In such cases, reinforcing or repairing the compromised timber components can often be more economical than full replacement. Fiber-reinforced polymer (FRP) composites, particularly those strengthened using carbon fiber, present significant potential in enhancing the stiffness or load-carrying capacity of these timber systems. In the present investigation, the bending behavior of both solid and glulam beams, reinforced with carbon FRP composites in a "U" shape at the bottom layer, was studied experimentally and numerically. It was observed that reinforced glulam beams exhibit superior load-carrying capacity, displacement, modulus of rupture, and modulus of elasticity as compared to their unreinforced solid beam counterparts. Even though both types of beams are fabricated from identical materials, the laminated beams demonstrated markedly enhanced bending characteristics. Moreover, the addition of reinforcement to glulam beams showed a substantial improvement in bending performance. Consistency between numerical simulations, conducted using a finite element analysis program, and experimental outcomes was noted. This research suggests that timber materials, when strengthened with fiber-augmented polymer fabrics, can be accurately represented using numerical tools.

Project delays pose a substantial challenge in the construction sector. The primary objective of this research is to discern the root causes of project delays in the construction industry and proffer potential solutions, inclusive of the application of building information modeling (BIM) and integrated project delivery (IPD). The integration of IPD and BIM, predicated upon established blueprints, was explored to streamline cost management processes and investigate the potential incorporation of design information within the framework of building price lists. A comprehensive review of extant literature identified 20 possible causes of delays in Iranian construction projects. This study employed a descriptive research design, analyzing data collected from 90 questionnaires completed by construction experts using the statistical package for the social sciences (SPSS) statistical software. A case study of the Dehloran Azad University building project was undertaken, utilizing Revit software for simulation exercises. Field investigations, coupled with a questionnaire disseminated among construction consultants and contractors, elucidated four primary factors contributing to project delays in Iran: 1) the employer's failure to fulfill financial obligations; 2) disregard for the socio-political-economic conditions; 3) absence of a feasibility study prior to tender participation; and 4) inadequate interdepartmental communication. Successful project execution hinges on active team participation and the value that such teamwork brings. The implementation of the IPD model was found to encourage increased enthusiasm and participation. Given that the most significant source of delays in Iran's construction projects was identified as financial issues, the adoption of BIM/IPD may mitigate delays and risks associated with inaccurate estimates. This approach was also found to be effective in projects that are in mid-stage completion.