Search Results (45)

Subscription video-on-demand (SVOD) platforms face high churn rates and substantial revenue losses from SVOD content piracy, all of which limit their ability to invest in acquiring/creating content compelling enough to win and retain subscribers. Based on social exchange theory, this study argues that platforms can improve relationships with SVOD content users by offering tiered discounts in exchange for advertising/loyalty and by promoting anti-piracy messages with a prosocial (threatening) approach that emphasizes harm to filmmakers (punishment for pirates). We hypothesize that these incentives enhance subscription intention when the incentive specifications (advertising levels, loyalty levels, message approach, and message credibility) match the public’s heterogeneous dispositions (advertising attitude, loyalty attitude, justice sensitivity, and fear of punishment). In a survey on the intention to subscribe to a hypothetical new platform, we confirmed the hypothesized interactions for advertising-based discounts, loyalty-based discounts, and prosocial messages, but did not find support for threatening messages. Further exploration showed that the evaluation of platform content was much more influential than any other incentive and that tiered loyalty discounts had a remarkable capacity to enhance subscription intention. This study’s findings may help shape incentives that are more satisfying to users and ultimately more profitable for platforms. Full article

We report a case of a 16-year-old girl presenting to our clinic with decreased visual acuity and increased intraocular pressure in both eyes. The ophthalmological examination revealed best-corrected visual acuity (BCVA) of 0.3 in the right eye (R.E.) and 0.4 in the left eye (L.E.) and intraocular pressure (IOP) of 46 mmHg in the R.E. and 42 mmHg in the L.E., with a 360° closed angle on gonioscopy, pupillary block due to bulging, a hyper-spherical lens and high corneal thickness, without ectopia lentis or cataract. The eyes responded poorly to pharmacological mydriasis; therefore, the lens equator could not be visualised. The patient had a history of pulmonary stenosis, short stature and no significant cognitive deficits. These elements point to the diagnosis of Weill–Marchesani syndrome, and the ophthalmological management was surgical, including lens extraction and the installation of a capsular tension ring, an intraocular lens and a Shunt ExPress implantation. Evolution was favourable, with improved BCVA of 0.7 in the R.E. and 0.63 in the L.E. and IOP of 14 mmHg in the R.E. and 13 mmHg in the L.E., without topical or systemic treatment at the 6-month follow-up. Weill–Marchesani syndrome has a complex presentation, with ophthalmological, musculoskeletal, cardiac and psychiatric manifestations. Usually, this leads to a need for a multidisciplinary approach. The ophthalmologic symptoms are often the cause of presentation to a specialist, and glaucoma is the most threatening of the ocular pathologies, with possible evolution into irreversible blindness; therefore, prompt surgery and careful follow-up become key components of the treatment plan. As a take-home message, we encourage a high degree of suspicion of Weill–Marchesani syndrome in such cases. Full article

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy. Full article

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the transmission of safety-critical messages and put lives at risk. This research paper focuses on developing robust detection methods and countermeasures to mitigate the impact of DDoS attacks in VANETs. Utilizing a combination of statistical analysis and machine learning techniques (i.e., Autoencoder with Long Short-Term Memory (LSTM), and Clustering with Classification), the study introduces innovative approaches for real-time anomaly detection and system resilience enhancement. Emulation results confirm the effectiveness of the proposed methods in identifying and countering DDoS threats, significantly improving (i.e., 94 percent anomaly detection rate) the security posture of a high mobility-aware ad hoc network. This research not only contributes to the ongoing efforts to secure VANETs against DDoS attacks but also lays the groundwork for more resilient intelligent transportation systems architectures. Full article

Climate change effects are a warning of the planetary crises threatening our collective future. This is a topic largely considered in the context of the environmental crisis, but we are now aware that climate change represents an increasingly alarming threat also in terms of the conservation of cultural heritage sites. Cultural heritage preservation should aim to an active environmental and societal strategy built on a renewed ethics of responsibility on long-term effects. This work provides a review of the current state of the art on the damage functions used for assessing the impacts of climate change on stone heritage surfaces. Within this framework, it introduces new concepts such as (i) the Loss of Details (LoD), in terms of the readability reduction of decorative elements and, subsequently, (ii) the Future Cultural Value (FCV), as the capacity of a cultural heritage to transmit its cultural message in its future appearance. The valorization of the historical legacy is a win–win solution to fix new planning tools and to achieve multiple goals oriented to a sustainable development for future generations. From this point of view, plaster cast galleries and museums play a crucial role in preserving cultural identity since they report a careful documentation of the original artifacts and monuments over the time. Full article

Bothrofav, a monospecific antivenom, was introduced in June 1991 and has shown excellent effectiveness against life-threatening and thrombotic complications of Bothrops lanceolatus envenoming. Because of the reoccurrence of cerebral stroke events despite the timely administration of antivenom, new batches of Bothrofav were produced and introduced into clinical use in January 2011. This study’s aim was to evaluate the effectiveness of Bothrofav generations at treating B. lanceolatus envenoming. During the first period of the study (2000–2010), 107 patients were treated with vials of antivenom produced in June 1991, while 282 envenomed patients were treated with vials of antivenom produced in January 2011 in the second study period (2011–2023). Despite timely antivenom administration, thrombotic complications reoccurred after an interval free of thrombotic events, and a timeframe analysis suggested that the clinical efficacy of Bothrofav declined after it reached its 10-year shelf-life. In of the case of an antivenom shortage due to the absence of regular batch production, no adverse effects were identified before the antivenom reached its 10-year shelf-life, which is beyond the accepted shelf-life for a liquid-formulation antivenom. While our study does not support the use of expired antivenom for potent, life-threatening B. lanceolatus envenoming, it can be a scientific message to public entities proving the necessity of new antivenom production for B. lanceolatus envenoming. Full article

As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced artificial intelligence techniques to significantly enhance multiplatform spam classification on Twitter and YouTube. The deep neural networks we use are state-of-the-art. They are recurrent neural network architectures with long- and short-term memory cells that are powered by both static and contextualized word embeddings. Extensive comparative experiments precede rigorous hyperparameter tuning on the datasets. Results reveal a profound impact of tailored, platform-specific AI techniques in combating sophisticated and perpetually evolving threats. The key innovation lies in tailoring deep learning (DL) architectures to leverage both intrinsic platform contexts and extrinsic contextual embeddings for strengthened generalization. The results include consistent accuracy improvements of more than 10–15% in multisource datasets, unlocking actionable guidelines on optimal components of neural models, and embedding strategies for cross-platform defense systems. Contextualized embeddings like BERT and ELMo consistently outperform their noncontextualized counterparts. The standalone ELMo model with logistic regression emerges as the top performer, attaining exceptional accuracy scores of 90% on Twitter and 94% on YouTube data. This signifies the immense potential of contextualized language representations in capturing subtle semantic signals vital for identifying disguised spam. As emerging adversarial attacks exploit human vulnerabilities, advancing defense strategies through enhanced neural language understanding is imperative. We recommend that social media companies and academic researchers build on contextualized language models to strengthen social media security. This research approach demonstrates the immense potential of personalized, platform-specific DL techniques to combat the continuously evolving threats that threaten social media security. Full article

Water scarcity is one of the global challenges that threatens economic development and imposes constraints on societal growth. In the semi-arid expanse of West Texas, small communities are struggling with both growing populations and decreasing water resources in the regional aquifer. This study compares two nature-based methods that could solve this problem. The first approach uses ponds and wetlands to make natural processes work together to treat the wastewater that the community receives. We applied a novel Pond-in-Pond system, which offers advantages compared to conventional pond system configurations. This system unlocks strategic hydrodynamic advantages by introducing a deeper anaerobic pit surrounded by berms, which then outflows into a larger pond. The second approach consists of an alternative strategy which integrates waste stabilization ponds, a storage basin, and the reuse of wastewater for crop irrigation—a feat that not only treats water but also enriches soil fertility. Both approaches were analyzed in terms of economic potential and pollution control. The land application had a better return on investment and emphasized the importance of innovative solutions for sustainable water management in arid regions, offering economic and community benefits. The application conveys a clear message: where water is scarce, innovation can grow; where problems are big, solutions are available; and where nature’s processes are understood, they can be used. Full article

In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks. Full article

Safeguarding the data collected by smart meters is essential because the disclosure of this information may threaten the privacy of the consumer. By obtaining them, hackers can find out the behavior of the person and use that information for malicious purposes. Therefore, the anonymity of such information can prevent the occurrence of risks. Given the paramount significance of user privacy and data integrity, this paper primarily investigates the confidentiality, integrity, and anonymity of messages. This paper aims to develop a platform for determining dynamic pricing to coordinate supply and demand, thereby maximizing the efficiency of facilities. In the previous research, the operation center was not authenticated for the customer in the first step, and they also had a heavy computational cost. But this paper has endeavored to develop an efficient and comprehensive privacy-preserving solution for the smart electricity network. Also, it has tried to cover all the required security objectives by dealing with authenticity, confidentiality, and irrefutability. The method of the research is that two entities mutually authenticate each other and reach a key agreement so that if the operation center wants to send a control command, it can send control commands directly to the meter with less time complexity. The power company sends control commands and requests to the smart meters until the analyzed and collected energy consumption data are transmitted. The data aggregator node gathers the data from the meters. The results showed that the proposed method reduced the computational complexity and communication overhead to a satisfactory level and is also resistant to various attacks. Full article

Joel is one of the 12 minor prophets (dōdekaprophēton). His prophecy aims at calling the nation and people to repentance through emphasizing that the Day of the Lord (yōm ădȏnay) is at hand (3:1–5 [2:28–32]). The locust plague (ʾarbbeh) in Joel’s message—which recalls the insects that threaten to destroy crops and vegetation in Africa and beyond, but which can also be used as food and livestock feed and offer other benefits as well—could be interpreted as Joel’s prophetic sign that the great Day of the Lord is near (1:2–2:17). Throughout history, scholars, theologians, and exegetes of differing schools of thought and from numerous locations have offered various interpretations for Joel’s prophecy and subjected it to diverse Eurocentric and Americo-centric hermeneutical methods. This work, however, with its focus on Africa, takes a different approach. Drawing from the work of many African hermeneuticians, it reads Joel’s prophecy using the tools of African Biblical Hermeneutics (ABH), a post-colonial enterprise, in light of the decolonial turn. The article exegetes and theologically analyzes the narrative of the locust plague (ʾarbbeh) in Joel 1:2–7, within the context of Joel 1–3, with the hopes that it will be transformational and beneficial for African readers within their faith context. Full article

Vehicular ad hoc networks (VANETs) are used for improving traffic efficiency and road safety. However, VANETs are vulnerable to various attacks from malicious vehicles. Malicious vehicles can disrupt the normal operation of VANET applications by broadcasting bogus event messages that may cause accidents, threatening people’s lives. Therefore, the receiver node needs to evaluate the authenticity and trustworthiness of the sender vehicles and their messages before acting. Although several solutions for trust management in VANETs have been proposed to address these issues of malicious vehicles, existing trust management schemes have two main issues. Firstly, these schemes have no authentication components and assume the nodes are authenticated before communicating. Consequently, these schemes do not meet VANET security and privacy requirements. Secondly, existing trust management schemes are not designed to operate in various contexts of VANETs that occur frequently due to sudden variations in the network dynamics, making existing solutions impractical for VANETs. In this paper, we present a novel blockchain-assisted privacy-preserving and context-aware trust management framework that combines a blockchain-assisted privacy-preserving authentication scheme and a context-aware trust management scheme for securing communications in VANETs. The authentication scheme is proposed to enable anonymous and mutual authentication of vehicular nodes and their messages and meet VANET efficiency, security, and privacy requirements. The context-aware trust management scheme is proposed to evaluate the trustworthiness of the sender vehicles and their messages, and successfully detect malicious vehicles and their false/bogus messages and eliminate them from the network, thereby ensuring safe, secure, and efficient communications in VANETs. In contrast to existing trust schemes, the proposed framework can operate and adapt to various contexts/scenarios in VANETs while meeting all VANET security and privacy requirements. According to efficiency analysis and simulation results, the proposed framework outperforms the baseline schemes and demonstrates to be secure, effective, and robust for enhancing vehicular communication security. Full article

Technology plays a double-edged role in the interpreting job market. The development of technology may threaten the jobs of interpreters, as well as provide aids for them. The sustainable development of interpreters depends on determining the balance between the challenges and aids of modern technology so as to take advantage of it to enhance the performance of interpreting to fend off the challenge of technology itself. This paper launches these empirical studies by taking a tech product (a speech recognition device) as the study object, considering the lack of empirical studies about technology-assisted interpreting, as well as to explore the way and the extent to which the technology can facilitate interpreting performance in such a competitive world. Listening comprehension is generally regarded as the most difficult element in interpreting because understanding what we hear requires a colossal amount of cognitive effort, which will inevitably jeopardize the overall performance of interpreting. Therefore, how and to what extent the application of technology is capable of improving this performance is worth our attention. To measure the interpreting performance via the domain of language accuracy, message fidelity, etc., the empirical research conducted in the paper includes 30 student interpreters and 20 professional interpreters to explore the extent to which speech recognition assistance can increase the effectiveness of interpreting, as well as to shed some light on the professional sustainability and future training of interpreters. Full article

Vehicular ad hoc networks have emerged as the main building block for the future cooperative intelligent transportation system (cITS) to improve road safety and traffic efficiency and to provide passenger comfort. However, vehicular networks are decentralized, characterized by high mobility and dynamicity, and vehicles move in a hostile environment; such characteristics make VANET applications suffer many security and communication issues. Recently, blockchain has been suggested to solve several VANET issues including the dissemination of trustworthy life-threatening information. However, existing dissemination schemes are inefficient for safety messages and are vulnerable to malicious nodes and rely on the majority of honest assumptions. In the VANET context, adversaries may collude to broadcast false information causing serious safety threats. This study proposes an intelligent proof-of-trustworthiness-based secure safety message dissemination scheme (PoTMDS) to efficiently share only trustworthy messages. The consistency and plausibility of the message were evaluated based on a predictive model developed using a convolutional neural network and signal properties such as the received signal strength and angle of arrival. A blockchain-based data dissemination scheme was developed to share critical messages. Each vehicle calculates the proof of trustworthiness of the disseminated messages by comparing the received message with the output of the prediction model. The results showed that the proposed scheme reduced the consensus delay by 58% and improved the detection accuracy by 7.8%. Therefore, the proposed scheme can have an important role in improving the applications of future cITS. Full article

Road safety is increasingly threatened by distracted driving. Studies have shown that there is a significantly increased risk for a driver of being involved in a car crash due to visual distractions (not watching the road), manual distractions (hands are off the wheel for other non-driving activities), and cognitive and acoustic distractions (the driver is not focused on the driving task). Driving simulators (DSs) are powerful tools for identifying drivers’ responses to different distracting factors in a safe manner. This paper aims to systematically review simulator-based studies to investigate what types of distractions are introduced when using the phone for texting while driving (TWD), what hardware and measures are used to analyze distraction, and what the impact of using mobile devices to read and write messages while driving is on driving performance. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 7151 studies were identified in the database search, of which 67 were included in the review, and they were analyzed in order to respond to four research questions. The main findings revealed that TWD distraction has negative effects on driving performance, affecting drivers’ divided attention and concentration, which can lead to potentially life-threatening traffic events. We also provide several recommendations for driving simulators that can ensure high reliability and validity for experiments. This review can serve as a basis for regulators and interested parties to propose restrictions related to using mobile phones in a vehicle and improve road safety. Full article