bluuebunny/crossref_metadata_2025 huggingface dataset:
Pseudorabies Virus Inhibits Expression of Liver X Receptors to Assist Viral Infection
Authors: Wang Yi , Li Guo-Li , Qi Yan-Li , Li Li-Yun , Wang Lu-Fang , Wang Cong-Rong , Niu Xin-Rui , Liu Tao-Xue , Wang Jiang , Yang Guo-Yu , Zeng Lei , Chu Bei-Bei
Published: March 2022

Pseudorabies virus (PRV) is a contagious herpesvirus that causes Aujeszky’s disease and economic losses worldwide. Liver X receptors (LXRs) belong to the nuclear receptor superfamily and are critical for the control of lipid homeostasis. However, the role of LXR in PRV infection has not been fully established. In this study, we found that PRV infection downregulated the mRNA and protein levels of LXRα and LXRβ in vitro and in vivo. Furthermore, we discovered that LXR activation suppressed PRV proliferation, while LXR inhibition promoted PRV proliferation. We demonstrated that LXR activation-mediated reduction of cellular cholesterol was critical for the dynamics of PRV entry-dependent clathrin-coated pits. Replenishment of cholesterol restored the dynamics of clathrin-coated pits and PRV entry under LXR activation conditions. Interestingly, T0901317, an LXR agonist, prevented PRV infection in mice. Our results support a model that PRV modulates LXR-regulated cholesterol metabolism to facilitate viral proliferation.
Rocaglates as Antivirals: Comparing the Effects on Viral Resistance, Anti-Coronaviral Activity, RNA-Clamping on eIF4A and Immune Cell Toxicity
Authors: Obermann Wiebke , Friedrich Alexandra , Madhugiri Ramakanth , Klemm Paul , Mengel Jan Philipp , Hain Torsten , Pleschka Stephan , Wendel Hans-Guido , Hartmann Roland K. , Schiffmann Susanne , Ziebuhr John , Müller Christin , Grünweller Arnold
Published: March 2022

Rocaglates are potent broad-spectrum antiviral compounds with a promising safety profile. They inhibit viral protein synthesis for different RNA viruses by clamping the 5′-UTRs of mRNAs onto the surface of the RNA helicase eIF4A. Apart from the natural rocaglate silvestrol, synthetic rocaglates like zotatifin or CR-1-31-B have been developed. Here, we compared the effects of rocaglates on viral 5′-UTR-mediated reporter gene expression and binding to an eIF4A-polypurine complex. Furthermore, we analyzed the cytotoxicity of rocaglates on several human immune cells and compared their antiviral activities in coronavirus-infected cells. Finally, the potential for developing viral resistance was evaluated by passaging human coronavirus 229E (HCoV-229E) in the presence of increasing concentrations of rocaglates in MRC-5 cells. Importantly, no decrease in rocaglate-sensitivity was observed, suggesting that virus escape mutants are unlikely to emerge if the host factor eIF4A is targeted. In summary, all three rocaglates are promising antivirals with differences in cytotoxicity against human immune cells, RNA-clamping efficiency, and antiviral activity. In detail, zotatifin showed reduced RNA-clamping efficiency and antiviral activity compared to silvestrol and CR-1-31-B, but was less cytotoxic for immune cells. Our results underline the potential of rocaglates as broad-spectrum antivirals with no indications for the emergence of escape mutations in HCoV-229E.
Protective Efficacy of BCG Vaccine against Mycobacterium leprae and Non-Tuberculous Mycobacterial Infections
Authors: Orujyan Davit , Narinyan William , Rangarajan Subhapradha , Rangchaikul Patrida , Prasad Chaya , Saviola Beatrice , Venketaraman Vishwanath
Published: March 2022

The genus mycobacterium includes several species that are known to cause infections in humans. The microorganisms are classified into tuberculous and non-tuberculous based on their morphological characteristics, defined by the dynamic relationship between the host defenses and the infectious agent. Non-tuberculous mycobacteria (NTM) include all the species of mycobacterium other than the ones that cause tuberculosis (TB). The group of NTM contains almost 200 different species and they are found in soil, water, animals—both domestic and wild—milk and food products, and from plumbed water resources such as sewers and showerhead sprays. A systematic review of Medline between 1946 and 2014 showed an 81% decline in TB incidence rates with a simultaneous 94% increase in infections caused by NTM. Prevalence of infections due to NTM has increased relative to infections caused by TB owing to the stringent prevention and control programs in Western countries such as the USA and Canada. While the spread of typical mycobacterial infections such as TB and leprosy involves human contact, NTM seem to spread easily from the environment without the risk of acquiring from a human contact except in the case of M. abscessus in patients with cystic fibrosis, where human transmission as well as transmission through fomites and aerosols has been recorded. NTM are opportunistic in their infectious processes, making immunocompromised individuals such as those with other systemic infections such as HIV, immunodeficiencies, pulmonary disease, or usage of medications such as long-term corticosteroids/TNF-α inhibitors more susceptible. This review provides insight on pathogenesis, treatment, and BCG vaccine efficacy against M. leprae and some important NTM infections.
Avian Orexin: Feed Intake Regulator or Something Else?
Authors: Ramser Alison , Dridi Sami
Published: March 2022

Originally named for its expression in the posterior hypothalamus in rats and after the Greek word for “appetite”, hypocretin, or orexin, as it is known today, gained notoriety as a neuropeptide regulating feeding behavior, energy homeostasis, and sleep. Orexin has been proven to be involved in both central and peripheral control of neuroendocrine functions, energy balance, and metabolism. Since its discovery, its ability to increase appetite as well as regulate feeding behavior has been widely explored in mammalian food production animals such as cattle, pigs, and sheep. It is also linked to neurological disorders, leading to its intensive investigation in humans regarding narcolepsy, depression, and Alzheimer’s disease. However, in non-mammalian species, research is limited. In the case of avian species, orexin has been shown to have no central effect on feed-intake, however it was found to be involved in muscle energy metabolism and hepatic lipogenesis. This review provides current knowledge and summarizes orexin’s physiological roles in livestock and pinpoints the present lacuna to facilitate further investigations.
Swimming Pool Regulations in the COVID-19 Era: Assessing Acceptability and Compliance in Greek Hotels in Two Consecutive Summer Touristic Periods
Authors: Mellou Kassiani , Mplougoura Athina , Mandilara Georgia , Papadakis Antonios , Chochlakis Dimosthenis , Psaroulaki Anna , Mavridou Athena
Published: March 2022

The COVID-19 pandemic has urged many countries to issue new regulations to assure safety in pool environments. Greece enforced stricter requirements in 2020 and 2021 for hotel pools. However, even though regulations are important, they can only be effective when accepted by the pool managers and users. The aim of this study was to (a) assess the acceptability of the regulations by hotel managers; (b) assess compliance during the summer touristic periods in 2020 and 2021; and (c) identify challenges during the implementation of the regulations that need to be addressed. Several non-compliances from the regulations were identified. Continuous chlorination with an automatic chlorinator was not a regular practice and suggested water circulation rates were poorly met. The microbiological and chemical testing frequency of the pool water and keeping the number of bathers allowed per surface area in the pool were reported as the most difficult requirements to meet. Most pool managers agreed that new measures contribute to the pool users’ safety; however, they reported increased cost as the main impediment for their implementation. The modernization of regulations governing swimming pools’ function, in terms of risk assessment orientation, will contribute to the adoption of an integrated compliance strategy on emerging health issues, such as COVID-19.
Joint Effects of the DEM Resolution and the Computational Cell Size on the Routing Methods in Hydrological Modelling
Authors: Li Jingjing , Chen Hua , Xu Chong-Yu , Li Lu , Zhao Haoyuan , Huo Ran , Chen Jie
Published: March 2022

Natural disasters, including droughts and floods, have caused huge losses to mankind. Hydrological modelling is an indispensable tool for obtaining a better understanding of hydrological processes. The DEM-based routing methods, which are widely used in the distributed hydrological models, are sensitive to both the DEM resolution and the computational cell size. Too little work has been devoted to the joint effects of DEM resolution and computational cell size on the routing methods. This study aims to study the joint effects of those two factors on discharge simulation performance with two representative routing methods. The selected methods are the improved aggregated network-response function routing method (I-NRF) and the Liner-reservoir-routing method (LRR). Those two routing methods are combined with two runoff generation models to simulate the discharge. The discharge simulation performance is evaluated under the cross combination of four DEM resolutions (i.e., 90 m, 250 m, 500 m, and 1000 m) and fifty-six computational cell sizes (ranging from 5 arc-min to 60 arc-min). Eleven years of hydroclimatic data from the Jianxi basin (2000–2010) and the Shizhenjie basin (1983–1993) in China are used. The results show that the effects of the DEM resolution and the computational cell size are different on the I-NRF method and the LRR method. The computational cell size has nearly no influence on the performance of the I-NRF methods, while the DEM resolution does. On the contrary, the LRR discharge simulation performance decreases with oscillating values as the computational cell size increases, but is hardly affected by the DEM resolution. Furthermore, the joint effects of the DEM resolution and the computational cell size can be ignored for both routing methods. The results of this study will help to establish the appropriate DEM resolution, computational cell size, and routing method when researchers build hydrological models to predict future disasters.
Nitrate Water Contamination from Industrial Activities and Complete Denitrification as a Remediation Option
Authors: Moloantoa Karabelo M. , Khetsha Zenzile P. , van Heerden Esta , Castillo Julio C. , Cason Errol D.
Published: March 2022

Freshwater is a scarce resource that continues to be at high risk of pollution from anthropogenic activities, requiring remediation in such cases for its continuous use. The agricultural and mining industries extensively use water and nitrogen (N)-dependent products, mainly in fertilizers and explosives, respectively, with their excess accumulating in different water bodies. Although removal of NO3 from water and soil through the application of chemical, physical, and biological methods has been studied globally, these methods seldom yield N2 gas as a desired byproduct for nitrogen cycling. These methods predominantly cause secondary contamination with deposits of chemical waste such as slurry brine, nitrite (NO2), ammonia (NH3), and nitrous oxide (N2O), which are also harmful and fastidious to remove. This review focuses on complete denitrification facilitated by bacteria as a remedial option aimed at producing nitrogen gas as a terminal byproduct. Synergistic interaction of different nitrogen metabolisms from different bacteria is highlighted, with detailed attention to the optimization of their enzymatic activities. A biotechnological approach to mitigating industrial NO3 contamination using indigenous bacteria from wastewater is proposed, holding the prospect of optimizing to the point of complete denitrification. The approach was reviewed and found to be durable, sustainable, cost effective, and environmentally friendly, as opposed to current chemical and physical water remediation technologies.
Optimal Pressure Management in Water Distribution Systems: Efficiency Indexes for Volumetric Cost Performance, Consumption and Linear Leakage Measurements
Authors: Mathye Risimati Patrick , Scholz Miklas , Nyende-Byakika Stephen
Published: March 2022

Water is a finite resource and should be given the attention it deserves to reduce its depletion through leakages in pipe systems. The authors implemented pressure management strategies linked to fixed and variable discharge (FAVAD), the burst and background estimate (BABE), and orifice principle methodologies to analyze a two-phased comparative method for applying optimal pressure management and its efficiency indexes in measuring volumetric cost performance, consumption, leakage flowrate, linear leakage reduction, infrastructural leakage and leakage cost indices. Using time-modulated smart control pressure reducing valve (PRV) simulation processes, the authors selected Alexandra Township in Johannesburg, South Africa as a case study. The results showed a reduction in head pressure, a reduction in the system input volume (SIV) from 26,272,579 m3 to 21,915,943 m3 and a reduction in minimum night flow (MNF) from 14.01% to 12.50%. The annual estimated nodal system output (NSO) was reduced from 14,774.62 m3 to 12,787.85 m3. The monthly average linear system repairs were reduced from 246 to 177, while the efficiency index percentages of leakage frequency/km/pressure were reduced from 8.31% to 5.98%. At a unit cost of $3.18/m3, the cost of leakages declined from $4,009,315.54 to $2,862,053.10 per month, while average household consumption (AMC) reduced from 36.33 m3 to 24.56 m3. Finally, the linear reduction value R2 for the percentage of the total leakage flowrate (TLFR)/SIV declined from 0.58 to 0.5, whereas the infrastructure leakage ratio (ILI) increased from 4 to 4.3. The results fully demonstrated that optimal pressure management is an alternative way to simulate, estimate, quantify and understand where and how water is lost in a distribution system. The authors propose that the implementation of proactive leakage management and domestic background leakage repair could further assist in reducing the frequency and cost of water leakages.
The views of GPs about using sit–stand desks: an observational study
Authors: Biddle Gregory J H , Thomas Nicholas , Edwardson Charlotte L , Clemes Stacy A , Daley Amanda J
Published: March 2022

Background

Occupational sitting is associated with negative health outcomes. Sit–stand workstations have been shown to reduce sitting time in office workers, although there is no evidence on whether this change to practice would be acceptable to GPs.

Aim

To investigate GPs views about the use of sit–stand desks within general practice and the potential impact they may have on the nature and quality of consultations with adult patients.

Design & setting

Observational study involving GPs located across the UK.

Method

An online survey was emailed to members of the Royal College of General Practitioners (RCGP) and shared on social media. Only GPs working in the UK were eligible. The survey included questions on socio-demographics, GPs views about the use of sit–stand desks within their work, their levels of physical activity, total time spent sitting at work each day, and time spent at work.

Results

14 142 surveys were sent by the RCGP to their members with 810 GPs responding, with a further 33 responding via social media. 60.6% of GPs would like a sit–stand desks in their consultation room, while 19.2% already had one. Most GPs thought sit–stand desks could be used for telephone consultations (91.9%) and administration tasks (92.3%). There was less agreement about whether they could be used during face-to-face consultations (35.0% agreed), with the potential impact on the doctor–patient relationship raised as the primary concern.

Conclusion

The implementation of sit–stand desks had support from GPs, but their possible impact on the doctor–patient relationship should be considered in future research.
Analytical Modeling of Flowrate and Its Maxima in Electrochemical Bioelectronics with Drug Delivery Capabilities
Authors: Avila Raudel , Wu Yixin , Garziera Rinaldo , Rogers John A. , Huang Yonggang
Published: March 2022

Flowrate control in flexible bioelectronics with targeted drug delivery capabilities is essential to ensure timely and safe delivery. For neuroscience and pharmacogenetics studies in small animals, these flexible bioelectronic systems can be tailored to deliver small drug volumes on a controlled fashion without damaging surrounding tissues from stresses induced by excessively high flowrates. The drug delivery process is realized by an electrochemical reaction that pressurizes the internal bioelectronic chambers to deform a flexible polymer membrane that pumps the drug through a network of microchannels implanted in the small animal. The flowrate temporal profile and global maximum are governed and can be modeled by the ideal gas law. Here, we obtain an analytical solution that groups the relevant mechanical, fluidic, environmental, and electrochemical terms involved in the drug delivery process into a set of three nondimensional parameters. The unique combinations of these three nondimensional parameters (related to the initial pressure, initial gas volume, and microfluidic resistance) can be used to model the flowrate and scale up the flexible bioelectronic design for experiments in medium and large animal models. The analytical solution is divided into (1) a fast variable that controls the maximum flowrate and (2) a slow variable that models the temporal profile. Together, the two variables detail the complete drug delivery process and control using the three nondimensional parameters. Comparison of the analytical model with alternative numerical models shows excellent agreement and validates the analytic modeling approach. These findings serve as a theoretical framework to design and optimize future flexible bioelectronic systems used in biomedical research, or related medical fields, and analytically control the flowrate and its global maximum for successful drug delivery.
I Know You by Heart: Biometric Authentication based on Electrocardiogram (ECG) signals
Authors: Lipps Christoph , Bergkemper Lea , Herbst Jan , Schotten Hans Dieter
Published: March 2022

Trust, confidence and trustworthiness are of fundamental importance in human societies, usually established and sustained through personal relationships. But, due to the globalization and ongoing interconnection of everything up to Cyber-Physical Productions Systems (CPPS) and the Internet of Everything (IoE), physical attendance is no longer necessary. (Remote) access to systems is possible from anywhere on the globe. Accompanied with the lack of personal relationship is the challenge to trust entities –humans or machines-, and proof the identity they are claiming to be. Whether it's payment transactions with smartwatches, logging in to systems, or accessing sensitive parts of buildings, the user's identity is the basic prerequisite. For human participants, for instance, the verification can be obtained through biometrics. These are distinguishable into physiological, biological and behavioral features, each characteristic but of varying difficulty to deduce them. Although using biometric features is not a new concept -indeed they are the oldest form of authentication-, modern approaches are shifting them back into focus. Improved sensor technology enables the identification of people by their gait, or to distinguish them by their characteristic gestures. This work highlights how the availability of (medical) data, and the possibilities of Artificial Intelligence (AI) contribute to the identification and authentication of humans. Therefore, Electrocardiogram (ECG) signals are recorded using a Microcontroller Unit (MCU) and ECG electrodes to derive a three-lead ECG. Using different Machine Learning (ML) algorithms: K-Nearest Neighbor (KNN), Support Vector Machines (SVM) and Gaussian Naïve Bayes (GNB); it is analyzed whether the ECG signals are able to distinguish individuals. Thereby, the ML algorithms are compared with each other, determining which one achieves the best results. The results of the evaluation indicate that ECG signals are capable to distinguish humans based on their heartbeat in such a manner that they can be used as Human - Physically Unclonable Functions (Human-PUFs). Furthermore, the results give reason to assume that the algorithms can also be used for medical applications, for example to recognize heart diseases.
Towards Detection of Selfish Mining Using Machine Learning
Authors: Peterson Matthew , Andel Todd , Benton Ryan
Published: March 2022

Selfish mining is an attack against a blockchain where miners hide newly discovered blocks instead of publishing them to the rest of the network. The selfish miners continue to mine on their private chain while the honest miners waste resources mining on a shorter chain. According to the blockchain protocol, a longer chain takes precedent and shorter chains are discarded which allows the selfish miners to gain an advantage by keeping their chain secret. This attack can be used by malicious miners to earn a disproportionate share of the mining rewards or in conjunction with other attacks to steal money from cryptocurrency exchanges. Several of these attacks were launched in 2018 and 2019 with the attackers stealing as much as $18 Million. Developers made several different attempts to fix this issue, but the effectiveness of the fixes is currently unknown. Although this attack is possible against both Proof-of-Work and Proof-of-Stake blockchains, this research concentrates on detection in Proof-of-Work blockchains. As is difficult to evaluate security advances in the real-time blockchain, it is imperative to focus on simulation to evaluate blockchain security properties. To this end, we extend a blockchain simulator and add the ability to simulate selfish mining attacks. Several existing simulators are examined before choosing SimBlock for this research. Our goal is to identify the factors that identify selfish mining. Using existing research, we choose several factors that could identify an attack in an unlaunched state, an active state, or historically. We plan to use simulated data to train a machine learning model to detect selfish mining. Using the modified simulator, we generate training and test data for unlaunched and active attacks. For historical attacks, we will use historical data from known selfish mining attacks. While some existing research has examined the detection of selfish mining, it only examines active attacks. In this paper, we seek to lay the groundwork for future research into detecting attacks that are unlaunched, active, or historical.
Identifying Adversaries’ Signatures Using Knowledge Representations of Cyberattack Techniques on Cloud Infrastructure
Authors: Van der Merwe Gilliam , Muller Christian , Van der Merwe Wilhelm , Blaauw Dewald
Published: March 2022

Advanced Persistent Threats (APTs) have increased in parallel to growing cloud infrastructure and cloud Software-as-a-Service (SaaS) needs, exposing new vulnerabilities within the cloud environment. Moreover, APT groups are becoming more sophisticated and organised which needs to be addressed by the research community to enable faster response and more importantly, prevent threats within the domain. The MITRE ATT&CK Cloud framework offers one of the leading structured inventories within this context. Our research is to expose patterns and signatures of a select group of APT’s on the MITRE Cloud Framework by using Formal Concept Analysis (FCA) to construct a “lattice graph” and an ontology. The goal is to develop a better conceptualisation of the MITRE ATT&CK Cloud Matrix framework for cyber security experts to be able to proactively act upon adversary techniques. The MITRE ATT&CK framework was retrieved, cleaned, and pre-processed to construct the lattice and ontology using data cleaning methods, FCA tools such as Concept Explorer, and the Web Ontology Language (OWL), with additional symbolic reasoning and inference generation. This resulted in knowledge representations/graphs, which are highly efficient representations of this knowledge field. The underlying linkages between techniques and targets specific to those APTs are further exposed and enriched and presented visually and integrated into the ontology. The ontology gives formalisation to associations and implications between techniques, tactics, and APTs – enabling cyber security practitioners to forecast potential targets and techniques based on their scenario, but also to attribute certain technique patterns and signatures to individual APTs. Cyber security practitioners can query from this knowledge graph and formulate strategic proactive measures. From these findings, the applications and constraints of the APTs’ cyber-attack techniques and their associated patterns were determined. The findings provide a guideline for future additional research in the field of AI knowledge representation in cybersecurity, as well as highlighting certain limitations in this field of research.
Education and Training Against Threat of Phishing Emails
Authors: Burita Ladislav , Klaban Ivo , Racil Tomas
Published: March 2022

The research results published in this article are oriented toward two areas: phishing email analysis and education for defense against the threats of phishing emails. The first topic builds on previous research primarily by analyzing changes in captured phishing emails over an interval of 4 weeks, half a year after the previous experiment. In this section, a statistical survey of phishing emails from both experiments is carried out and emails are segmented into categories focused on business, charity, asset transfer, and fund offers. The results of both experiments are then compared and validated. Based on this comparison and validation, a conclusion is made on trends and development in the phishing email domain in the last half a year. The second focus of our research is analysis of the existing education and testing systems for phishing emails. Based on the results of the analysis, a suitable system for university education and training against phishing and other malicious email threats will be designed. There is also an analysis of existing systems for improving and testing users' ability to recognize and react to phishing emails. Based on our findings about these systems, our own system is proposed. An experiment is prepared on "self-service" testing of phishing email detection skills performed by students with their colleagues. Some activists were employed to assist with this experiment; they will operate and prepare the environment according to the processed scenario. All experiments must be completely safe and effective at the same time. The experiments will be evaluated and the experience used to develop the education and training system at the university.
Social Media Privacy Using EDEE Security Model
Authors: Yankson Benjamin
Published: March 2022

Social Media platforms have become a significant part of our daily lives and a modern way to connect friends and family, document our lives, and share other great personal information about our lives. These activities leave us vulnerable to privacy and security breach due to lapse security controls necessary to protect users' sensitive data on these platforms. We conducted exploratory privacy and security analysis on paramount social media platforms such as Facebook and Snapchat and determined that current Social Media privacy and security posture insufficient and proposed Social Media platform Security through “Educate,” “Determine,” “Enable,” and “Evaluate” (EDEE) Security model to address the evolving Social Media platform security as a growing concern in Cybersecurity for individual using the platform and companies hosting the platform.
Ransomware Detection using Process Memory
Authors: Singh Avinash , Ikuesan Richard , Venter Hein
Published: March 2022

Ransomware attacks have increased significantly in recent years, causing great destruction and damage to critical systems and business operations. Attackers are unfailingly finding innovative ways to bypass detection mechanisms, which encouraged the adoption of artificial intelligence. However, most research summarizes the general features of AI and induces many false positives, as the behavior of ransomware constantly differs to bypass detection. Focusing on the key indicating features of ransomware becomes vital as this guides the investigator to the inner workings and main function of ransomware itself. By utilizing access privileges in process memory, the main function of the ransomware can be detected more easily and accurately. Furthermore, new signatures and fingerprints of ransomware families can be identified to classify novel ransomware attacks correctly. The current research used the process memory access privileges of the different memory regions of the behavior of an executable to quickly determine its intent before serious harm can occur. To achieve this aim, several well-known machine learning algorithms were explored with an accuracy range of 81.38% – 96.28%. The study thus confirms the feasibility of utilizing process memory as a detection mechanism for ransomware.
Analysis of Image Thresholding Algorithms for Automated Machine Learning Training Data Generation
Authors: Creek Tristan , Mullins Barry
Published: March 2022

Secured compounds often safeguard physical layout details of both internal and external facilities, but these details are at risk due to the growing inclusion of Light Detection and Ranging (LiDAR) sensors in consumer off-the-shelf (COTS) technology such as cell phones. The ability to record detailed distance data with cell phones facilitates the production of high-quality three-dimensional scans in a discrete manner which directly threatens the security of private compounds. Therefore, it behooves the organizations in charge of private compounds to detect LiDAR activity. Many security cameras already detect LiDAR sources as generic light sources in specific conditions, but further analysis must identify these light sources as LiDAR sources in order to alert an organization of a potential security incident. Testing confirms the feasibility of identifying some LiDAR sources based on the color and intensity of light shined directly into a camera sensor, but this analysis proves inadequate for cell phone LiDAR. However, the unique intensity and pattern characteristics of cell phone LiDAR reflected off a surface can potentially be identified by an object identification machine learning model. In order to train a model to identify a LiDAR object, we must first produce a training dataset containing marked and labelled LiDAR objects. To do this, we apply an image thresholding algorithm to isolate the LiDAR object in an image to calculate its bounding box. The image thresholding algorithm directly affects the bounding box accuracy, so we test two different algorithms and find that Otsu’s image thresholding algorithm performs best, resulting in 99.5% accurate bounding boxes.
Ensuring the Security of Space Systems from Eavesdropping Attacks
Authors: Richardson Caleb , Reith Mark , Henry Wayne
Published: March 2022

In a day and age where satellite communications are more important than ever to ensure global communication, establish international military power, and support our everyday way of life, satellite security must be at the forefront of innovation. However, eavesdropping attacks pose a serious threat to satellite communication systems that have not been adequately addressed. An eavesdropping attack threatens to put sensitive data in the wrong hands or even jeopardize critical missions. Research is needed to explore why defense against eavesdropping attacks is crucial, particularly for satellite systems. Three potential solutions to the problem are presented, addressing different challenges. Realistic solutions to the eavesdropping threat are needed urgently to defend the space domain from malicious threats.
New Wave Cyber Attacks
Authors: Mison Angela , Davies Gareth , Eden Peter
Published: March 2022

There is increasing enthusiasm for, and recognition of, the benefits that artificial intelligence (AI) can provide to society.  The emphasis has been on the positive, but AI and deep learning can be used for negative purposes. Modular Neural Networks (MNN) are capable of independent learning and have been targeted at evolutionary, complex financial systems.  If the goal of an MNN were to be defined as system penetration, there is no reason why an algorithm could not run in the background.  There are a resource requirements, but organised crime groups, technology companies, nation states and individuals with a curious bent are all capable of such.  Ordered society and security requires a degree of certainty that systems on which society depends will remain recognisable, dependable and resilient.   Under current conditions, security is difficult enough.  It is suggested that limitations may be required before release of certain AI systems, in the knowledge of their potential for detriment to society. An AI system capable of independent learning, permits undefined emergent behaviours.  That the results of any emergent properties may be benign or malign is irrelevant.  Scientific history is littered with developments whose uses were redirected away from the benign.Such concern could be interpreted as fear of the unknown, standing in the way of technological advances.  Unless society wishes to become machine-driven, the power and control of systems should be defined and limited by society, not accidently sprung on humanity or based on a ruthless logic that may drive a system to  an unacceptable conclusion. Currently there are sophisticated botnet forming methods ensuring botnet persistence.   If combined with the concepts of AI, there is a possibility that botnets could exist in perpetuity, with no one able to predict emergence, and no time limits on evolution.  Whither cyber defence in the face of the unstoppable, increasingly intelligent, goal directed systems?
Role of Big Tech in Future Cyber Defence
Authors: Mison Angela , Davies Gareth , Eden Peter
Published: March 2022

Ordered society and nation states are dependent on interconnected systems, the defence of which is largely in private hands whose actions are driven by need for oligopolistic market dominance, protection of assets, and their monetisation models.  This paper queries the responsibility of the nation state for the protection of itself and its citizenry.  By some definitions, corporations are conducting cyberwarfare and, in cyberspace, are virtual nation states with ownership and rights over the data they hold and the intelligence it yields. The financial challenge for market dominance could drive an internecine war among the major technology corporations, and an assertion that the rights over the data they control are superior to those of the nation state.  As functional monopolists, data they have acquired is not available from any other source.  The intelligence from analytics exercised over that data, and the data itself is proprietary.  These corporations exercise monopolist characteristics in the areas of data, information and intelligence. The aggregate value of the top 5 technology corporations, colloquially known as Big Tech is equivalent to third in projected global GDP rankings for 2021.  This represents an equivalent expression of power in/over cyberspace.  Cloud service providers (CSP) are often offshoots of Big Tech and have a high compound annual growth rate, thereby revealing the motivation for protection of market dominance and potential threat to user/customers.  By concentrating on traditional cyber warfare and defence, there is limited consideration on policing or guarding against the rise of these virtual supranational powers driven by strict market agenda.   What consideration there is regarding potential threats is driven by an economic perspective and anti-trust initiatives.  Whether judged by the nation state as benign or malign, Big Tech has an impact on the nature and direction of society as currently understood and the question must be raised whether both citizens, organisations, and states need protection from it.
Data Mining for the Security of Cyber Physical Systems Using Deep-Learning Methods
Authors: Nath Bhagawan , Hamaleinen Timo , Ezekiel Soundararajan
Published: March 2022

Cyber Physical Systems (CPSs) have become widely popular in recent years, and their applicability have been growing exponentially. A CPS is an advanced system that incorporates a computation unit along with a hardware unit, allowing for computing processes to interact with the physical world. However, this increased usage has also led to the security concerns in them, as they allow potential attack vendors to exploit the possibilities of committing misconduct for their own benefit. It is of paramount importance that these systems have comprehensive security mechanisms to mitigate these security threats. A typical attack vector for a CPS is malicious data supplied by compromised sensors that are part of the CPSs. To combat this attack vector, many systems are secured through fault tolerance, including methods such as checkpointing to recover the system. Looking at the diverse nature of attacks and their ever growing complexities, traditional security approaches may not counter them efficiently, which creates a vacuum to be filled with sophisticated state-of-the-art techniques. In this paper, Deep Learning methods such as autoencoders, and Support Vector Machines are proposed to secure CPSs against these attacks. The networks in these applied methods are trained with a normal data profile devoid of any malicious data. Data collected from the system’s sensors at specified intervals is used to form a data series and input to the neural networks. The networks compare and analyze new data to the normal profile to detect anomalies, if there is any. In the presence of anomalous data, the networks generate corrective action(s) for these sensors and the physical states they are recording. Through detection of anomalies, effective security of CPSs may be improved in addition to providing protection for the sensors. Moreover, the proposed method of securing CPSs opens up the possibility of further research by showcasing the applicability of neural networks in securing CPSs.
Prácticas de masculinidad de los jornaleros migrantes desde la narrativa de las mujeres: discriminación, trabajo, paternidad y pareja
Authors: Molina-Rodríguez Nancy Elizabeth
Published: March 2022

The article focused on women's narratives, from their role as partners and daughters accompanying male migrant day laborers engaged in sugarcane cutting. The objective was to explore masculinity practices concerning discrimination, work, partnership, and paternity; during their stay in the migrant shelter in Colima, Mexico. The method was qualitative. The data collection techniques were: focused interview based on an interview script and participant observation in the migrant shelter through the systematic recording of the field diary. The participants were seven women between 20 and 35 years of age. For the data analysis, we elaborated the following categories: characterization of the context and the migrant day laborers, discrimination, work, partnership, and paternity. The results show discrimination that migrant day laborers are subject to by the host community since they belong to indigenous, migrant, and impoverished peoples. In addition, they point out how social structures construct masculinity practices involving the malleability of their bodies to adjust to social norms of gender, race, ethnicity and socioeconomic status, being regarded as "destined" to strenuous physical labor and resistant to long working hours, which is related to the fact that they are perceived as absent figures such as partners and fathers in the family dynamics.
Retos y desafíos del mundo del trabajo. La erosión de la membresía sindical
Authors: Zepeda-Martínez Roberto , Sánchez-Sánchez Ernesto
Published: March 2022

This article analyzes the relevance of unionism based on the fluctuations of unionization in OECD member countries and some Latin American countries, as well as its impact on contemporary Mexico. The origins of unions as essential organizations for the defense of the labor rights of unionized workers are examined, as well as their role as political actors. In addition, union density is described and analyzed to measure the power and influence of unions in the political and labor spheres. Fluctuations in union density in a group of OECD countries between 1980 and 2018 are taken into account. This allows us to observe variations in union density over the period of neoliberal globalization and to determine which countries have experienced booms and busts in unionization rates.
Recursos sociales y familiares según la experiencia con la migración en jóvenes universitarios mexicanos
Authors: Obregón-Velasco Nydia , Rivera-Heredia María Elena , Padrós-Blazquez Ferrán
Published: March 2022

The objective was to analyze the family and social resources of Mexican university students according to the type of experience with migration, be it their own and / or that of a family member to support the design of interventions. It is a quantitative study of correlational type of comparison of groups. 4,284 students from 15 schools of Mexican Higher Education institutions participated, who answered different scales to evaluate their social and family resources. Significant differences were found in three subdimensions of social resources: initiative F (2,923) p (.003), support network F (2,170) p (.027) and total social resources F (1,437) p (.175). And in two subdimensions of family resources: family expression F (1.259) p (.260) and family difficulties F (3.70) p (.001), the differences are discussed emphasizing their usefulness for the design of specific interventions that take into account the type of experience with migration that has been lived.
Desarrollo de herramientas tecnológicas didácticas para la implementación de un programa de educación ambiental no formal
Authors: Gómez-Márquez Clara Alicia , Figueroa-Ayala Lorena , García-Azpeitia Lilia
Published: March 2022

In Mexico, environmental problems require the intervention of all sectors of society, higher education institutions are an important axis to develop a sustainable society aware of environmental care. The educational community is undoubtedly one of the key sectors in contributing to the improvement of environmental quality, both because of its responsibility in educating citizens and because of the influence it has on the rest of society. Technological tools are new forms of communication and information in both public and private spheres, and must be integrated into educational systems as a didactic resource choice, which is why this document presents a project to use technological tools as part of a non-formal environmental education program, which will serve as a means of learning for environmental awareness and education that allow the regional conservation of the thorny ecosystem, mainly of the reserve that is located in the Mario Molina Technological University Lagos de Moreno Academic Unit. For the selection of the material that the program will contain, 68 children between 6 and 15 years old were surveyed, of which 84% do not know that it is a nature reserve and 90% are unaware of the ecological reserves of Lagos de Moreno; That is why as part of this non-formal environmental education program, one of the tools generated was the design and development of a web page that serves as an environmental education center for the ecological reserve, where it is intended that the Laguense population know the only endemic ecosystem of this municipal head. Another tool that was carried out was the development of didactic software represented by games aimed at obtaining knowledge of the flora and fauna that exist in the thorny forest of the ecological reserve of the Technological Mario Molina Lagos de Moreno Academic Unit, as well as the diversity of other thorn forests in other regions and with this tool strengthen the learning and awareness process about thorn ecosystems.
Escala para medir la significación del pensamiento complejo en entornos virtuales de aprendizaje de instituciones de educación superior
Authors: Avendaño-Porras Víctor del Carmen , Bohórquez-Olaya Claudia Inés Bohórquez-Olaya , Lara-Buitagro Paola Andrea
Published: March 2022

The concept of complex thought in Higher Education Institutions has been little addressed and understood, since it is still in the intellectual imagination of educational actors that it is a theoretical construct with which problems of formal sciences can be solved, more not of the factual ones; If we add to this that during the Covid-19 pandemic Virtual Learning Environments had to be used with much greater intensity, the challenge of perceiving the concept and merging it with ICTs for the construction of meta-complex virtual environments turns out to be a challenge. The main objective of this document was to develop an instrument to measure the significance of complex thinking in Virtual Learning Environments in Higher Education Institutions; For this purpose, the instrumental methodology of Hernández (2011) was used, applying the validation of experts, using the focus group method, where a value of .77 was obtained, which is considered a good degree of validity. Reliability was also analyzed using Cronbach's Alpha, which showed a value of .780, which is why it is considered adequate. The questionnaire is made up of 63 items, 57 of which are integrated using a 4-level Likert scale; Therefore, it is considered to be a valid and reliable instrument. The instrument proposes to know precisely the relationship that exists between the concept of complex thought and virtuality and how these are merged by educational actors.
Las mujeres y el cine de guerra en Irán
Authors: Gonzalez Guerrero Alejandra
Published: March 2022

El cine de guerra es el único género que la República Islámica de Irán usa como medio de propaganda oficial, pues la Guerra Irán-Iraq tuvo gran importancia en la formación de la idea de nación y en la consolidación del régimen mismo; pero fue hasta finales de la década de los 90 que las mujeres empezaron a tener personajes de importancias en estos filmes y hasta 2005 se filmó una película del tema dirigido por una mujer. La memoria colectiva sólo es efectiva si hay diversas de voces y se recuerda desde distintas perspectivas. Los filmes con mujeres protagonistas y dirigidos por mujeres hablan de las vivencias femeninas y recuerdan que las guerras no sólo las viven los soldados ni se acaban con los armisticios.