Search results for: vulnerability intelligence

Authors: Aishi Kundu, Elhan Raza

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"Necessity is the mother of invention" - Rapid increase in the global human population has directed the agricultural domain toward machine learning. The basic need of human beings is considered to be food which can be satisfied through farming. Farming is one of the major revenue generators for the Indian economy. Agriculture is not only considered a source of employment but also fulfils humans’ basic needs. So, agriculture is considered to be the source of employment and a pillar of the economy in developing countries like India. This paper provides a brief review of the progress made in implementing Machine Learning in the agricultural sector. Accurate predictions are necessary at the right time to boost production and to aid the timely and systematic distribution of agricultural commodities to make their availability in the market faster and more effective. This paper includes a thorough analysis of various machine learning algorithms applied in different aspects of agriculture (crop management, soil management, water management, yield tracking, livestock management, etc.).Due to climate changes, crop production is affected. Machine learning can analyse the changing patterns and come up with a suitable approach to minimize loss and maximize yield. Machine Learning algorithms/ models (regression, support vector machines, bayesian models, artificial neural networks, decision trees, etc.) are used in smart agriculture to analyze and predict specific outcomes which can be vital in increasing the productivity of the Agricultural Food Industry. It is to demonstrate vividly agricultural works under machine learning to sensor data. Machine Learning is the ongoing technology benefitting farmers to improve gains in agriculture and minimize losses. This paper discusses how the irrigation and farming management systems evolve in real-time efficiently. Artificial Intelligence (AI) enabled programs to emerge with rich apprehension for the support of farmers with an immense examination of data.

Keywords: machine Learning, artificial intelligence, crop management, precision farming, smart farming, pre-harvesting, harvesting, post-harvesting

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Authors: Ani Wilson Uchenna, Ogbonna Chikodi

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This study examined the impact of artificial intelligence driven applications on banks’ performance in West Africa using Nigeria and Ghana as case studies. Specifically, the study examined the extent to which deployment of smart automated teller machine impacts the banks’ net worth within the reference period in Nigeria and Ghana. It ascertained the impact of point of sale on banks’ net worth within the reference period in Nigeria and Ghana. Thirdly, it verified the extent to which webpay services can influence banks’ performance in Nigeria and Ghana and finally, determined the impact of mobile pay services on banks’ performance in Nigeria and Ghana. The study used automated teller machine (ATM), Point of sale services (POS), Mobile pay services (MOP) and Web pay services (WBP) as proxies for explanatory variables while Bank net worth was used as explained variable for the study. The data for this study were sourced from central bank of Nigeria (CBN) Statistical Bulletin as well as Bank of Ghana (BoGH) Statistical Bulletin, Ghana payment systems oversight annual report and world development indicator (WDI). Furthermore, the mixed order of integration observed from the panel unit test result justified the use of autoregressive distributed lag (ARDL) approach to data analysis which the study adopted. While the cointegration test showed the existence of cointegration among the studied variables, bound test result justified the presence of long-run relationship among the series. Again, ARDL error correction estimate established satisfactory (13.92%) speed of adjustment from long run disequilibrium back to short run dynamic relationship. The study found that while Automated teller machine (ATM) had statistically significant impact on bank net worth (BNW) of Nigeria and Ghana, point of sale services application (POS) statistically and significantly impact on bank net worth within the study period, mobile pay services application was statistically significant in impacting the changes in the bank net worth of the countries of study while web pay services (WBP) had no statistically significant impact on bank net worth of the countries of reference. The study concluded that artificial intelligence driven application have significant an positive impact on bank performance with exception of web pay which had negative impact on bank net worth. The study recommended that management of banks both in Nigerian and Ghanaian should encourage more investments in AI-powered smart ATMs aimed towards delivering more secured banking services in order to increase revenue, discourage excessive queuing in the banking hall, reduced fraud and minimize error in processing transaction. Banks within the scope of this study should leverage on modern technologies to checkmate the excesses of the private operators POS in order to build more confidence on potential customers. Government should convert mobile pay services to a counter terrorism tool by ensuring that restrictions on over-the-counter withdrawals to a minimum amount is maintained and place sanctions on withdrawals above that limit.

Keywords: artificial intelligence (ai), bank performance, automated teller machines (atm), point of sale (pos)

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Authors: Mohammed Seyam, Faridah Othman, Ahmed El-Shafie

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The early warning of high stream flow events (HSF) and floods is an important aspect in the management of surface water and rivers systems. This process can be performed using either process-based models or data driven-based models such as artificial intelligence (AI) techniques. The main goal of this study is to develop efficient AI-based model for predicting the real-time hourly stream flow (Q) and apply it as early warning tool of HSF and floods in the downstream area of the Selangor River basin, taken here as a paradigm of humid tropical rivers in Southeast Asia. The performance of AI-based models has been improved through the integration of the lag time (Lt) estimation in the modelling process. A total of 8753 patterns of Q, water level, and rainfall hourly records representing one-year period (2011) were utilized in the modelling process. Six hydrological scenarios have been arranged through hypothetical cases of input variables to investigate how the changes in RF intensity in upstream stations can lead formation of floods. The initial SF was changed for each scenario in order to include wide range of hydrological situations in this study. The performance evaluation of the developed AI-based model shows that high correlation coefficient (R) between the observed and predicted Q is achieved. The AI-based model has been successfully employed in early warning throughout the advance detection of the hydrological conditions that could lead to formations of floods and HSF, where represented by three levels of severity (i.e., alert, warning, and danger). Based on the results of the scenarios, reaching the danger level in the downstream area required high RF intensity in at least two upstream areas. According to results of applications, it can be concluded that AI-based models are beneficial tools to the local authorities for flood control and awareness.

Keywords: floods, stream flow, hydrological modelling, hydrology, artificial intelligence

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Authors: Mehrzad Soltani, Pegah Rezaei

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In response to the escalating challenges posed by resource scarcity, urban congestion, and the dearth of green spaces, contemporary urban areas have undergone a remarkable transformation into smart cities. This evolution necessitates a strategic and forward-thinking approach to urban development, with the primary objective of diminishing and eventually eradicating dependence on non-renewable energy sources. This steadfast commitment to sustainable development is geared toward the continual enhancement of our global urban milieu, ensuring a healthier and more prosperous environment for forthcoming generations. This transformative vision has been meticulously shaped by an extensive research framework, incorporating in-depth field studies and investigations conducted at both neighborhood and city levels. Our holistic strategy extends its purview to encompass major cities and states, advocating for the realization of exceptional development firmly rooted in the principles of sustainable intelligence. At its core, this approach places a paramount emphasis on stringent pollution control measures, concurrently safeguarding ecological equilibrium and regional cohesion. Central to the realization of this vision is the widespread adoption of environmentally friendly materials and components, championing the cultivation of plant life and harmonious green spaces, and the seamless integration of intelligent lighting and irrigation systems. These systems, including solar panels and solar energy utilization, are deployed wherever feasible, effectively meeting the essential lighting and irrigation needs of these dynamic urban ecosystems. Overall, the transformation of urban areas into smart cities necessitates a holistic and innovative approach to urban development. By actively embracing sustainable intelligence and adhering to strict environmental standards, these cities pave the way for a brighter and more sustainable future, one that is marked by resilient, thriving, and eco-conscious urban communities.

Keywords: smart city, green urban, sustainability, urban management

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Authors: Tai Mei Kin, Omar Abdull Kareem

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In light of globalization, educational reform has become a top priority for many countries. However, the task of leading change effectively requires a multidimensional set of competencies. Over the past two decades, technical competencies of principal change leadership have been extensively analysed and discussed. Comparatively, little research has been conducted in Malaysian education context on non-technical competencies or popularly known as emotional intelligence, which is equally crucial for the success of change. This article provides a validation of the Principal Change Leadership Non-Technical Competencies (PCLnTC) Scale, a tool that practitioners can easily use to assess school principals’ level of change leadership non-technical competencies that facilitate change and maximize change effectiveness. The overall coherence of the PCLnTC model was constructed by incorporating three theories: a)the change leadership theory whereby leading change is the fundamental role of a leader; b)competency theory in which leadership can be taught and learned; and c)the concept of emotional intelligence whereby it can be developed, fostered and taught. An exploratory factor analysis (EFA) was used to determine the underlying factor structure of PCLnTC model. Before conducting EFA, five important pilot test approaches were conducted to ensure the validity and reliability of the instrument: a)reviewed by academic colleagues; b)verification and comments from panel; c)evaluation on questionnaire format, syntax, design, and completion time; d)evaluation of item clarity; and e)assessment of internal consistency reliability. A total of 335 teachers from 12 High Performing Secondary School in Malaysia completed the survey. The PCLnTCS with six points Liker-type scale were subjected to Principal Components Analysis. The analysis yielded a three-factor solution namely, a)Interpersonal Sensitivity; b)Flexibility; and c)Motivation, explaining a total 74.326 per cent of the variance. Based on the results, implications for instrument revisions are discussed and specifications for future confirmatory factor analysis are delineated.

Keywords: exploratory factor analysis, principal change leadership non-technical competencies (PCLnTC), interpersonal sensitivity, flexibility, motivation

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Authors: Muhammad Tariq A. Chaudhary

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The tremendous loss of life that resulted in the aftermath of recent earthquakes in developing countries is mostly due to the collapse of non-engineered and semi-engineered building structures. Such structures are used as houses, schools, primary healthcare centres and government offices. These building are classified structurally into two categories viz. non-engineered and semi-engineered. Non-engineered structures include: adobe, Unreinforced Masonry (URM) and wood buildings. Semi-engineered buildings are mostly low-rise (up to 3 story) light concrete frame structures or masonry bearing walls with reinforced concrete slab. This paper presents an overview of the typical damage observed in non-engineered structures and their most likely causes in the past earthquakes with specific emphasis on the performance of such structures in the 2005 Kashmir earthquake. It is demonstrated that seismic performance of these structures can be improved from life-safety viewpoint by adopting simple low-cost modifications to the existing construction practices. Incorporation of some of these practices in the reconstruction efforts after the 2005 Kashmir earthquake are examined in the last section for mitigating seismic risk hazard.

Keywords: Kashmir earthquake, non-engineered buildings, seismic hazard, structural details, structural strengthening

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Authors: Timothy Kephart

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Many law enforcement officials believe that gangs communicate messages to both the community and to rival gangs through graffiti. Some social scientists have documented this as well, however no recent research has examined gang graffiti for its underlying meaning. Empirical research on gang graffiti and gang communication through graffiti is limited. This research can be described as an exploratory effort to better understand how, and perhaps why, gangs employ this medium for communication. Furthermore this research showcases how law enforcement agencies can utilize this hidden form of communication to better direct resources and impact gang violence.

Keywords: gangs, graffiti, juvenile justice, policing

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Authors: Yunbin Deng

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Most existing applications of automatic speech recognition relies on cooperative subjects at a short distance to a microphone. Standoff speech recognition using microphone arrays can extend the subject to sensor distance somewhat, but it is still limited to only a few feet. As such, most deployed applications of standoff speech recognitions are limited to indoor use at short range. Moreover, these applications require air passway between the subject and the sensor to achieve reasonable signal to noise ratio. This study reports long range (50 feet) automatic speech recognition experiments using a Laser Doppler Vibrometer (LDV) sensor. This study shows that the LDV sensor modality can extend the speech acquisition standoff distance far beyond microphone arrays to hundreds of feet. In addition, LDV enables 'listening' through the windows for uncooperative subjects. This enables new capabilities in automatic audio and speech intelligence, surveillance, and reconnaissance (ISR) for law enforcement, homeland security and counter terrorism applications. The Polytec LDV model OFV-505 is used in this study. To investigate the impact of different vibrating materials, five parallel LDV speech corpora, each consisting of 630 speakers, are collected from the vibrations of a glass window, a metal plate, a plastic box, a wood slate, and a concrete wall. These are the common materials the application could encounter in a daily life. These data were compared with the microphone counterpart to manifest the impact of various materials on the spectrum of the LDV speech signal. State of the art deep neural network modeling approaches is used to conduct continuous speaker independent speech recognition on these LDV speech datasets. Preliminary phoneme recognition results using time-delay neural network, bi-directional long short term memory, and model fusion shows great promise of using LDV for long range speech recognition. To author’s best knowledge, this is the first time an LDV is reported for long distance speech recognition application.

Keywords: covert speech acquisition, distant speech recognition, DSR, laser Doppler vibrometer, LDV, speech intelligence surveillance and reconnaissance, ISR

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Authors: Warnakulasuriya Arachichige Malisha Ann Rosary Fernando, Udalamatta Gamage Omila Chalanka Jinadasa, Bihini Pabasara Amandi Amarasinghe, Manul Thisuraka Mandalawatta, Uthpala Samarakoon, Manori Gamage

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The surge in sexually transmitted diseases (STDs) has become a critical public health crisis demanding urgent attention and action. Like many other nations, Sri Lanka is grappling with a significant increase in STDs due to a lack of education and awareness regarding their dangers. Presently, the available applications for tracking and managing STDs cover only a limited number of easily detectable infections, resulting in a significant gap in effectively controlling their spread. To address this gap and combat the rising STD rates, it is essential to leverage technology and data. Employing technology to enhance the tracking and management of STDs is vital to prevent their further propagation and to enable early intervention and treatment. This requires adopting a comprehensive approach that involves raising public awareness about the perils of STDs, improving access to affordable healthcare services for early detection and treatment, and utilizing advanced technology and data analysis. The proposed mobile application aims to cater to a broad range of users, including STD patients, recovered individuals, and those unaware of their STD status. By harnessing cutting-edge technologies like image detection, symptom-based identification, prevention methods, doctor and clinic recommendations, and virtual counselor chat, the application offers a holistic approach to STD management. In conclusion, the escalating STD rates in Sri Lanka and across the globe require immediate action. The integration of technology-driven solutions, along with comprehensive education and healthcare accessibility, is the key to curbing the spread of STDs and promoting better overall public health.

Keywords: STD, machine learning, NLP, artificial intelligence

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Authors: Ayoub El Bourtali, Abdessamed Najine, Amrou Moussa Benmoussa

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It is becoming more and more necessary to manage flood risk, and it must include all stakeholders and all possible means available. The goal of this work is to map the vulnerability of the Oued Derna-region Tagzirt flood zone in the semi-arid region. This is about implementing predictive models and flood control. This allows for the development of flood risk prevention plans. In this study, A resistivity survey was conducted over the area to locate and evaluate soil characteristics in order to calculate discharges and prevent flooding for the study area. The development of a one-dimensional (1D) hydrodynamic model of the Derna River was carried out in HEC-RAS 5.0.4 using a combination of survey data and spatially extracted cross-sections and recorded river flows. The study area was hit by several extreme floods, causing a lot of property loss and loss of life. This research focuses on the most recent flood events, based on the collected data, the water level, river flow and river cross-section were analyzed. A set of flood levels were obtained as the outputs of the hydraulic model and the accuracy of the simulated flood levels and velocity.

Keywords: derna river, 1D hydrodynamic model, flood modelling, HEC-RAS 5.0.4

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Authors: Ali Hamza, Bertug Akintug

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Having an appropriate plan of sanitation systems is one of the critical issues for global urban slums. Poor sanitation systems in urban slums outcomes an enhanced vulnerability of severe diseases, low hygiene and environmental risks within our environment. Mentioning human excreta being one of the most highly risked pollutants among all the other major contributors of sanitation pollutants is increasing public health risks and amounts of pollution loads within the slum environment. Higher population growth, urge of urbanization and illegal status of urban slums makes it impossible to increase the level of performance of sanitation systems in urban slums. According to Sustainable Sanitation Alliance, design parameters for sanitation systems were set up to ensure sustainable environment. This paper reviews the characteristics of human excreta at present, treatment technologies, and procedures of processes that can be adopted feasibly in the urban slums. Keeping these factors as our significant concern of study, assessment of sustainable sanitation systems is done using sanitation chain concept in accordance to the pre-determined sustainability indicators and criteria which reflect the potential and feasible application of waterless sanitation systems bringing sustainable sanitation systems in urban slums.

Keywords: human excreta, sanitation chain, sustainable sanitation systems, urban slums

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Authors: Rezzan Alagoz, Seda Gonul

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This study examines the migration, work, and social life experiences of undocumented Afghan migrants employed as shepherds in Igdır. Despite their high visibility in informal labor markets, their undocumented status renders them invisible in everyday life. Their invisibility in both official status and social life, coupled with their vulnerability to exploitation in the labor market, renders them particularly susceptible to marginalization. This research employs the concept of the subaltern to examine the characteristics of Afghan migrants as unrepresented, unheard, and invisible. It also analyzes their experiences in the labor market based on the concept of biopolitics. Undocumented Afghan migrants are engaged in labor-intensive occupations such as shepherding, thereby addressing an essential gap in the workforce that local workers are reluctant to undertake. The reliance of employers on the labor of these employees is significant; however, the undocumented status of these workers leaves them vulnerable to exploitation. In addition to serving as a critical source of low-cost labor, these individuals are susceptible to exploitation in the form of non-payment for their work, extended and intensive work schedules, and, on some occasions, physical violence. In the event of a conflict between shepherds and their employers, undocumented workers are unable to seek legal recourse, which serves to reinforce their marginalized status further. The predominant practice among Afghan shepherds is to utilize the workplace as a place of residence. In the context of shepherding work, the prevailing conditions at the workplace frequently pose a significant threat to the health and well-being of the individuals engaged in such activities. As a result of their lack of official status, these individuals lack access to basic services such as healthcare, which has the consequence of rendering them invisible in public and institutional spaces. Attempts to engage with public systems carry the risk of deportation, reinforcing the already fragile and precarious nature of their existence. This study examines the socio-political implications of undocumented status and addresses these experiences in the context of national and international migration policies. In line with Agamben's concept of the "state of exception" undocumented migrants exist in a state where fundamental rights are effectively nullified, and they are rendered outside the protection of the law. This exclusion is further exacerbated by the intersection of economic exploitation, political and physical invisibility, and limited access to basic services, which collectively contribute to a cycle of vulnerability. This research is based on in-depth interviews with 18 Afghan shepherds in Igdir province in August 2024. The research contributes to the ongoing critical debates on migration, labor exploitation, and biopolitics by focusing on the experiences of Afghan shepherds. The article examines how undocumented migrants maneuver between visibility and invisibility within the context of a system that relies on exploitation in the labor market and migration policies. The research findings demonstrate the necessity for policy intervention to address the structural exclusion of undocumented Afghan migrants from national and international protection systems, as well as their indispensable role in local economies.

Keywords: Afghan migrants, biopolitics, border economy, informal labour market, migration policy, sheepherding, Subaltern

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Authors: Ghassan Kbar, Shady Aly, Ibrahim Alsharawy, Akshay Bhatia, Nur Alhasan, Ronaldo Enriquez

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The Smart Help for persons with disability (PWD) is a part of the project SMARTDISABLE which aims to develop relevant solution for PWD that target to provide an adequate workplace environment for them. It would support PWD needs smartly through smart help to allow them access to relevant information and communicate with other effectively and flexibly, and smart editor that assist them in their daily work. It will assist PWD in knowledge processing and creation as well as being able to be productive at the work place. The technical work of the project involves design of a technological scenario for the Ambient Intelligence (AmI) - based assistive technologies at the workplace consisting of an integrated universal smart solution that suits many different impairment conditions and will be designed to empower the Physically disabled persons (PDP) with the capability to access and effectively utilize the ICTs in order to execute knowledge rich working tasks with minimum efforts and with sufficient comfort level. The proposed technology solution for PWD will support voice recognition along with normal keyboard and mouse to control the smart help and smart editor with dynamic auto display interface that satisfies the requirements for different PWD group. In addition, a smart help will provide intelligent intervention based on the behavior of PWD to guide them and warn them about possible misbehavior. PWD can communicate with others using Voice over IP controlled by voice recognition. Moreover, Auto Emergency Help Response would be supported to assist PWD in case of emergency. This proposed technology solution intended to make PWD very effective at the work environment and flexible using voice to conduct their tasks at the work environment. The proposed solution aims to provide favorable outcomes that assist PWD at the work place, with the opportunity to participate in PWD assistive technology innovation market which is still small and rapidly growing as well as upgrading their quality of life to become similar to the normal people at the workplace. Finally, the proposed smart help solution is applicable in all workplace setting, including offices, manufacturing, hospital, etc.

Keywords: ambient intelligence, ICT, persons with disability PWD, smart application, SHW

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Authors: Ho Young Son, Bu Seog Ju, Woo Young Jung

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This study presents the seismic safety evaluation of weir structure subjected to strong earthquake ground motions, as a flood defense structure in civil engineering structures. The seismic safety analysis procedure was illustrated through development of Finite Element (FE) and InFinite Element (IFE) method in ABAQUS platform. The IFE model was generated by CINPS4, 4-node linear one-way infinite model as a sold continuum infinite element in foundation areas of the weir structure and then nonlinear FE model using friction model for soil-structure interactions was applied in this study. In order to understand the complex behavior of weir structures, nonlinear time history analysis was carried out. Consequently, it was interesting to note that the compressive stress gave more vulnerability to the weir structure, in comparison to the tensile stress, during an earthquake. The stress concentration of the weir structure was shown at the connection area between the weir body and stilling basin area. The stress both tension and compression was reduced in IFE model rather than FE model of weir structures.

Keywords: seismic, numerical analysis, FEM, weir, boundary condition

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Authors: Ekrem Aksoy, Nihat Adar, Selçuk Canbek

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Ambient Computing or Ambient Intelligence (AmI) is emerging area in computer science aiming to create intelligently connected environments and Internet of Things. In this paper, we propose communication middleware architecture for AmI. This middleware architecture addresses problems of communication, networking, and abstraction of applications, although there are other aspects (e.g. HCI and Security) within general AmI framework. Within this middleware architecture, any application developer might address HCI and Security issues with extensibility features of this platform.

Keywords: AmI, ambient computing, middleware, distributed-systems, software-defined networking

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Authors: Moise A. E., State G., Tudorache M., Custură I., Enea D. N., Osman (Defta) A., Drăgotoiu D.

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This scientific paper explores risk management strategies in the food supply chain, with a focus on chicken raw materials, in the context of a company sourcing from the EU and non-EU. The aim of the paper is to adapt the requirements of international standards (IFS, BRC, QS, ITW, FSSC, ISO), proposing efficient methods to identify and remediate non-conformities and corrective and preventive actions. Defining the supply flow and acceptance steps promotes collaboration with suppliers to ensure the quality and safety of raw materials. To assess the risks of suppliers and raw materials, objective criteria are developed and vulnerabilities in the supply chain are analyzed, including the risk of fraud. Active monitoring of international alerts through RASFF helps to identify emerging risks quickly, and regular analysis of international trends and company performance enables continuous adaptation of risk management strategies. Implementing these measures strengthens food safety and consumer confidence in the final products supplied.

Keywords: food supply chain, international standards, quality and safety of raw materials, RASFF

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Authors: Somnath Bhattachryya, Montree Bunruangses, Somchat Sonasang, Preecha Yupapin

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In this study and research on meditation and insight, the design and experiment with electronic circuits to manipulate the meditators' mental circles that call the chakras to have the same size is proposed. The shape of the circuit is 4-ports, called an add-drop multiplexer, that studies the meditation structure called the four-mindfulness foundation, then uses an AC power signal as an input instead of the meditation time function, where various behaviors with the method of re-filtering the signal (successive filtering), like eight noble paths. Start by inputting a signal at a frequency that causes the velocity of the wave on the perimeter of the circuit to cause particles to have the speed of light in a vacuum. The signal changes from electromagnetic waves and matter waves according to the velocity (frequency) until it reaches the point of the relativistic limit. The electromagnetic waves are transformed into photons with properties of wave-particle overcoming the limits of the speed of light. As for the matter wave, it will travel to the other side and cannot pass through the relativistic limit, called a shadow signal (echo) that can have power from increasing speed but cannot create speed faster than light or insight. In the experiment, the only the side where the velocity is positive, only where the speed above light or the corresponding frequency indicates intelligence. Other side(echo) can be done by changing the input signal to the other side of the circuit to get the same result. But there is no intelligence or speed beyond light. It is also used to study the stretching, contraction of time and wormholes that can be applied for teleporting, Bose-Einstein condensate and teleprinting, quantum telephone. The teleporting can happen throughout the system with wave-particle and echo, which is when the speed of the particle is faster than the stretching or contraction of time, the particle will submerge in the wormhole, when the destination and time are determined, will travel through the wormhole. In a wormhole, time can determine in the future and the past. The experimental results using the microstrip circuit have been found to be by the principle of quantum relativity, which can be further developed for both tools and meditation practitioners for quantum technology.

Keywords: quantu meditation, insight picture, quantum circuit, absolute time, teleportation

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Authors: Imaneh Abbasi, Ladan Fata, Majid Sadeghi, Sara Banihashemi, Abolfazl Mohammadee

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Background: Dimensional and transdiagnostic approaches as a result of high comorbidity among mental disorders have captured researchers and clinicians interests for exploring the latent factors of development and maintenance of some psychological disorders. The goal of present study is to compare some of these common factors between generalized anxiety disorder and unipolar mood disorder. Methods: 27 patients with generalized anxiety disorder, 29 patients with depression disorder were recruited using SCID-I and 69 non-clinical population were selected using GHQ cut off point. MANCOVA was used for analyzing data. Results: The results show that worry, rumination, intolerance of uncertainty, maladaptive metacognitive beliefs, and experiential avoidance were all significantly different between GAD and unipolar mood disorder groups. However, there were not any significant differences in difficulties in emotion regulation and neuroticism between GAD and unipolar mood disorder groups. Discussion: Results indicate that although there are some transdiagnostic and common factors in GAD and unipolar mood disorder, there may be some specific vulnerability factors for each disorder. Further study is needed for answering these questions.

Keywords: transdiagnostic, depression, generalized anxiety disorder, emotion regulation

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Authors: László Schmidt

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Technological and digital innovation is constantly and dynamically evolving, which poses an enormous challenge to both lawmaking and law enforcement. To legislation because artificial intelligence permeates many areas of people’s daily lives that the legislator must regulate. it can see how challenging it is to regulate e.g. self-driving cars/taxis/camions etc. Not to mention cryptocurrencies and Chat GPT, the use of which also requires legislative intervention. Artificial intelligence also poses an extraordinary challenge to law enforcement. In criminal cases, police and prosecutors can make great use of AI in investigations, e.g. in forensics, DNA samples, reconstruction, identification, etc. But it can also be of great help in the detection of crimes committed in cyberspace. In the case of cybercrime, on the one hand, it can be viewed as a new type of crime that can only be committed with the help of information systems, and that has a specific protected legal object, such as an information system or data. On the other hand, it also includes traditional crimes that are much easier to commit with the help of new tools. According to Hungarian Criminal Code section 375 (1), any person who, for unlawful financial gain, introduces data into an information system, or alters or deletes data processed therein, or renders data inaccessible, or otherwise interferes with the functioning of the information system, and thereby causes damage, is guilty of a felony punishable by imprisonment not exceeding three years. The Covid-19 coronavirus epidemic has had a significant impact on our lives and our daily lives. It was no different in the world of crime. With people staying at home for months, schools, restaurants, theatres, cinemas closed, and no travel, criminals have had to change their ways. Criminals were committing crimes online in even greater numbers than before. These crimes were very diverse, ranging from false fundraising, the collection and misuse of personal data, extortion to fraud on various online marketplaces. The most vulnerable age groups (minors and elderly) could be made more aware and prevented from becoming victims of this type of crime through targeted programmes. The aim of the study is to show the Hungarian judicial practice in relation to cybercrime and possible preventive solutions.

Keywords: cybercrime, COVID-19, Hungary, criminal law

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Elio El Kahi, Michel Khouri, Olivier Deck, Pierre Rahme, Rasool Mehdizadeh

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Recent developments in civil engineering create multiple interaction problems between the soil and the structure. One of the major problems is the impact of ground movements on buildings. Consequently, managing risks associated with these movements, requires a determination of the different influencing factors and a specific knowledge of their variability/uncertainty. The main purpose of this research is to study the behavior of structures submitted to differential settlement, in order to assess their vulnerability, taking into consideration the different sources of uncertainties. Analytical approach is applied to investigate on one hand the influence of these uncertainties that are related to the soil, and on the other hand the structure stiffness variation with the presence of openings and the movement transmitted between them as related to the origin and shape of the free-field movement. Results reveal the effect of taking these uncertainties into consideration, and specify the dominant and most significant parameters that control the ground movement associated with the Soil-Structure Interaction (SSI) phenomenon.

Keywords: analytical approach, building, damage, differential settlement, soil-structure interaction, uncertainties

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Authors: Yanting Cao, Kazumitsu Nawata

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As smartphones upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described as more refined, complex, and detailed. In this context, we analyzed a set of experimental data obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model becomes extremely challenging. After a series of feature selection and parameters adjustment, a well-performed SVM classifier has been trained.

Keywords: smart sensors, human activity recognition, artificial intelligence, SVM

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Authors: Akila Murali, Prithika Manivel

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Disasters are defined as a serious disruption of the functioning of a community or a society, which involves widespread human, material, economic or environmental impacts. The number of people suffering food crisis as a result of natural disasters has tripled in the last thirty years. The economic losses due to natural disasters have shown an increase with a factor of eight over the past four decades, caused by the increased vulnerability of the global society, and also due to an increase in the number of weather-related disasters. Efficient disaster detection and alerting systems could reduce the loss of life and properties. In the event of a disaster, another important issue is a good search and rescue system with high levels of precision, timeliness and safety for both the victims and the rescuers. Wireless Sensor Networks technology has the capability of quick capturing, processing, and transmission of critical data in real-time with high resolution. This paper studies the capacity of sensors and a Wireless Sensor Network to collect, collate and analyze valuable and worthwhile data, in an ordered manner to help with disaster management.

Keywords: alerting systems, disaster detection, Ad Hoc network, WSN technology

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Authors: S. Rezaei, Z. Mirzaei, M. Khalighi, J. Bahrami

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Bridges, as an essential part of road infrastructures, are affected by various deterioration mechanisms over time due to the changes in their performance. As changes in performance can have many negative impacts on society, it is essential to be able to evaluate and measure the performance of bridges throughout their life. This evaluation includes the development or the choice of the appropriate performance indicators, which, in turn, are measured based on the selection of appropriate models for the existing deterioration mechanism. The purpose of this article is a statistical study of indicators and deterioration mechanisms of bridges in order to discover further research capacities in bridges performance assessment. For this purpose, some of the most common indicators of bridge performance, including reliability, risk, vulnerability, robustness, and resilience, were selected. The researches performed on each index based on the desired deterioration mechanisms and hazards were comprehensively reviewed. In addition, the formulation of the indicators and their relationship with each other were studied. The research conducted on the mentioned indicators were classified from the point of view of deterministic or probabilistic method, the level of study (element level, object level, etc.), and the type of hazard and the deterioration mechanism of interest. For each of the indicators, a number of challenges and recommendations were presented according to the review of previous studies.

Keywords: bridge, deterioration mechanism, lifecycle, performance indicator

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Authors: Beatriz Delgado

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Although social anxiety is one of the most prevalent disorders in adolescents and causes considerable difficulties and social distress in those with the disorder, to date very few studies have explored the impact of social anxiety on academic adjustment in student populations. The aim of this study was analyze the effect of social anxiety on school functioning in Secondary Education. Specifically, we examined the relationship between social anxiety and self-concept, academic goals, causal attributions, intellectual aptitudes, and learning strategies, personality traits, and academic performance, with the purpose of creating a causal-explanatory model of academic performance. The sample consisted of 2,022 students in the seven to ten grades of Compulsory Secondary Education in Spain (M = 13.18; SD = 1.35; 51.1% boys). We found that: (a) social anxiety has a direct positive effect on internal attributional style, and a direct negative effect on self-concept. Social anxiety also has an indirect negative effect on internal causal attributions; (b) prior performance (first academic trimester) exerts a direct positive effect on intelligence, achievement goals, academic self-concept, and final academic performance (third academic trimester), and a direct negative effect on internal causal attributions. It also has an indirect positive effect on causal attributions (internal and external), learning goals, achievement goals, and study strategies; (c) intelligence has a direct positive effect on learning goals and academic performance (third academic trimester); (d) academic self-concept has a direct positive effect on internal and external attributional style. Also, has an indirect effect on learning goals, achievement goals, and learning strategies; (e) internal attributional style has a direct positive effect on learning strategies and learning goals. Has a positive but indirect effect on achievement goals and learning strategies; (f) external attributional style has a direct negative effect on learning strategies and learning goals and a direct positive effect on internal causal attributions; (g) learning goals have direct positive effect on learning strategies and achievement goals. The structural equation model fit the data well (CFI = .91; RMSEA = .04), explaining 93.8% of the variance in academic performance. Finally, we emphasize that the new causal-explanatory model proposed in the present study represents a significant contribution in that it includes social anxiety as an explanatory variable of cognitive-motivational constructs.

Keywords: academic performance, adolescence, cognitive-motivational variables, social anxiety

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Authors: James Brown, Stella Kortekaas, Ian Finnie, George Zhang, Christine Devine, Neil Healy

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The use of GIS as an analysis tool for engineering decision making is now best practice in the offshore industry. GIS enables multidisciplinary data integration, analysis and visualisation which allows the presentation of large and intricate datasets in a simple map-interface accessible to all project stakeholders. Presenting integrated geoscience and geotechnical data in GIS enables decision makers to be well-informed. This paper is a successful case study of how GIS spatial analysis techniques were applied to help select the most favourable pipeline route. Routing a pipeline through any natural environment has numerous obstacles, whether they be topographical, geological, engineering or financial. Where the pipeline is subjected to external hydrostatic water pressure and is carrying pressurised hydrocarbons, the requirement to safely route the pipeline through hazardous terrain becomes absolutely paramount. This study illustrates how the application of modern, GIS-based pipeline routing techniques enabled the identification of a single most-favourable pipeline route crossing of a challenging seabed terrain. Conventional approaches to pipeline route determination focus on manual avoidance of primary constraints whilst endeavouring to minimise route length. Such an approach is qualitative, subjective and is liable to bias towards the discipline and expertise that is involved in the routing process. For very short routes traversing benign seabed topography in shallow water this approach may be sufficient, but for deepwater geohazardous sites, the need for an automated, multi-criteria, and quantitative approach is essential. This study combined multiple routing constraints using modern least-cost-routing algorithms deployed in GIS, hitherto unachievable with conventional approaches. The least-cost-routing procedure begins with the assignment of geocost across the study area. Geocost is defined as a numerical penalty score representing hazard posed by each routing constraint (e.g. slope angle, rugosity, vulnerability to debris flows) to the pipeline. All geocosted routing constraints are combined to generate a composite geocost map that is used to compute the least geocost route between two defined terminals. The analyses were applied to select the most favourable pipeline route for a potential gas development in deep water. The study area is geologically complex with a series of incised, potentially active, canyons carved into a steep escarpment, with evidence of extensive debris flows. A similar debris flow in the future could cause significant damage to a poorly-placed pipeline. Protruding inter-canyon spurs offer lower-gradient options for ascending an escarpment but the vulnerability of periodic failure of these spurs is not well understood. Close collaboration between geoscientists, pipeline engineers, geotechnical engineers and of course the gas export pipeline operator guided the analyses and assignment of geocosts. Shorter route length, less severe slope angles, and geohazard avoidance were the primary drivers in identifying the most favourable route.

Keywords: geocost, geohazard, pipeline route determination, pipeline route optimisation, spatial analysis

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Authors: Shreya Poddar

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Legal drafting and revising are recognized as highly demanding tasks for legal professionals. This paper introduces an approach to automate and refine these processes through the use of advanced Artificial Intelligence (AI). The method employs Large Language Models (LLMs), with a specific focus on 'Chain of Thoughts' (CoT) and knowledge injection via prompt engineering. This approach differs from conventional methods that depend on comprehensive training or fine-tuning of models with extensive legal knowledge bases, which are often expensive and time-consuming. The proposed method incorporates knowledge injection directly into prompts, thereby enabling the AI to generate more accurate and contextually appropriate legal texts. This approach substantially decreases the necessity for thorough model training while preserving high accuracy and relevance in drafting. Additionally, the concept of guardrails is introduced. These are predefined parameters or rules established within the AI system to ensure that the generated content adheres to legal standards and ethical guidelines. The practical implications of this method for legal work are considerable. It has the potential to markedly lessen the time lawyers allocate to document drafting and revision, freeing them to concentrate on more intricate and strategic facets of legal work. Furthermore, this method makes high-quality legal drafting more accessible, possibly reducing costs and expanding the availability of legal services. This paper will elucidate the methodology, providing specific examples and case studies to demonstrate the effectiveness of 'Chain of Thoughts' and knowledge injection in legal drafting. The potential challenges and limitations of this approach will also be discussed, along with future prospects and enhancements that could further advance legal work. The impact of this research on the legal industry is substantial. The adoption of AI-driven methods by legal professionals can lead to enhanced efficiency, precision, and consistency in legal drafting, thereby altering the landscape of legal work. This research adds to the expanding field of AI in law, introducing a method that could significantly alter the nature of legal drafting and practice.

Keywords: AI-driven legal drafting, legal automation, futureoflegalwork, largelanguagemodels

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Authors: Eduardo Álvarez, Gerardo Quiroga, Jorge Orozco, Gabriel Chavira

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AmI proposes a new way of thinking about computers, which follows the ideas of the Ubiquitous Computing vision of Mark Weiser. In these, there is what is known as a Disappearing Computer Initiative, with users immersed in intelligent environments. Hence, technologies need to be adapted so that they are capable of replacing the traditional inputs to the system by embedding these in every-day artifacts. In this work, we present an approach, which uses Radiofrequency Identification (RFID) and Near Field Communication (NFC) technologies. In the latter, a new form of interaction appears by contact. We compare both technologies by analyzing their requirements and advantages. In addition, we propose using a combination of RFID and NFC.

Keywords: touching interaction, ambient intelligence, ubiquitous computing, interaction, NFC and RFID

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Authors: Tori Reddy Dodla, Laura Ann Jones

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The objective of this study was to propose strategies to improve the efficiency of Knowledge Management Systems (KMS). This study highlights best practices from various industries to create an overall summary of Knowledge Management (KM) and efficiency in organizational performance. Results indicated eleven best practices for maximizing the efficiency of organizational KMS that can be divided into four categories: Designing the KMS, Identifying Case Studies, Implementing the KMS, and Promoting adoption and usage. Our findings can be used as a foundation for scholars to conduct further research on KMS efficiency.

Keywords: artificial intelligence, knowledge management efficiency, knowledge management systems, organizational performance

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Authors: Cornelius Ehlers, Lisa Harmatiuk, Sharon Rowland, Michelle Shafers

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The objective of the Street Connect program is to provide client-centered care for the homeless population within the City of Red Deer. The program aims to provide an extended continuum of care (addiction, mental health, and physical health) for high acuity homeless individuals who are not connected to a service provider and/or community service agency. Street Connect includes both primary and secondary streams of service: Overall, Street Connect has demonstrated its ability to support vulnerable populations within the City of Red Deer, specifically those who are homeless and seeking addiction, mental health, and medical assistance. The results from the data extract and chart audit reflect the complexity and vulnerability of the clients enrolled in the Street Connect program. The clients were predominantly male, with an average age of 41 years. The majority did not have a permanent address, and 65% did not have employment. Substance abuse/addiction issues were common, combined with a history of psychiatric diagnoses and previous mental health hospitalizations. The most utilized drugs were street drugs such as methamphetamine, fentanyl, and other opioids.

Keywords: client-centred care, homelessness, mental health, rural

Procedia PDF Downloads 96