The huge fire broke out in the centre of Abergavenny on Sunday and continued into Monday morning.

Home Secretary Amber Rudd tells MPs David Anderson QC will oversee a review of the way recent attacks were handled. in a statement on the terror attacks in Manchester and London.

The Scottish Fire and Rescue Service is spending £1.6m on new kit to tackle the problem.

Aden Thurlow, 11, is believed to be the youngest person to lead climbs on Am Buachaille and Old Man of Stoer.

The States of Jersey Ambulance Service campaign calls for greater respect and support for workers.

Guernsey Post issues customers with cards aimed at protecting their staff from dog attacks.

Police say a number of cars, a wheelie bin and fence were deliberately set alight.

Great North Air Ambulance says a green laser was shone into its helicopter on Monday evening.

The authority says there was a backlog of planning applications following the incident.

Officers say they believe someone accessed the rear of the property through a side alleyway.

The Public Spaces Protection Order aims "to improve safety and quality of life" in the town.

Four Army teachers are preparing to row across the Atlantic in a 3,000 mile challenge for charity.

Lancashire Fire and Rescue Service say four crews were deployed to a derelict building in Preston.

The MP says people across Cumbria and Lancashire are waiting up to 15 months for treatment.

The fire service is investigating the cause of the blaze on Northgate Street, Devizes.

Customers are some of your best leads. According to a 2022 HubSpot Blog survey of more than 500 sales professionals, more than 70 percent said that upsell and cross-sell drives up to 30 percent of their revenue.

Bristol Rovers boss Matt Taylor says it will be a moment of "togetherness" as fans plan support for players who have suffered racist abuse.

By now, you may already be aware of Ashton Kutcher's campaign against the Village Voice, sparked by an article critical of the substance and message of his anti-trafficking adverts. The Voice has form on this one: their earlier article about dodgy stats that get bandied about in the trafficking discussion is a must-read.

The usual disclaimer... I am (as indeed all of Kutcher's critics are) opposed to trafficking in any form, including child sex trafficking. But we must not let emotion exclusively carry the day; it achieves nothing. The Voice hits the nail on the head when they sum up anti-trafficking efforts: "an emotional reaction, based on good intentions, but grounded in bogus information."

The problem of bogus information is this - campaigns such as Kutcher's conflate all sex work with child sex trafficking, and child sex trafficking with all trafficking. Approaches that do so not only encourage criminalisation legislation that harms consenting adults, but also obscures the real victims. How? By using vastly inflated numbers for one kind of trafficking, and pretty much ignoring everything else. Actual children being actually trafficked for actual sex are rarely, if ever, found by the kind of scatterhsot brothel raids and streetwalker crackdowns so many seem to consider "succeses" in the anti-trafficking effort.

Please, please stop kidding yourselves. The raids you hear about are not successes. They are vast wastes of time, money, and manpower. And many groups receiving funding meant to help victims of trafficking seem instead to be lining their own pockets. There is undoubtedly work to be done eliminating trafficking of men and women for any kind of labour. It almost certainly isn't the approach anti-traffickers think will work.

Kutcher’s response against the Village Voice has included tweeting advertisers on Backpage.com, accusing them of supporting slavery. So far, so "concerned". And then this tweet:

Like a lot of people, Ashton Kutcher seems to have some pretty confused ideas about sex work. To be in a position of wanting to help people, yet still falling back on ridicule and stereotype when talking about them, is inexcusable. Someone who says,

"I’ve spent the last 2 years meeting with every expert on the issue of Human Trafficking that I can find, reading countless books, meeting with victims and former traffickers, and studying effective international models to combat trafficking."

Maybe Ashton should have made time for a little bit of victim sensitivity training in there somewhere? (Not that he's known for politically correct tweeting, mind. He seems to channel the spirit of Littlejohn every now and again.)

Kutcher's response has been strongly supported by the Family Research Council who are regarded by many as a hate group. The FRC is one of the main contributors to the Witherspoon Institute’s “research” on pornography that conflates the adult industry with trafficking. That report contained significant input from Patrick Fagan of The Heritage Foundation – you know, those people whose work inspired Reagan’s covert Cold War military actions.

The Witherspoon report encourages celebrities to “use the bully pulpit” and abuses suspiciously similar dodgy statistics as Kutcher’s campaign. And while it doesn't name particular celebrities to be promoted as faces of such bullying, a similar document from Abt Associates does - it specifically names Kutcher's wife, Demi Moore.

I know a little about what it's like to be asked to comment on issues you don't necessarily have expertise in. Sometimes, journalists and television shows approach people like me to provide commentary rather than, say, academics in the relevant fields. It's unfortunate but it's a fact of media life. And I do try, by following academic discussions and talking with friends who are professionals in, say, sex education or the porn industry, to at least not come off as too much of an ignorant tit. I would shudder in horror, though, to ever be described as a "leading player" in the debate around trafficking or related issues. Something that Kutcher and his wife Moore seem to have no problem with. The strategy clearly works, with significant numbers of Kutcher's followers joining in his Twitter tirade, and the man himself being promoted as somehow more of an expert on the issues than, ya know, actual experts.

Not bad for a guy whose credentials, according to his Twitter profile, are: "I make stuff up."

The Voice article pointed out that it's not known how many of the millions, if any, raised by Demi Moore and Ashton Kutcher's campaign have actually gone to helping victims directly. With their charity having only been launched 5 months ago, and apparently no financial reports as yet available, it's hard to know when that pointed question will be resolved. I'd like to add another dimension to that question: how many of the millions raised by Demi Moore and Ashton Kutcher could be coming from groups such as the Family Research Council?

The well-meaning Twitter fans following Kutcher's lead probably don't realise they're being taken for a ride on the facts front. It can actually be very hard to sort the real from the fake when people keep repeating made-up stuff as true. So I guess my question now is, why are Kutcher’s millions of fans seemingly totally okay with this movement's possible links to the far-right hawkish Christian lobbyists? And when will the likes of Kutcher and Moore realise they're the ones being used, not by evil evil sex traffickers, but by conservative groups with a frightening agenda?

Listen to this week's podcast (Podcast edit: I mistakenly mention Respond.js, which is actually a media query polyfill - I'm actually talking about Responsive images) Google Dart By far the biggest news of the week isn't JavaScript, but rather a language called Dart (formerly Dash?), which certain factions within Google hope will replace ...

ENTREVISTA A JACOBO CURRAIS Hoy tenemos con nosotros a Jacobo Currais, traductor e intérprete y profesor de interpretación en la Universidad de Vigo con una amplia experiencia profesional en numerosos...

La entrada EL REHABLADO Y LA INTERPRETACIÓN se publicó primero en Nartran Translations.

En la entrada de hoy quiero hacerme eco de un artículo de Takahata Sachi que se titula «Las malas condiciones desincentivan a los intérpretes judiciales», publicado en Nippon.com y que versa acerca de la situación de los intérpretes judiciales en Japón. El artículo se puede leer en español, por lo que no hay motivo para tormento […]

Estamos en pleno proceso de preinscripción universitaria y algunos estudiantes se estarán preguntando dónde pueden estudiar el grado de Traducción e Interpretación. […]

La segunda convocatoria para defender los trabajos de fin de grado (TFG) de Traducción e Interpretación se acerca y sé que algunos […]

Para utilizar el vocabulario de manera adecuada, has de saber que las palabras no solamente son portadoras de significado. A menudo se cargan de connotaciones. […]

Origen

Image forgery detection techniques refer to the process of detecting manipulated or altered images, which can be used for various purposes, including malicious intent or misinformation. Image forgery detection is a crucial task in digital image forensics, where researchers have developed various techniques to detect image forgery. These techniques can be broadly categorised into active, passive, machine learning-based and hybrid. Active approaches involve embedding digital watermarks or signatures into the image during the creation process, which can later be used to detect any tampering. On the other hand, passive approaches rely on analysing the statistical properties of the image to detect any inconsistencies or irregularities that may indicate forgery. In this paper for the detection of scaling and cropping attack a deep learning method has been proposed using ResNet. The proposed method (Res-Net-Adam-Adam) is able to achieve highest amount of accuracy of 99.14% (0.9914) while detecting fake and real images.

This study investigates the effects of social media marketing efforts (SMME) on smartphone brand identification, attachment, association, and willingness to pay premium prices. A survey of 320 smartphone users who followed official social media handles managed by smartphone companies was conducted for this purpose. PLS-SEM was used to analyse the collected data. The findings demonstrated importance of SMME dimensions. According to the study's findings, the smartphone brand's SMMEs had significant impact on brand identification, brand association, and brand attachment. The results revealed that SMMEs had significant impact on willingness to pay the premium price. The findings also show that brand identification, attachment, and association mediated the relationship between SMMEs and willingness to pay a premium price. The findings of this study will be useful in developing social media marketing strategies for smartphones. This study demonstrates the use of social media marketing to promote mobile phones, particularly in emerging markets.

Aim/Purpose: The aim of the study is to test whether the perception of self-regulated learning during text reading in online teacher education is improved by using a digital tool for the use of metacognitive strategies for planning, monitoring, and self-assessment. Background: The use of self-regulated learning is important in reading skills, and for students to develop self-regulated learning, their teachers must master it. Therefore, teaching strategies for self-regulated learning in teacher education is essential. Methodology: The sample size was 252 participants with the tool used by 42% or the participants. A quasi-experimental design was used in a pre-post study. ARATEX-R, a text-based scale, was used to evaluate self-regulated learning. The 5-point Likert scale includes the evaluation of five dimensions: planning strategies, cognition management, motivation management, comprehension assessment and context management. A Generalized Linear Model was used to analyse the results. Contribution: Using the tool to self-regulate learning has led to an improvement during text reading, especially in the dimensions of motivation management, planning management and comprehension assessment, key dimensions for text comprehension and learning. Findings: Participants who use the app perceive greater improvement, especially in the dimensions of motivation management (22,3%), planning management (19.9%) and comprehension assessment (24,6%), which are fundamental dimensions for self-regulation in text reading. Recommendations for Practitioners: This tool should be included in teacher training to enable reflection during the reading of texts, because it helps to improve three key types of strategies in self-regulation: (1) planning through planning management, (2) monitoring through motivation management and comprehension assessment, and (3) self-assessment through comprehension assessment. Recommendation for Researchers: The success of the tool suggests further study for its application in other use cases: other student profiles in higher education, other teaching modalities, and other educational stages. These studies will help to identify adaptations that will extend the tool’s use in education. Impact on Society: The use of Metadig facilitates reflection during the reading of texts in order to improve comprehension and thus self-regulate the learning of content. This reflection is crucial for students’ knowledge construction. Future Research: Future research will focus on enhancing the digital tool by adding features to support the development of cognition and context management. It will also focus on how on adapting the tool to help other types of learners.

In today's world, malware has become a part and threat to our computer systems. All electronic devices are very susceptible/vulnerable to various threats like different types of malware. There is one subset of malware called ransomware, which is majorly used to have large financial gains. The attacker asks for a ransom amount to regain access to the system/data. When dynamic technique using machine learning is used, it is very important to select the correct set of features for the detection of a ransomware attack. In this paper, we present two novel algorithms for the detection of ransomware attacks. The first algorithm is used to assign the time stamp to the features (API calls) for the ordering and second is used for the ordering and ranking of the features for the early detection of a ransomware attack.

Smart home devices are vulnerable to various attacks, including distributed-denial-of-service (DDoS) attacks. Current detection techniques face challenges due to nonlinear thought, unusual system traffic, and the fluctuating data flow caused by human activities and device interactions. Identifying the baseline for 'normal' traffic and suspicious activities like DDoS attacks from encrypted data is also challenging due to the encrypted protective layer. This work introduces a concept called synoptic crow search with recurrent transformer network-based DDoS attack detection, which uses the synoptic weighted crow search algorithm to capture varying traffic patterns and prioritise critical information handling. An adaptive recurrent transformer neural network is introduced to effectively regulate DDoS attacks within encrypted data, counting the historical context of the data flow. The proposed model shows effective performance in terms of low false alarm rate, higher detection rate, and accuracy.

A hybrid network system for mitigating errors due to jamming attacks in cognitive radio networks (CRNs) is named LeNet deep stacked autoencoder (LDSAE) and is developed. In this exploration, the sensing stage and decision-making are considered. The sensing unit is composed of four steps. First, the detected signal is forwarded to filtering progression. Here, BPF is utilised to filter the detected signal. The filtered signal is squared in the second phase. Third, signal samples are combined and jamming attacks occur by including false energy levels. Last, the attack is maliciously affecting the FC decision in the fourth step. On the other hand, FC initiated the decision-making and also recognised jamming attacks that affect the link amidst PU and SN in decision-making stage and it is accomplished by employing LDSAE-based trust model where the proposed module differentiates the malicious and selfish users. The analytic measures of LDSAE gained 79.40%, 79.90%, and 78.40%.

Aim/Purpose: A vital business activity within socio-technical enterprises is tacit knowledge externalization, which elicits and explicates tacit knowledge of enterprise employees as external knowledge. The aim of this paper is to integrate diverse aspects of externalization through the Enterprise Ontology model. Background: Across two decades, researchers have explored various aspects of tacit knowledge externalization. However, from the existing works, it is revealed that there is no uniform representation of the externalization process, which has resulted in divergent and contradictory interpretations across the literature. Methodology : The Enterprise Ontology model is constructed step-wise through the conceptual and measurement views. While the conceptual view encompasses three patterns that model the externalization process, the measurement view employs certainty-factor model to empirically measure the outcome of the externalization process. Contribution: The paper contributes towards knowledge management literature in two ways. The first contribution is the Enterprise Ontology model that integrates diverse aspects of externalization. The second contribution is a Web application that validates the model through a case study in banking. Findings: The findings show that the Enterprise Ontology model and the patterns are pragmatic in externalizing the tacit knowledge of experts in a problem-solving scenario within a banking enterprise. Recommendations for Practitioners : Consider the diverse aspects (what, where, when, why, and how) during the tacit knowledge externalization process. Future Research: To extend the Enterprise Ontology model to include externalization from partially automated enterprise systems.

Aim/Purpose: This work aims to present a knowledge modeling technique that supports the representation of the student learning process and that is capable of providing a means for self-assessment and evaluating newly acquired knowledge. The objective is to propose a means to address the pedagogical challenges in m-learning by aiding students’ metacognition through a model of a student with the target domain and pedagogy. Background: This research proposes a framework for social and meta-cognitive support to tackle the challenges raised. Two algorithms are introduced: the meta-cognition algorithm for representing the student’s learning process, which is capable of providing a means for self-assessment, and the social group mapping algorithm for classifying students according to social groups. Methodology : Based on the characteristics of knowledge in an m-learning system, the cognitive knowledge base is proposed for knowledge elicitation and representation. The proposed technique allows a proper categorization of students to support collaborative learning in a social platform by utilizing the strength of m-learning in a social context. The social group mapping and metacognition algorithms are presented. Contribution: The proposed model is envisaged to serve as a guide for developers in implementing suitable m-learning applications. Furthermore, educationists and instructors can devise new pedagogical practices based on the possibilities provided by the proposed m-learning framework. Findings: The effectiveness of any knowledge management system is grounded in the technique used in representing the knowledge. The CKB proposed manipulates knowledge as a dynamic concept network, similar to human knowledge processing, thus, providing a rich semantic capability, which provides various relationships between concepts. Recommendations for Practitioners: Educationist and instructors need to develop new pedagogical practices in line with m-learning. Recommendation for Researchers: The design and implementation of an effective m-learning application are challenging due to the reliance on both pedagogical and technological elements. To tackle this challenge, frameworks which describe the conceptual interaction between the various components of pedagogy and technology need to be proposed. Impact on Society: The creation of an educational platform that provides instant access to relevant knowledge. Future Research: In the future, the proposed framework will be evaluated against some set of criteria for its effectiveness in acquiring and presenting knowledge in a real-life scenario. By analyzing real student interaction in m-learning, the algorithms will be tested to show their applicability in eliciting student metacognition and support for social interactivity.

Aim/Purpose: A vital business activity within organizations is tacit knowledge (TK) transfer. This work aims to propose a novel framework for TK transferability in multinational corporations (MNCs) from the information and communication technology (ICT) perspective. Background: In the past two decades, researchers have developed several frameworks for TK transfer based on humanistic, business, and educational perspectives. However, a review of the existing work revealed that TK transfer has seldom been examined from an ICT perspective. Methodology: A qualitative method was adopted because it was considered the most appropriate for the research requirements. A grounded theory approach was employed to generate the items that potentially affect the transferability of TK. Face-to-face semi-structured interviews were conducted to collect data, along with observations when possible. The study sample consisted of 20 expert interviewees in Malaysia. An inductive ongoing data analysis process based on grounded theory via open, axial, and selective coding was used as the primary analysis method. In addition, comparative and frequency count analyses were used to examine the collected data. Contribution: The main contribution of this study is its use of the grounded theory approach, which resulted in the generation of items that affect the transferability of TK, not all of which had been identified by previous researchers. This paper reports one of the few inductive studies conducted on TK transferability among MNCs and, therefore, makes distinct contributions to the literature on TK management, specifically bringing to the fore the intricacies involved in TK transferability. Findings: Information and communication technology plays a crucial role in and has a positive impact on TK transfer in MNCs. This study evaluated the potential of various ICT channels to facilitate TK transfer. The findings show that ICT tools cannot completely substitute for face-to-face contact. Tacit knowledge that has a high degree of complexity requires sophisticated channel features for its transfer. From this standpoint, virtual face-to-face communication is the richest communication medium in the ICT hierarchy. On the other hand, email and voicemail are less effective channels for TK transfer, while synchronous groupware is an intermediate communication medium. The findings highlight the importance of utilizing an appropriate mix of channels to improve TK transferability. Recommendations for Practitioners: It is recommended that practitioners consider the diverse aspects of TK and the potential of a range of ICT channels for the TK transfer process in order to increase the efficiency of TK transfer. The analytic categories developed in this study may give managers new insights into and a better understanding of how TK can be supported in a modern organization and how to overcome the barriers to TK transfer.

Aim/Purpose: The paper’s objective is to examine the challenges of using the mobile phone to mine location data for effective contact tracing of symptomatic, pre-symptomatic, and asymptomatic individuals and the implications of this technology for public health governance. Background: The COVID-19 crisis has created an unprecedented need for contact tracing across South Africa, requiring thousands of people to be traced and their details captured in government health databases as part of public health efforts aimed at breaking the chains of transmission. Contact tracing for COVID-19 requires the identification of persons who may have been exposed to the virus and following them up daily for 14 days from the last point of exposure. Mining mobile phone location data can play a critical role in locating people from the time they were identified as contacts to the time they access medical assistance. In this case, it aids data flow to various databases designated for COVID-19 work. Methodology: The researchers conducted a review of the available literature on this subject drawing from academic articles published in peer-reviewed journals, research reports, and other relevant national and international government documents reporting on public health and COVID-19. Document analysis was used as the primary research method, drawing on the case studies. Contribution: Contact tracing remains a critical strategy in curbing the deadly COVID-19 pandemic in South Africa and elsewhere in the world. However, given increasing concern regarding its invasive nature and possible infringement of individual liberties, it is imperative to interrogate the challenges related to its implementation to ensure a balance with public governance. The research findings can thus be used to inform policies and practices associated with contact tracing in South Africa. Findings: The study found that contact tracing using mobile phone location data mining can be used to enforce quarantine measures such as lockdowns aimed at mitigating a public health emergency such as COVID-19. However, the use of technology can expose the public to criminal activities by exposing their locations. From a public governance point of view, any exposure of the public to social ills is highly undesirable. Recommendations for Practitioners: In using contact tracing apps to provide pertinent data location caution needs to be exercised to ensure that sensitive private information is not made public to the extent that it compromises citizens’ safety and security. The study recommends the development and implementation of data use protocols to support the use of this technology, in order to mitigate against infringement of individual privacy and other civil liberties. Recommendation for Researchers: Researchers should explore ways of improving digital applications in order to improve the acceptability of the use of contact tracing technology to manage pandemics such as COVID-19, paying attention to ethical considerations. Impact on Society: Since contact tracing has implications for privacy and confidentiality it must be conducted with caution. This research highlights the challenges that the authorities must address to ensure that the right to privacy and confidentiality is upheld. Future Research: Future research could focus on collecting primary data to provide insight on contact tracing through mining mobile phone location data. Research could also be conducted on how app-based technology can enhance the effectiveness of contact tracing in order to optimize testing and tracing coverage. This has the potential to minimize transmission whilst also minimizing tracing delays. Moreover, it is important to develop contact tracing apps that are universally inter-operable and privacy-preserving.

Epidemiologists recommended contact-tracing as an effective control measure for the global infection like COVID-19 pandemic. Despite its effectiveness in infection containment, it has many limitations such as labour-intensive process, prone to human errors and most importantly, user privacy concerns. To address these shortcomings, we proposed location-aware blockchain-based hierarchical contact-tracing framework for anonymised data collection and processing. This infectious disease control framework serves both the infected users with localised alerts as well as stakeholders such as city officials and health workers with health statistics. Our proposed solution uses hierarchical network design that offloads individual infection block data to create hospital and city-level 'chains' for generating macro-level infection statistics. Results demonstrate that our system can represent the dynamic complexities of contract tracing in highly infection situations. Overall, our design emphasises on data processing and verification mechanism for large volume of infection data over a significant period of time for active risk assessment.

Aim/Purpose: The paper provides general background on the who, what, when, and why of deception. Methodology: It uses a naturalistic observational methodology. Whenever possible, the paper provides examples. Contribution: The research cited in this paper comes from a large variety of disparate fields of study. As such, it is one of the few multidisciplinary attempts to understand de-ception Findings: The research uncovered general principles for conducting deception and tactics that support these principles. Recommendation for Researchers: The authors hope that this paper’s finding will shed light on the topic of fake news as well as misinformation and disinformation, particularly in politics.

Aim/Purpose: The aim of this qualitative study is to explore social workers’ representations of hate speech (HS), the effects it has on the community, and socio-educational actions aimed at combating it. Background: Hate speech is any form of communication that promotes discrimination, hostility, or violence towards individuals or groups based on their identity. Although its spread is facilitated by particular characteristics of the online environment (such as anonymity and ubiquity), HS has pervasive consequences even in offline reality. In the last year, several community-based projects involving social workers have been implemented to address the problem. Professionals who work with the community play a crucial strategic role in the fight against HS. Therefore, it is imperative to begin by considering their perspective to gain a better understanding of HS and how it can be controlled. Methodology: Following a psycho-sociological perspective, six focus groups were conducted with 42 social workers (19 females and 23 males) belonging to associations or organizations of a different nature, such as NGOs, local social promotion organizations, universities, private social organizations, whose mission included the theme of countering hate speech. Contribution: There are no studies in the literature that consider the views of operators working to counter hate speech within communities. Our study contributes to deepening the knowledge of the phenomenon and identifying the most suitable strategies to combat it, starting from an approach that does not only focus on the online or offline dimension but on an integrated “onlife” approach. The study offers an outline of how hate speech affects the daily lives of the communities in the cities of Torino, Palermo, and Ancona. Additionally, it proposes a grassroots strategy to address hate speech. Findings: The results suggest that strategies effective in countering hate speech in offline contexts may not be effective in online environments. The technological revolution brought about by social media has significantly expanded the potential audience while weakening traditional communities. Addressing hate speech in the present context requires efforts to rebuild fragmented communities, gaining a thorough understanding of how the new virtual public space operates, and prioritizing hate speech as a specific concern only after these initial steps. Recommendation for Researchers: Hate speech represents a violation of human rights and a threat to freedom of expression. The spread of hateful messages has a significant impact on society, as it can negatively influence social cohesion, diversity, and inclusion. Understanding the causes and consequences of hate speech can help develop effective strategies to prevent and counter it, which is a crucial challenge for both research and society as a whole. Studying hate speech should involve the use of interdisciplinary methodologies. Future Research: Future research should focus on comparative analysis at the European Union level to assess the ability of civil society in other countries to develop effective strategies against hate speech.

Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

Background and Objective: In response to the challenges of poor mapping outcomes and susceptibility to obstacles encountered by indoor mobile vehicles relying solely on pure cameras or pure LiDAR during their movements, this paper proposes an obstacle avoidance method for indoor mobile vehicles that integrates image and LiDAR data, thus achieving obstacle avoidance for mobile vehicles. Materials and Methods: This method combines data from a depth camera and LiDAR, employing the Gmapping SLAM algorithm for environmental mapping, along with the A* algorithm and TEB algorithm for local path planning. In addition, this approach incorporates gesture functionality, which can be used to control the vehicle in certain special scenarios where “pseudo-obstacles” exist. The method utilizes the YOLO V3 algorithm for gesture recognition. Results: This paper merges the maps generated by the depth camera and LiDAR, resulting in a three-dimensional map that is more enriched and better aligned with real-world conditions. Combined with the A* algorithm and TEB algorithm, an optimal route is planned, enabling the mobile vehicles to effectively obtain obstacle information and thus achieve obstacle avoidance. Additionally, the introduced gesture recognition feature, which has been validated, also effectively controls the forward and backward movements of the mobile vehicles, facilitating obstacle avoidance. Conclusion: The experimental platform for the mobile vehicles, which integrates depth camera and LiDAR, built in this study has been validated for real-time obstacle avoidance through path planning in indoor environments. The introduced gesture recognition also effectively enables obstacle avoidance for the mobile vehicles.

This article extends the understanding of what impels buyers to contact particular sellers in online business-to-business (B2B) marketplaces, which are typically characterized by sparse social structures and concomitant limitations in observing social cues. Integrating an institutional perspective with signaling theory, our core argument is that offline seller characteristics that are visible online—in particular, geographic location and legal status—convey credible signals of seller behavior because they provide buyers with information on sellers' local institutional quality and the institutionally-induced obligations and controls acting on sellers. Using unique data from a large Italian online B2B marketplace between the fourth quarter of 1999 and July 2001, we find that both sellers' local institutional quality and their legal statuses affect a buyer's likelihood of contacting a seller. Moreover, consistent with the idea that a buyer's own local institutional quality generates a relevant reference point against which sellers are evaluated, we find that a buyer is progressively more likely to contact sellers the higher their local institutional quality relative to the buyer. Jointly, our findings imply that in online B2B marketplaces, signals conveyed by sellers' geographic locations and legal statuses may be substantive sources of competitive heterogeneity and market segmentation.

Management scholars have noted that leader departures often foreshadow higher turnover intentions (or lower organizational attachment) by subordinates left behind, especially when relationships between the departing leader and the subordinates, or leader-member exchanges (LMX), had been of high quality. In this paper, we posit that the quality of subordinates' relationships with all members of their relational system, not only their leader, must be considered to better understand how leader departures affect subordinates' organizational attachment. Our proposed relationships are illustrated in a theoretical model that includes phenomena at the individual-level (i.e., a subordinate's identification with the departing leader and with his/her organization), at the group-level (i.e., turnover contagion), and at the organizational level (i.e., organization-wide developmental climate). As such, we propose that elucidating how leader-departures affect organizational attachment requires multi-level theorizing and constructs. Theoretical and practical implications of such a 360-degree relational perspective on leader-departure effects are discussed.

Many online are raising questions about a recent spate of public violence, as officials continue to censor discussion.

RTAC Large Lasercut MOLLE Backpack with a Pistol Retention System is not just $20.99 after a sale and coupon code at check out. That is 75%+ off...

A fire at a Digital Realty Singapore datacenter by a lithium-ion battery explosion disrupts Alibaba Cloud services.