Trump va nommer les "faucons" Rubio et Waltz à la tête de la politique étrangère (rapports)

J'ai lu les titres et articles de presse à propos d'Asia Argento et de sa supposée agression contre Jimmy Bennett. Ça sent le traquenard à plein nez. 

Le type assure avoir été agressé sexuellement par l'actrice à l'âge de 17 ans. Il se fout de la gueule de qui, le petit traumatisé ? La prétendue agression ne l'empêchait pas de titrer quelques semaines après que "Maman" lui manquait. Pour la petite histoire, les deux acteurs jouent à la mère et au fils depuis qu'ils ont tourné ensemble dans un film alors que Jimmy avait 7 ans et qu'Asia incarnait sa ...

(Belga) Le président américain Joe Biden a nommé lundi une émissaire spéciale sur les droits de l'Homme en Corée du Nord, un poste laissé vacant pendant tout le mandat de l'ancienne administration de Donald Trump.

Le poste a été confié à une diplomate de carrière, Julie Turner, qui dirige actuellement la section Asie du bureau des droits de l'homme au sein du département d'État américain. Il avait été laissé vacant depuis janvier 2017, alors que l'ancien président Donald Trump cherchait à nouer des liens avec Pyongyang, rencontrant le dirigeant nord-coréen Kim Jong Un à trois reprises. Cela avait conduit à apaiser les tensions entre les deux pays, sans que cela ne débouche sur un accord à long terme. Le poste, qui a le rang d'ambassadeur, avait été créé par une loi en 2004 visant à faire pression sur la question des droits de l'homme en Corée du Nord, l'un des pays les plus autoritaires au monde. La tension est montée de façon spectaculaire ces derniers mois entre la Corée du Nord et son voisin du Sud ainsi que les États-Unis et le Japon en raison du nombre record en 2022 de tirs de missiles par Pyongyang. Dans son dernier rapport annuel sur les droits de l'homme, le département d'État avait dénoncé de vastes abus dans le pays communiste reclus, y compris l'emprisonnement de masse et le travail forcé. Mme Turner, qui parle couramment le français et le coréen selon sa biographie officielle, doit encore être confirmée à ce poste par un vote du Sénat américain. (Belga)

Hasard du calendrier ou volontￜ de prￜserver le nouveau Premier ministre ? Selon Mediapart, une enquￜte judiciaire ouverte par le parquet de Perpignan, potentiellement gￜnante pour Jean Castex, a ￜtￜ...

In this paper, a second-order sliding mode adaptive controller with finite time stability is proposed for trajectory tracking of robotic systems. In order to reduce the chattering phenomenon in the response of the variable structure resistant controller, two dependent sliding surfaces are used. In the outer loop, a kinematic controller is used to compensate the geometric uncertainty of the robot, and in the inner loop, the proposed resistive control is used as the main loop. On the other hand, considering the dynamic uncertainty and disturbance of the robot, an adaptive strategy has been used to estimate the uncertainty limit during the control process in order to eliminate the need for basic knowledge to determine the uncertainty limit in the resistant structure. The proposed control method demonstrates significant enhancements in performance, with the linear velocity error improving by approximately 80%, leading to a more accurate and responsive system.

With the expansion of business scope, the environment faced by enterprises has also changed, and competition is becoming increasingly fierce. Traditional financial systems are increasingly difficult to handle complex tasks and predict potential financial risks. In the context of the digital economy era, the booming financial sharing services have reduced labour costs and improved operational efficiency. This paper designs and implements an intelligent financial sharing platform, establishes a fund payment risk early warning model based on an improved support vector machine algorithm, and tests it on the Financial Distress Prediction dataset. The experimental results show that the effectiveness of using F2 score and AUC evaluation methods can reach 0.9484 and 0.9023, respectively. After using this system, the average financial processing time per order decreases by 43%, and the overall financial processing time decreases by 27%. Finally, this paper discusses the role of intelligent financial sharing platform in accounting transformation and optimisation of production.

The face of the hospitality sector in India is continuously changing and in times of career transitiveness, it is important to know the factors that support a successful career. The current research aims to explore the relationship between career planning, employee optimism, career adaptability and career satisfaction in the Indian hospitality sector. The study included 283 employees from Indian hospitality sector. Additionally, the study used SEM and bootstrap method to measure the dual mediating relationship between career planning, employee optimism dimensions, career adaptability dimensions, and career satisfaction in Indian setting. The results indicated that optimism dimensions and career adaptability dimensions partially mediate the relationship between career planning and career satisfaction in Indian hospitality sector. The study suggests useful implications for academia and industrial purpose. The limitations and future research avenues have been discussed. The study would contribute to the sparse literature on employee optimism, career planning, career adaptability and subjective career success. It would contribute to the social cognitive career theory (SCCT).

Aim/Purpose: This study seeks to understand the various ways information systems (IS) students experience introductory programming to inform IS educators on effective pedagogical approaches to teaching programming. Background: Many students who choose to major in information systems (IS), enter university with little or no experience of learning programming. Few studies have dealt with students’ learning to program in the business faculty, who do not necessarily have the computer science goal of programming. It has been shown that undergraduate IS students struggle with programming. Methodology: The qualitative approach was used in this study to determine students’ notions of learning to program and to determine their cognitive processes while learning to program in higher education. A cohort of 47 students, who were majoring in Information Systems within the Bachelor of Commerce degree programme were part of the study. Reflective journals were used to allow students to record their experiences and to study in-depth their insights and experiences of learning to program during the course. Using phenomenographic methods, categories of description that uniquely characterises the various ways IS students experience learning to program were determined. Contribution: This paper provides educators with empirical evidence on IS students’ experiences of learning to program, which play a crucial role in informing IS educators on how they can lend support and modify their pedagogical approach to teach programming to students who do not necessarily need to have the computer science goal of programming. This study contributes additional evidence that suggests more categories of description for IS students within a business degree. It provides valuable pedagogical insights for IS educators, thus contributing to the body of knowledge Findings: The findings of this study reveal six ways in which IS students’ experience the phenomenon, learning to program. These ways, referred to categories of description, formed an outcome space. Recommendations for Practitioners: Use the experiences of students identified in this study to determine approach to teaching and tasks or assessments assigned Recommendation for Researchers: Using phenomenographic methods researchers in IS or IT may determine pedagogical content knowledge in teaching specific aspects of IT or IS. Impact on Society: More business students would be able to program and improve their logical thinking and coding skills. Future Research: Implement the recommendations for practice and evaluate the students’ performance.

One of the key attempts towards a collective African vision is the New Economic Partnership for African Development (NEPAD). Barnard and Vonk (2003) report that “53 countries have been urged to implement ICTs in three crucial development arenas: education, health and trade”. While NEPAD and other initiatives have contributed to the provision of ICT infrastructure with positive results as seen in the growth of Internet uses, the disparities in development across Africa are enormous. The challenge to Higher Education Institutions in Africa has been summarised by Colle (2005): “central to creating digital resources and academic infrastructure is the question of universities’ relevance to the world around them, and especially to the challenge of being an active player – ‘an anchor of a broad-based poverty alleviation strategy’ in an increasingly knowledge-based economy”. It can be inferred from Colle that the activities of HEIs in Africa ought to be geared towards contributing to the realisation of the Millennium development goals.

Aim/Purpose: Although cassava is one of the crops that can be grown during the dry season in Northeastern Thailand, most farmers in the region do not know whether the crop can grow in their specific areas because the available agriculture planning guideline provides only a generic list of dry-season crops that can be grown in the whole region. The purpose of this research is to develop a predictive model that can be used to predict suitable areas for growing cassava in Northeastern Thailand during the dry season. Background: This paper develops a decision support system that can be used by farmers to assist them determine if cassava can be successfully grown in their specific areas. Methodology: This study uses satellite imagery and data on land characteristics to develop a machine learning model for predicting suitable areas for growing cassava in Thailand’s Nakhon-Phanom province. Contribution: This research contributes to the body of knowledge by developing a novel model for predicting suitable areas for growing cassava. Findings: This study identified elevation and Ferric Acrisols (Af) soil as the two most important features for predicting the best-suited areas for growing cassava in Nakhon-Phanom province, Thailand. The two-class boosted decision tree algorithm performs best when compared with other algorithms. The model achieved an accuracy of .886, and .746 F1-score. Recommendations for Practitioners: Farmers and agricultural extension agents will use the decision support system developed in this study to identify specific areas that are suitable for growing cassava in Nakhon-Phanom province, Thailand Recommendation for Researchers: To improve the predictive accuracy of the model developed in this study, more land and crop characteristics data should be incorporated during model development. The ground truth data for areas growing cassava should also be collected for a longer period to provide a more accurate sample of the areas that are suitable for cassava growing. Impact on Society: The use of machine learning for the development of new farming systems will enable farmers to produce more food throughout the year to feed the world’s growing population. Future Research: Further studies should be carried out to map other suitable areas for growing dry-season crops and to develop decision support systems for those crops.

Aim/Purpose: This paper reports our implementation of a prototype system, namely PRATO (Proposals Reviewers Automated Taxonomy-based Organization), for automatic assignment of proposals to reviewers based on categorized tracks and partial matching of reviewers’ profiles of research interests against proposal keywords. Background: The process of assigning reviewers to proposals tends to be a complicated task as it involves inspecting the matching between a given proposal and a reviewer based on different criteria. The situation becomes worse if one tries to automate this process, especially if a reviewer partially matches the domain of the paper at hand. Hence, a new controlled approach is required to facilitate the matching process. Methodology: Proposals and reviewers are organized into categorized tracks as defined by a tree of hierarchical research domains which correspond to the university’s colleges and departments. In addition, reviewers create their profiles of research interests (keywords) at the time of registration. Initial assignment is based on the matching of categorized sub-tracks of proposal and reviewer. Where the proposal and a reviewer fall under different categories (sub-tracks), assignment is done based on partial matching of proposal content against re-viewers’ research interests. Jaccard similarity coefficient scores are calculated of proposal keywords and reviewers’ profiles of research interest, and the reviewer with highest score is chosen. The system was used to automate the process of proposal-reviewer assignment at the Umm Al-Qura University during the 2017-2018 funding cycle. The list of proposal-reviewer assignments generated by the system was sent to human experts for voting and subsequently to make final assignments accordingly. With expert votes and final decisions as evaluation criteria, data system-expert agreements (in terms of “accept” or “reject”) were collected and analyzed by tallying frequencies and calculating rejection/acceptance ratios to assess the system’s performance. Contribution: This work helped the Deanship of Scientific Research (DSR), a funding agency at Umm Al-Qura University, in managing the process of reviewing proposals submitted for funding. We believe the work can also benefit any organizations or conferences to automate the assignment of papers to the most appropriate reviewers. Findings: Our developed prototype, PRATO, showed a considerable impact on the entire process of reviewing proposals at DSR. It automated the assignment of proposals to reviewers and resulted in 56.7% correct assignments overall. This indicates that PRATO performed considerably well at this early stage of its development. Recommendations for Practitioners: It is important for funding agencies and publishers to automate reviewing process to obtain better reviewing quality in a timely manner. Recommendation for Researchers: This work highlighted a new methodology to tackle the proposal-reviewer assignment task in an automated manner. More evaluation might be needed with consideration of different categories, especially for partially matched candidates. Impact on Society: The new methodology and knowledge about factors influencing the implementation of automated proposal-reviewing systems will help funding agencies and publishers to improve the quality of their internal processes. Future Research: In the future, we plan to examine PRATO’s performance on different classification schemes where specialty areas can be represented in graphs rather than trees. With graph representation, the scope for reviewer selection can be widened to include more general fields of specialty. Moreover, we will try to record the reasons for rejection to identify accurately whether the rejection was due to improper assignment or other reasons.

Aim/Purpose: Two research objectives were addressed in this study. The first objective was to determine the effect of knowledge management orientation behaviour on business performance, and the second objective was to investigate the mediating effect of market orientation in the relationship between knowledge management orientation behaviour and business performance. Background: In business strategic perspective, the idea of knowledge management has been discussed widely. However, there is a lack of study exploring the notion of knowledge management orientation especially in the perspective of Indonesia’s creative economy sector. Methodology: One hundred and thirty one participants were involved in this study. They were economy creative practitioners in Indonesia. Data were analysed by using Partial Least Squares. Contribution: Upon the completion of the research objectives, this study contributes to both theoretical and practical perspectives. From a theoretical standpoint, this study proposes a conceptual model explaining the relationship among knowledge management orientation behaviour, market orientation, and business performance in Indonesia’s creative economy sector. As this study found a significant effect of knowledge sharing in market orientation and market orientation in business performance, the study showed the mediation role of market orientation in the relationship between knowledge sharing and business performance. From a practical perspective, this study implies a guideline for business practitioners in enhancing business through the application of knowledge management orientation behaviour. Findings: The results show that organizing memory, knowledge absorption, and knowledge receptivity has a direct significant effect on business performance. However, in affecting business performance, knowledge sharing must be mediated by market orientation. Recommendations for Practitioners: Based on the results of the study, practitioners should enhance their behaviour in implementing knowledge management in terms of increasing business performance. In addition, it is suggested that business practitioners must be market driven, as market orientation was found to have an important role in affecting business performance. Recommendation for Researchers: Future researchers might integrate other constructs such as innovation, marketing capabilities, or organizational learning with this current conceptual model to have more comprehensive insight about the relationship between knowledge management orientation and business performance. Impact on Society: This study suggests that business practitioners must have knowledge management driven behaviour as well as market orientation to enhance the performance of their business. Future Research: Future research might add other variables to make the conceptual model more comprehensive and also replicate this study into different industrial settings.

Aim/Purpose: Accurate detection of vacant parking spaces is crucial for autonomous parking. Deep learning, particularly Graph Neural Networks (GNNs), holds promise for addressing the challenges of diverse parking lot appearances and complex visual environments. Our GNN-based approach leverages the spatial layout of detected marking points in around-view images to learn robust feature representations that are resilient to occlusions and lighting variations. We demonstrate significant accuracy improvements on benchmark datasets compared to existing methods, showcasing the effectiveness of our GNN-based solution. Further research is needed to explore the scalability and generalizability of this approach in real-world scenarios and to consider the potential ethical implications of autonomous parking technologies. Background: GNNs offer a number of advantages over traditional parking spot detection methods. Unlike methods that treat objects as discrete entities, GNNs may leverage the inherent connections among parking markers (lines, dots) inside an image. This ability to exploit spatial connections leads to more accurate parking space detection, even in challenging scenarios with shifting illumination. Real-time applications are another area where GNNs exhibit promise, which is critical for autonomous vehicles. Their ability to intuitively understand linkages across marking sites may further simplify the process compared to traditional deep-learning approaches that need complex feature development. Furthermore, the proposed GNN model streamlines parking space recognition by potentially combining slot inference and marking point recognition in a single step. All things considered, GNNs present a viable method for obtaining stronger and more precise parking slot recognition, opening the door for autonomous car self-parking technology developments. Methodology: The proposed research introduces a novel, end-to-end trainable method for parking slot detection using bird’s-eye images and GNNs. The approach involves a two-stage process. First, a marking-point detector network is employed to identify potential parking markers, extracting features such as confidence scores and positions. After refining these detections, a marking-point encoder network extracts and embeds location and appearance information. The enhanced data is then loaded into a fully linked network, with each node representing a marker. An attentional GNN is then utilized to leverage the spatial relationships between neighbors, allowing for selective information aggregation and capturing intricate interactions. Finally, a dedicated entrance line discriminator network, trained on GNN outputs, classifies pairs of markers as potential entry lines based on learned node attributes. This multi-stage approach, evaluated on benchmark datasets, aims to achieve robust and accurate parking slot detection even in diverse and challenging environments. Contribution: The present study makes a significant contribution to the parking slot detection domain by introducing an attentional GNN-based approach that capitalizes on the spatial relationships between marking points for enhanced robustness. Additionally, the paper offers a fully trainable end-to-end model that eliminates the need for manual post-processing, thereby streamlining the process. Furthermore, the study reduces training costs by dispensing with the need for detailed annotations of marking point properties, thereby making it more accessible and cost-effective. Findings: The goal of this research is to present a unique approach to parking space recognition using GNNs and bird’s-eye photos. The study’s findings demonstrated significant improvements over earlier algorithms, with accuracy on par with the state-of-the-art DMPR-PS method. Moreover, the suggested method provides a fully trainable solution with less reliance on manually specified rules and more economical training needs. One crucial component of this approach is the GNN’s performance. By making use of the spatial correlations between marking locations, the GNN delivers greater accuracy and recall than a completely linked baseline. The GNN successfully learns discriminative features by separating paired marking points (creating parking spots) from unpaired ones, according to further analysis using cosine similarity. There are restrictions, though, especially where there are unclear markings. Successful parking slot identification in various circumstances proves the recommended method’s usefulness, with occasional failures in poor visibility conditions. Future work addresses these limitations and explores adapting the model to different image formats (e.g., side-view) and scenarios without relying on prior entry line information. An ablation study is conducted to investigate the impact of different backbone architectures on image feature extraction. The results reveal that VGG16 is optimal for balancing accuracy and real-time processing requirements. Recommendations for Practitioners: Developers of parking systems are encouraged to incorporate GNN-based techniques into their autonomous parking systems, as these methods exhibit enhanced accuracy and robustness when handling a wide range of parking scenarios. Furthermore, attention mechanisms within deep learning models can provide significant advantages for tasks that involve spatial relationships and contextual information in other vision-based applications. Recommendation for Researchers: Further research is necessary to assess the effectiveness of GNN-based methods in real-world situations. To obtain accurate results, it is important to employ large-scale datasets that include diverse lighting conditions, parking layouts, and vehicle types. Incorporating semantic information such as parking signs and lane markings into GNN models can enhance their ability to interpret and understand context. Moreover, it is crucial to address ethical concerns, including privacy, potential biases, and responsible deployment, in the development of autonomous parking technologies. Impact on Society: Optimized utilization of parking spaces can help cities manage parking resources efficiently, thereby reducing traffic congestion and fuel consumption. Automating parking processes can also enhance accessibility and provide safer and more convenient parking experiences, especially for individuals with disabilities. The development of dependable parking capabilities for autonomous vehicles can also contribute to smoother traffic flow, potentially reducing accidents and positively impacting society. Future Research: Developing and optimizing graph neural network-based models for real-time deployment in autonomous vehicles with limited resources is a critical objective. Investigating the integration of GNNs with other deep learning techniques for multi-modal parking slot detection, radar, and other sensors is essential for enhancing the understanding of the environment. Lastly, it is crucial to develop explainable AI methods to elucidate the decision-making processes of GNN models in parking slot detection, ensuring fairness, transparency, and responsible utilization of this technology.

The world's economies are attempting to transform themselves to have a greater focus on developing knowledge as a commodity through innovation. Innovation starts with a creative activity that yields an invention but is augmented through a systematic value driven knowledge management system to yield new knowledge that can create a competitive advantage. To succeed in such an economy, organisations must have or develop the talent that can produce and use information effectively, they must have an ambidextrous organisational structure that allows them to innovate and produce simultaneously, and they must have an innovation management system to sustain effective innovation. In this paper we show how to augment existing university courses to simultaneously develop subject matter and innovation skills in students. We also suggest the incorporation of the new Innovations Management System Standard Series ISO 56000 into business curricula to better prepare students to function in the knowledge economy.

This study attempts to present the variety of possible uses for iPads, in the learning process. The objective is to evaluate a unique implementation model that was tried out at a teacher training college in Israel. The methodology is based on a qualitative research paradigm. The findings show that students’ use the iPads in various contexts: (a) for ongoing personal use; (b) for planning lessons; (c) for active integration in the classroom; and (d) for reading and developing content and games. These findings are summarized in a chart that shows the different uses as levels in a hierarchical taxonomy. Analysis of the iPad’s pedagogical uses may shed light on the various skills students need to acquire in order to become teachers in 21st century. In addition, understanding the various iPad uses and their frequency can affect decision-making at the level of policy in the field of implementation of the use of mobile technologies in educational institutions.

Labor codes have been voluntarily adopted and used by manufacturers in emerging economies for the past two decades, as a means of ensuring minimally acceptable or core labor standards for workers. However, far too little is known of the potential benefits from the voluntary adoption of labor codes to the manufacturer, and prior human resource management research has been virtually silent on the business implications of their use for emerging economy manufacturers participating in global supply chains. Drawing on previous work across multiple disciplines and proposing a framework that extends human resource management theory more explicitly and rigorously to the context of emerging economy manufacturing, I theorize and demonstrate that the voluntary adoption of a labor code may constitute an effective human resource investment in emerging economies in improving establishment-level employee outcomes and operational and financial performance. The hypotheses are tested using longitudinal data on a sample of apparel manufacturing plants in Sri Lanka. Implications of this study include providing insight into how to expand the scope and relevance of human resource management theory to better understand research and practice in emerging economies.

The burgeoning economic inequality between the richest and the poorest is a cause of concern for social, political, and ethical reasons. While businesses are both implicated and affected by growing inequality, business schools have largely neglected to subject the phenomenon to sufficient critique. This is, in part, because far too many management educators rely on orthodox economic perspectives—often represented by neoliberal capitalism—which have dominated the curricula and the teaching philosophy of business schools. To address this issue, this article underscores the need for business schools to critically examine the relationship between neoliberal capitalism and economic inequalities, and to overtly engage with this nexus in pedagogical practice. The article concludes by revisiting the concepts of relationality and answerability as paths by which to address the current predicament. Relationality and answerability collectively offer: i) conceptual and reflexive tools by which to re-imagine business school education, and, ii) space for business schools to debate important questions about the taken-for-granted, but problematic, assumptions underlying the ideology of neoliberal capitalism

DONALD Trump’s victory in the 2024 US presidential election has raised global concerns about how his economic policies may impact countries like Malaysia.

With an “America First” approach focused on protecting domestic interests, the Trump administration is expected to reshape international trade, shift investment flows and influence geopolitical relationships.

For Malaysia, this outcome presents not only challenges but also opportunities in key economic sectors, including trade, foreign investment and commodities.

Trump is anticipated to continue protectionist policies that prioritise US jobs and domestic production. His proposal to impose a 10% import tariff on all goods entering the US aims to reduce reliance on foreign products and bolster domestic manufacturing.

Additionally, Trump’s plan to impose tariffs as high as 60% on Chinese products could have significant implications for Malaysia, one of the major exporters of electronic products and components to the US. If high tariffs are applied to Chinese goods, Malaysian products incorporating Chinese components could also be impacted, potentially diminishing US demand for Malaysian exports.

While this situation presents risks, it also provides opportunities as companies diversify supply chains away from China. Malaysia benefitted from the “China+1” strategy during Trump’s first term, as exports to the US increased amid US-China trade tensions.

Malaysia’s semiconductor industry, a focus of large investments from multinational companies such as Intel and Infineon, may continue to attract interest as a stable manufacturing base.

Currently, Malaysia holds around 13% of the global market in chip packaging and testing, making it a favourable location for companies seeking to expand operations outside of China. These conditions indicate Malaysia’s potential to further establish itself as a manufacturing hub if it can maintain political stability and investor-friendly economic policies.

The energy sector is also likely to be affected. Trump’s pro-oil stance could lead to increased US production and exports of fossil fuels. Should global oil prices rise, Malaysia, as an oil exporter, stands to benefit from higher national revenue.

However, rising oil prices also carry inflationary risks, as increased energy costs could drive up production costs and consumer prices domestically. While the energy sector may gain, higher energy costs could pressure consumer purchasing power and escalate operational costs for local industries.

To maximise these potential gains, Malaysia will need to balance these impacts on the consumer sector and ensure monetary policies support price stability.

The Malaysian commodity sector, particularly palm oil, faces potential challenges as well. During Trump’s first term, the US imposed import restrictions on Malaysian palm oil companies such as FGV Holdings and Sime Darby Plantation over allegations of forced labour. These restrictions affected Malaysian palm oil exports to the US, reducing revenue and harming the country’s image as a responsible producer.

Should similar policies persist, Malaysia will need to strengthen sustainable labour practices and meet international standards to retain access to global markets and protect its reputation as an ethical producer.

Trump’s policies could bring added uncertainty to Malaysia’s capital markets and the ringgit’s value. With US interest rates currently at 4.75%-5.00%, any influence Trump may exert on the Federal Reserve to raise rates could lead global investors to favour US assets, potentially causing capital outflows from Malaysia.

In 2023, Malaysia saw a 6.8% decline in foreign equity inflows, and the ringgit depreciated by around 8% against the US dollar. This shift reduces liquidity in local capital markets, and foreign investors may approach Malaysian equities with greater caution, especially if Trump’s policies introduce additional tariffs or trade restrictions.

As demand for the US dollar rises, the ringgit may face continued downward pressure. A weaker ringgit could increase import costs, particularly in vital sectors like food and technology, compounding domestic inflationary pressures, which currently stand at 2.8%.

To address these challenges, Malaysia needs a strong risk management strategy to maintain market stability and support the ringgit amid growing uncertainties.

In addition, Trump’s protectionist stance may directly impact Foreign Direct Investment (FDI) into Malaysia. As a manufacturing hub in Southeast Asia, Malaysia could see reduced FDI if the US pursues an aggressive stance on countries with significant trade surpluses.

Trump’s emphasis on protecting US jobs and domestic economic interests may lead to decreased investment from US companies in Malaysia.

Concurrently, prolonged US-China trade tensions could make investors more cautious about Malaysia, which may be perceived as politically and economically vulnerable. Any decline in FDI could affect job creation, technology growth and Malaysia’s long-term economic stability.

Furthermore, Trump’s victory raises concerns about the future of the US-led Indo-Pacific Economic Framework (Ipef). Trump has previously expressed a desire to withdraw from trade agreements like Ipef, which he sees as “another TPP”. If this happens, Malaysia may face challenges in maintaining market access and regional economic integration.

To prepare, Malaysia must diversify its trade partnerships, strengthen local industries and foster growth in resilient sectors. Malaysia’s involvement in Ipef reflects its commitment to regional economic integration, which could help mitigate the negative effects of US protectionist policies.

In summary, Trump’s victory could have significant implications for Malaysia’s economy. Protectionist policies and prolonged trade tensions could disrupt global supply chains, increase market uncertainty and challenge Malaysia’s economic growth.

Malaysia must be prepared with sustainable and adaptable strategies to tackle these challenges while capitalising on emerging opportunities to maintain economic resilience amid an increasingly complex global landscape.

The writer is a researcher and Islamic Finance consultant.
Comments: letters@thesundaily.com

WASHINGTON: U.S. President-elect Donald Trump said on Tuesday he has picked Fox News Channel host Pete Hegseth to be secretary of defense, tapping an outsider who has railed against diversity in the military.

“Pete is tough, smart and a true believer in America First,“ Trump said in a statement. “With Pete at the helm, America’s enemies are on notice - Our Military will be Great Again, and America will Never Back Down.”

Hegseth is an Army National Guard veteran and according to his website served in Afghanistan, Iraq, and Guantanamo Bay, Cuba.

Hegseth has said he left the Army in 2021 after being deemed an extremist by an Army that didn't want him anymore.

“The feeling was mutual -- I didn’t want this Army anymore either,“ Hegseth said in his book “The War on Warriors: Behind the Betrayal of the Men Who Keep Us Free.”

There is already anxiety in the Pentagon that Trump aims to root out military officers and career civil servants he perceives to be disloyal.

Culture war issues could be one trigger for firings. Trump was asked by Fox News in June whether he would fire generals described as “woke,“ a term for those focused on racial and social justice but which is used by conservatives to disparage progressive policies.

“At a basic level, do we really want only the woke ‘diverse’ recruits that the Biden administration is curating to be the ones with the guns and the guidons?” Hegseth wrote in “The War on Warriors,“ which was published in June.

“We want those diverse recruits -- pumped full of vaccines and even more poisonous ideologies -- to be sharing a basic training bunk with sane Americans,“ he said.

Trump's former U.S. generals and defense secretaries are among his fiercest critics, with some declaring him unfit for office. Angered, Trump has suggested that his former chairman of the Joint Chiefs of Staff, Mark Milley, could be executed for treason.

Shehbaz expresses optimism that interest rate cut will boost investor confidence and increase investments in Pakistan

Activism: In the ever-evolving landscape of politics, we often find ourselves confined to the traditional left-right spectrum. This binary view, with its emphasis on economic and social policies, sometimes obscures deeper motivations driving voter behavior and political trends. As a result, we might miss crucial insights that could enhance our understanding of why people vote the way they do, why political movements gain momentum, and why some ideas resonate while others falter. I have found an alternative framework to be far more helpful: the Maslow Pyramid.

Abraham Maslow’s hierarchy of needs, typically illustrated as a pyramid, categorizes human needs into five levels: physiological, safety, love/belonging, esteem, and self-actualization. Maslow famously hypothesized, that a lower-level need must be satisfied before we start attempting to fulfill the needs of the next level — as an example, while we’re starving and live in fear of being robbed (level one), we’re not so much concerned with having the respect of the community (level four). By examining political trends through this lens, we can gain a richer, more nuanced perspective on what drives societal shifts and voter preferences.

Physiological Needs and the Politics of Survival

At the base of Maslow’s pyramid are physiological needs: food, water, warmth, and rest — as well as immediate physical safety. In times of economic crisis, political discourse often gravitates towards these fundamental concerns. Populist movements frequently gain traction by promising to address the immediate needs of the people. For instance, during the Great Recession, there was a surge in support for policies focused on job creation, healthcare access, and basic economic security. Politicians who can convincingly address these basic needs often see significant support from constituencies facing hardship just getting from one day to the next without getting beaten, robbed, or starved. Fear of getting to this state (fear of getting robbed on your way to/from work, school, etc.) will also suffice to place oneself at this level.

However, if established parties fail to address these concerns, voters will inevitably turn to whomever offers a solution, even if it’s an atrocious one. It’s like choosing Comcast for your Internet connection when no other provider is available—you know the service is subpar, but having some connection is better than none. Similarly, in politics, when mainstream parties neglect the foundational needs of the populace, fringe or extremist parties can gain support by simply acknowledging and addressing these unmet needs — and that is regardless of how flawed their solutions to said problems may be.

Safety Needs and the Demand for Stability

Moving up the pyramid, once the physical needs are met, then safety needs encompass longer-term personal security, employment, and health. Political rhetoric around law and order, immigration control, and national security taps into these safety concerns. When people feel their safety is threatened, whether by crime, terrorism, or economic instability, they are more likely to support policies and leaders who promise to restore stability and protect them from perceived threats. The post-9/11 era (just after 2001), with its heightened focus on national security, is a prime example of how safety needs can dominate the political agenda.

Yet again, if traditional parties fail to provide a sense of security, voters may gravitate towards any party that promises to deliver it, even if their methods are draconian and/or frankly ridiculous.

Love and Belonging: The Politics of Identity

The middle tier of the pyramid addresses social needs: relationships, friendships, and a sense of belonging. Identity politics, which includes movements advocating for the rights of specific social groups based on race, gender, sexual orientation, and more, finds its roots here. Political movements that foster a sense of community and belonging can galvanize supporters by addressing these intrinsic needs. The LGBPQRST+ rights movement, for instance, not only fights for legal rights but also seeks to create a supportive community for its members.

When mainstream parties overlook these social needs, people will seek out any group or party that offers them a sense of belonging, even if that party’s overall agenda is problematic. It’s a matter of seeking connection where it’s available.

Esteem: The Quest for Recognition

Esteem needs encompass respect, self-esteem, status, and recognition. Political leaders who can validate the contributions and worth of their supporters often build strong, loyal followings. This is evident in political campaigns that emphasize the dignity of work, the importance of patriotism, and the recognition of personal achievements. Policies aimed at rewarding hard work and providing opportunities for personal advancement resonate deeply with voters seeking validation and respect.

Self-Actualization: The Pursuit of Fulfillment

At the peak of the pyramid is self-actualization — the realization of one’s potential and the pursuit of personal growth and fulfillment. Politics at this level involves visionary thinking and appeals to higher ideals. Environmental movements with or without solutions based in reality, space exploration initiatives, and educational reforms often engage this need. Leaders who inspire through their vision of a better future, who challenge citizens to think beyond their immediate concerns and contribute to something greater than themselves, tap into this highest level of human motivation.

It’s rather telling that the biggest telltale sign for voters (and media), who are personally at this level of human needs, is that they often and happily paint the political parties and movements answering to level-one and level-two human needs as brutish, uneducated, simpleton and backwards — when in reality, what such name-calling voters who pretend to hold themselves to some sort of higher standard are really doing, is disacknowledging that other people’s most basic needs are simply not being met. Talk about being overprivileged in ivory towers! “Let them eat cake”, anyone?

If mainstream political parties neglect to engage voters at this level, people will align with any party that inspires them, even if the broader agenda is not entirely sound. It can be somewhat like signing up for a self-help seminar led by a guy who lives in his mom’s basement because he speaks so passionately about “unlocking your potential.”

A Holistic Approach to Political Analysis

By applying the Maslow Pyramid to our understanding of political trends, we gain a multi-dimensional view that goes beyond the simplicity of left versus right. This approach allows us to see how different policies and political messages resonate with various segments of the population based on their current needs and aspirations.

For instance, a comprehensive healthcare reform policy can address physiological needs by ensuring access to medical care, safety needs by providing financial security, love and belonging by reducing social disparities, esteem by recognizing healthcare as a right, and self-actualization by promoting a healthier society capable of achieving its full potential.

It’s further important to realize that an individual voter would vote for completely different parties, even at opposite ends of the traditional spectrum, depending on where they feel the most urgency in their personal needs at the moment, and that this is not a contradiction or uncertainty on policies.

In conclusion, the Maslow Pyramid provides a valuable framework for understanding the complex and dynamic nature of political trends. It reminds us that politics is fundamentally about people and their needs. By considering these needs in our political analysis, we can develop more empathetic, effective, and inclusive strategies that resonate deeply with the human condition. And crucially, we must remember that when these needs are ignored, voters will turn to any party that promises to meet them, even if it means accepting a deeply flawed solution. After all, in the absence of better options, you might just end up with Comcast.

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