Differential Strategies Research Articles

Enabling Efficient Oxygen Evolution via Anchoring Carbon-Layer-Confined RuOx on a Well-Matched Substrate.

Oxygen evolution reaction (OER) is a multistep proton-coupled four-electron process with sluggish kinetics, which seriously limits the hydrogen production efficiency, thus it is of great importance to develop an efficient and stable OER catalyst. In this study, a two-step differential pyrolysis strategy is employed to design a three-dimensional porous microstructured material consisting of RuOx nanoparticles coated by a thin-layer carbon, where the active particles were isolated in separate chambers and the RuOx nanoparticles mainly existed in the form of a heterogeneous interface between RuO2 and partial metallic Ru. The preparation parameters of the catalysts are optimized via combining transient and steady-state polarization properties, and the target catalyst Cat-500-1.5t shows the best OER catalytic performance after ca. 60 h of a chronopotentiometry test in an acidic medium with a much smaller performance change than other samples. The unique design of adopting a carbon layer to form separate reaction chambers largely mitigates the excessive oxidation loss of the active components under strong oxidation potential. The suitability of the catalyst with the loaded substrate and test media is explored, and in an acidic medium, the carbon paper is much better than the titanium fiber, while in an alkaline medium, the titanium fiber is obviously superior to the carbon paper. On both carbon paper and titanium fiber, the performance in an alkaline medium outperforms that in an acidic medium, and the possible reasons for the performance difference are analyzed. Herein, to obtain the actual electrocatalytic performance, the optimal design of the catalyst structure and matching suitable conductive substrate in a specific medium are quite necessary, which provides a feasible strategy for the acquisition of efficient and stable electrocatalysts and the desirable presentation of performance.

Dual-path differential perturbation sand cat swarm optimization algorithm integrated with escape mechanism

In this paper, based on the sand cat swarm optimization (SCSO) algorithm, a dual-path differential perturbation sand cat swarm optimization algorithm integrated with escape mechanism (EDSCSO) is proposed. EDSCSO aims to solve the problems of the original SCSO, such as the limited diversity of the population, low efficiency of solving complex functions, and ease of falling into a local optimal solution. First, an escape mechanism was proposed to balance the exploration and exploitation of the algorithm. Second, a random elite cooperative guidance strategy was used to utilize the elite population to guide the general population to improve the convergence speed of the algorithm. Finally, the dual-path differential perturbation strategy is used to continuously perturb the population using two differential variational operators to enrich population diversity. EDSCSO obtained the best average fitness for 27 of 39 test functions in the IEEE CEC2017 and IEEE CEC2019 test suites, indicating that the algorithm is an efficient and feasible solution for complex optimization problems. In addition, EDSCSO is applied to optimize the three-dimensional wireless sensor network coverage as well as the unmanned aerial vehicle path planning problem, and it provides optimal solutions for both problems. The applicability of EDSCSO in real-world optimization scenarios was verified.

A millisecond pulsar position determined to 0.2 mas precision with VLBI

Context. Precise millisecond pulsar (MSP) positions determined with very long baseline interferometry (VLBI) hold the key to building the connection between the kinematic and dynamic reference frames respectively used for VLBI and pulsar timing. A frame connection would provide an important pathway to examining the planetary ephemerides used in pulsar timing, and would potentially enhance the sensitivities of the pulsar timing arrays used to detect stochastic gravitational-wave background in the nano-Hz regime. Aims. We aim to significantly improve the precision of the VLBI-based MSP position (from >1 mas at present) by reducing the two dominant components in the positional uncertainty – the propagation-related uncertainty and the uncertainty resulting from the frequency-dependent core shifts of the reference sources. Methods. We introduce a new differential astrometry strategy called PINPT (Phase-screen Interpolation plus frequeNcy-dePendent core shifT correction; pronounced “pinpoint”), which entails the use of multiple calibrators observed at several widely separated frequencies. The strategy allows determination of the core shift and mitigates the impact of residual delay in the atmosphere. We implemented the strategy on PSR J2222-0137, an MSP that is well constrained astrometrically with VLBI and pulsar timing. Results. Using the PINPT strategy, we determined core shifts for four AGNs around PSR J2222-0137, and derived a VLBI-based pulsar position with uncertainties of 0.17 mas and 0.32 mas in Right Ascension and Declination, respectively, approaching the uncertainty level of the best-determined timing-based MSP positions. Additionally, incorporating the new observations into historical ones, we refined the pulsar proper motion and the parallax-based distance to the <10 µas yr-1 level and the subparsec level, respectively. Conclusions. The realization of the PINPT strategy promises a factor-of-five positional precision enhancement (over conventional VLBI astrometry) for all kinds of compact radio sources observed at <2 GHz, including most fast radio bursts.

Proteomic identification of apoplastic proteins from rice, wheat, and barley after Magnaporthe oryzae infection

The fungal pathogen Magnaporthe oryzae causes devastating blast disease in various cereals, including rice (Oryza sativa), wheat (Triticum aestivum), maize (Zea mays), and barley (Hordeum vulgare). Despite previous reports on fungal host specificity, the mechanisms underlying differential host infection strategies remain unclear. This study aimed to identify differentially abundant proteins (DAPs) in the apoplast of rice, barley, and wheat following infection with two M. oryzae pathovars using liquid chromatography-tandem mass spectrometry (LC–MS/MS). LC–MS/MS analysis revealed an enrichment of both M. oryzae and host proteins in the apoplast during the compatible reaction compared to the incompatible reaction. DAPs from M. oryzae involved in the host interaction included secreted extracellular enzymes (e.g., hydrolases), which were significantly increased in the M. oryzae Oryzae (MoO)-infected rice apoplast. Among host proteins, the proportion of protein-modifying enzymes increased in the M. oryzae Triticum (MoT)-infected rice and MoO-infected wheat apoplastic fluids, particularly rice glycosidases, peroxidases, and serine proteases, as well as wheat serine proteases. Furthermore, DAPs from MoL-infected rice were enriched in carbohydrate metabolism, suggesting that carbohydrate metabolism-related proteins may play a vital role in rice resistance to MoL. Additionally, protein-modifying and cytoskeletal proteins, as well as stress-responsive proteins, were enriched in the MoO-infected wheat apoplastic fluid. Finally, DAPs from both MoO- and MoL-infected barley were enriched in hydrogen peroxide catabolism, suggesting that peroxidases may be vital for barley resistance to M. oryzae. The identification of DAPs from both M. oryzae strains and the three host plants offers valuable insights into the host specificity mechanisms of M. oryzae in cereal crops.

Patterns of sperm swimming behaviour depend on male mating tactic and spawning environment in chinook salmon

Many species exhibit alternative mating tactics (ARTs), with larger socially dominant males competing for females and smaller males adopting “sneaker” strategies to exploit fertilisation opportunities without competition or courtship. Females typically prefer larger socially dominant males, but their ability to manipulate mating or fertilisation outcomes is largely unknown. Here, using chinook salmon Oncorhynchus tshawytscha, we examined whether the female’s ovarian fluid (OF) differentially influences the temporal patterns of sperm swimming traits in ejaculates from non-preferred sneaker (‘parr’) and preferred (dominant) males. Results demonstrate that OF improves sperm swimming speed and linearity compared to river water, regardless of male mating tactic. We report a novel tactic-specific difference in sperm linearity in which parr male sperm initially maintain straighter trajectories in river water, compared to dominant males, but then rapidly change to less linear and more circular paths over time. Intriguingly, we show that OF counteracts this change in sperm linearity in parr males so that patterns become indistinguishable from dominants when parr sperm swim in OF. Together, these results show that male chinook salmon exhibit differential sperm trait investment strategies depending on reproductive tactic.

Impact of Mild Field Drought on the Aroma Profile and Metabolic Pathways of Fresh Tea (Camellia sinensis) Leaves Using HS-GC-IMS and HS-SPME-GC-MS.

Aroma plays a pivotal role in defining tea quality and distinctiveness, and tea producers have often observed that specific drought conditions are closely associated with the formation and accumulation of characteristic aroma compounds in tea leaves. However, there is still limited understanding of the differential strategies employed by various tea cultivars in response to drought stress for the accumulation of key volatile aroma compounds in fresh tea leaves, as well as the associated metabolic pathways involved in aroma formation. In this study, two widely cultivated tea cultivars in China, Fuding Dabai (FD) and Wuniuzao (WNZ), were examined to assess the impact of mild field drought stress on the composition and accumulation of key volatile aroma compounds in fresh leaves using headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) and headspace solid phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) technologies. Results revealed that drought stress led to a substantial increase in the diversity of volatile compounds (VOCs) in FD, while WNZ exhibited a notable rise in low-threshold VOC concentrations, amplifying sweet, floral, fruity, and earthy aroma profiles in post-drought fresh leaves. Through partial least squares discriminant analysis (PLS-DA) of HS-GC-IMS and HS-SPME-GC-MS data, integrating variable importance projection (VIP) scores and odor activity values (OAVs) above 1, 9, and 13, key odor-active compounds were identified as potential markers distinguishing the drought responses in the two cultivars. These compounds serve as crucial indicators of the aromatic profile shifts induced by drought, providing insights into the differential metabolic strategies of the cultivars. Additionally, KEGG enrichment analysis revealed 12 metabolic pathways, such as terpenoid biosynthesis, fatty acid synthesis, cutin, suberine, and wax biosynthesis, and phenylalanine metabolism, which may play crucial roles in the formation and accumulation of VOCs in tea leaves under drought stress. These findings provide a comprehensive framework for understanding the cultivar-specific mechanisms of aroma formation and accumulation in tea leaves under mild drought conditions.

An adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies for influence maximization in social networks

Influence maximization is a critical research topic of social network analysis, particularly with the increasing involvement of individuals in the global networked society. The purpose of the problem is to identify k influential nodes from the social network and activate them initially to maximize the expected number of influenced nodes at the end of the spreading process. Although some meta-heuristics based on swarm intelligence or biological evolution have been proposed to tackle this intractable problem, further investigation is required to refine the exploration and exploitation operations based on the iterative information from the evolutionary process. In this paper, an adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies is proposed for the influence maximization problem. In order to enhance the evolutionary capability of the later stages of the discrete differential evolution, the mutation in the framework, consisting of three policies, namely comprehensive learning particle swarm mutation strategy, differential mutation strategy, and perturbation strategy, is implied based on different probabilistic models. An adaptive local search strategy is presented to improve the local optimum results based on a potential alternative library consisting of structural hole nodes, which guides the differential evolution to find a more optimal solution. Experimental results on six real-world social networks demonstrate the competitive performance of the proposed algorithm in terms of both efficacy and efficiency compared to state-of-the-art algorithms.

HSMAOA: An enhanced arithmetic optimization algorithm with an adaptive hierarchical structure for its solution analysis and application in optimization problems

The Arithmetic Optimization Algorithm (AOA) has recently gained significant attention as a novel meta-heuristic algorithm. However, it faces challenges such as premature convergence and entrapment in local optima when addressing complex optimization problems. To overcome these limitations, this paper proposes an enhanced AOA, termed the Self-Adaptive Hierarchical Arithmetic Optimization Algorithm (HSMAOA). The proposed method integrates three key strategies: Firstly, a spiral-guided random walk mechanism is introduced to improve global search ability. Secondly, a novel adaptive hierarchy leader and follower mechanism is proposed, which establishes a complete multi-branch tree hierarchy with decreasing branching degrees within the population, thereby increasing information exchange among population individuals to escape local optima. Finally, a differential mutation strategy based on ranked selection is introduced to enhance candidate solution quality. HSMAOAʼs performance was evaluated on the CEC2022 test suite against some state-of-the-art algorithms. Results, including optimization accuracy analysis, convergence curves, and various statistical tests, demonstrate HSMAOAʼs superior optimization capability and robustness. In addition, tests on eight engineering structure optimization problems, including the pressure vessel design problem, the multiple disk clutch brake design problem, and the step-cone pulley problem, and so forth, further validate its effectiveness. Thus, HSMAOA shows strong competitiveness in complex optimization tasks and potential for a wide range of applications, and is an advantageous and promising alternative solution for optimization problems.

Dynamics of Dominance in Interacting Zebrafish

While two-body fighting behavior occurs throughout the animal kingdom to settle dominance disputes, important questions such as how the dynamics ultimately lead to a winner and loser are unresolved. Here we examine fighting behavior at high resolution in male zebrafish. We combine multiple cameras, a large volume containing a transparent interior cage to avoid reflection artifacts, with computer vision to track multiple body points across multiple organisms while maintaining individual identity in three dimensions. In the body point trajectories we find a spectrum of timescales which we use to build informative joint coordinates consisting of relative orientation and distance. We use the distribution of these coordinates to automatically identify fight epochs, and we demonstrate the postfight emergence of an abrupt asymmetry in relative orientations—a clear and quantitative signal of hierarchy formation. We identify short-time, multi-animal behaviors as clustered transitions between joint configurations, and show that fight epochs are spanned by a subset of these clusters, which we denote as maneuvers. The resulting space of maneuvers is rich but interpretable, including motifs such as “attacks” and “circling.” In the longer-time dynamics of maneuver frequencies we find differential and changing strategies, including that the eventual loser attacks more often towards the end of the contest. Our results suggest a reevaluation of relevant assessment models in zebrafish, while our approach is generally applicable to other animal systems. Published by the American Physical Society 2024

Regulatory impact of informality on gasoline consumption efficiency in Africa: A proposed two-part complementary hypothesis test

This study provides new evidence for the regulatory impact of informality on gasoline efficiency in Africa. I propose a Two-Part Complementary Hypothesis (hereafter referred to as the TPCH test), advocating a differential approach to promoting gasoline efficiency: (1) an inverted U-shaped relationship between informality and gasoline inefficiency, and (2) a U-shaped relationship between government regulation and informality, with a significant level effect. The findings indicate an inverted U-shaped effect of informality on gasoline efficiency and a level-negative effect of regulation on informality. These results suggest a differential strategy for enhancing gasoline efficiency. Government regulation is more effective in economies at the pre-saturation stage (characterized by normal growth levels of informality) but proves ineffective in economies at the post-saturation stage (characterized by abnormal growth levels of informality), where energy-saving behaviors may be self-motivated. This is corroborated by the inefficiency equation, where indicators of good governance, such as the rule of law, control of corruption, and regulatory quality, are statistically significant in advancing energy efficiency goals. Gasoline efficiency performance varies across countries, with the higher performers also being the continent's most economically advanced. However, these estimates risk downward bias if outliers or unobserved/observed heterogeneity are not considered.

FTDZOA: An Efficient and Robust FS Method with Multi-Strategy Assistance.

Feature selection (FS) is a pivotal technique in big data analytics, aimed at mitigating redundant information within datasets and optimizing computational resource utilization. This study introduces an enhanced zebra optimization algorithm (ZOA), termed FTDZOA, for superior feature dimensionality reduction. To address the challenges of ZOA, such as susceptibility to local optimal feature subsets, limited global search capabilities, and sluggish convergence when tackling FS problems, three strategies are integrated into the original ZOA to bolster its FS performance. Firstly, a fractional order search strategy is incorporated to preserve information from the preceding generations, thereby enhancing ZOA's exploitation capabilities. Secondly, a triple mean point guidance strategy is introduced, amalgamating information from the global optimal point, a random point, and the current point to effectively augment ZOA's exploration prowess. Lastly, the exploration capacity of ZOA is further elevated through the introduction of a differential strategy, which integrates information disparities among different individuals. Subsequently, the FTDZOA-based FS method was applied to solve 23 FS problems spanning low, medium, and high dimensions. A comparative analysis with nine advanced FS methods revealed that FTDZOA achieved higher classification accuracy on over 90% of the datasets and secured a winning rate exceeding 83% in terms of execution time. These findings confirm that FTDZOA is a reliable, high-performance, practical, and robust FS method.

Differential Experiences of Displacement in Transit through Mexico. The Ethnic Bridges among Garífuna and Honduran-Latino Groups

This article focuses on the differential experiences of migratory displacement between two ethnic groups from Honduras; the Afro-American Garifuna and Latinos. It seeks to compare the differential migration strategies of these two ethnically distinct displaced populations from the same country. Both move towards the same destination starting with transit through Mexico and then attempting to reach the United States. The manuscript is based on fieldwork conducted in migrant shelters within the irregular migratory route in Mexico, also, in New Orleans and Houston, two major destinations for the Garifuna population in the U.S. The paper consequently focuses on the case of the Garifuna, for whom it identifies the processes of territorial exclusion in which they are involved from the origin, nevertheless put the Garifuna population at an advantage over their non-Garifuna countrymen Latinos concerning the migratory process and their migration networks, specifically for having a better previous integration in the country of destination.

Do all complain the same? Examining the role of luxury hotels reviewer attributes using text mining

Purpose Although extant studies found the determinants of customer complaints in luxury hotels, comparison of complaint severity using reviewer characteristics (nationality, reviewer type and expertise) is limited. This study aims to identify the variation in customers’ complaint attributes among different customer categories. Design/methodology/approach This study tried to address this gap by using structural topic modelling and text mining techniques to find the sentiment score, word count, complaints attributes and severity score. The research sample covers luxury hotels in India, and online review text and their associated data has been collected and analysed. Findings The authors found that leisure customers wrote lengthier reviews and complained about the facility-related aspects (valet parking, entertainment and amenities) and food-related aspects (food variety, service and menu) while the business customers’ complaint severity score is more on salient aspects (room size, bathroom and staff related services). The international customers wrote lengthier reviews and complained more about the facility-related aspects (amenities, pick-up and entertainment) and core service offerings (stay experience, bathroom, dirtiness and complaint resolution). Whereas domestic customers complained more about stay-related aspects (food variety, quality, services, staff behaviour and stay experience). Also, this study used correlation analysis to find the dependencies between topics and uncovered underlying patterns, trends and associations, aiding in a deeper understanding and interpretation of the complaints. This study can help service providers tailor their services based on customer attributes to ensure maximum customer satisfaction. Originality/value The findings of this study can enable hotel service providers to customise their services based on customer attributes to reduce dissatisfaction among customers and promote customer loyalty. The findings of this study can also be used to devise differential promotional strategies for different types of customers.

Epidemiological Features of 4777 Cysts and Odontogenic Tumors Based on the 2022 WHO Classification.

The epidemiology of cysts and odontogenic tumors is important for differential diagnosis and treatment strategies. We aimed to describe the epidemiological features of cysts and odontogenic tumors in the Chilean population using the current WHO classification. We reviewed 22,914 biopsy requests received between January 1984 and September 2023 at the oral pathology department, School of Dentistry, Universidad Mayor, Santiago, Chile. Patients diagnosed with cysts of the jaws and odontogenic tumors were selected and information regarding age, sex, and location was recorded. 4226 (18.4%) were cysts, and 551 (2.4%) were odontogenic tumors, ranging from 2 to 97 years old. Males represented 54.4% and females 45.7% of the total sample. The most prevalent cysts were radicular cysts (58.6%), dentigerous cysts (17.9%), and odontogenic keratocysts (13.3%). The most prevalent odontogenic tumors were odontomas (40.1%) and conventional ameloblastoma (17.6%). Our study was the first retrospective analysis to determine the epidemiological features of both cysts and odontogenic tumors together, based on the 2022 WHO classification. This is relevant as it offers a potential basis for comprehensive comparisons of the epidemiological features of these entities, which could contribute to an accurate differential diagnosis, therefore, leading to more effective therapeutic interventions.

Optimization of Synchronous Control Parameters Based on Improved Sinusoidal Gray Wolf Algorithm

High precision control is often accompanied by many control parameters, which are interrelated and difficult to adjust directly. It is difficult to convert the system control effect directly into mathematical expression, so it is difficult to optimize it by intelligent algorithm. To solve this problem, we propose an improved sinusoidal gray wolf optimization algorithm (ISGWO). In this algorithm, a particle crossing processing mechanism based on the symmetry idea is introduced to maximize the retention of the position information of the optimal individual and improve the search accuracy of the algorithm. In addition, a differential cross-perturbation strategy is adopted to help the algorithm jump out of the local optimal solution in time, which enhances the development capability of ISGWO. Meanwhile, the position update formula with improved sinusoidal can better balance the development and exploration of ISGWO. The ISGWO algorithm is compared with three improved Gray Wolf algorithms on the CEC2017 test set as well as the synchronization controller. The experimental results show that the ISGWO algorithm has better selectivity, speed and robustness.

Assessing Differential Transfusion Requirements for Children with Congenital Malformations vs. Pediatric Acute Abdomen Emergencies.

This retrospective study aimed to evaluate the efficacy of preoperative blood transfusions in correcting anemia for pediatric patients with congenital malformations (CMs) versus those with acute abdomen (AA) conditions. The study hypothesized that the response to transfusions might vary significantly between these groups due to the differences in the underlying pathology and clinical status. The study included 107 pediatric patients admitted to Timisoara 'Louis Turcanu' Emergency Hospital for Children between January 2015 and May 2023, who required blood transfusions for preoperative anemia. Hemoglobin (HGB), hematocrit (HCT), and red blood cell counts (RBC) were assessed at admission, 48 h post-transfusion, and at discharge. Statistical analyses, including Student's t-test, Pearson correlation, and chi-square tests, were utilized to compare outcomes between the groups. The study population was divided into 53 children with CM and 54 with AA. Initial analyses showed that children with CM had statistically significantly higher baseline HGB (8.54 ± 1.00 g/dL vs. 7.87 ± 1.02 g/dL, p = 0.001) and HCT (26.07 ± 3.98% vs. 23.95 ± 2.90%, p = 0.002) compared to those with AA. Post-transfusion, children with CM exhibited a greater increase in HGB, with the highest increases noted in patients with central nervous system defects (mean increase of 3.67 g/dL, p = 0.038). In contrast, the increases in HGB for children with AA were less pronounced, with the highest being 2.03 g/dL in those with peritonitis (p = 0.078). No significant gender differences were noted in response to transfusion. Children with congenital malformations respond more effectively to preoperative blood transfusions compared to those with acute abdomen conditions. These findings suggest that differential transfusion strategies may be required based on the underlying medical condition to optimize the management of preoperative anemia in pediatric patients. Tailoring transfusion approaches according to specific patient needs and conditions could enhance clinical outcomes and resource utilization in pediatric surgical settings.

Enhancing VPPA welding quality prediction: A hybrid model integrating prior physical knowledge and CNN analysis

In response to the inconsistency between the features obtained by deep learning models and the quality features reflected by the physical laws of the welding process, this study proposes a solution by integrating a physical prior information model with a CNN model. Initially, the physical laws of the welding process are utilized to annotate the arc, weld pool, and weld seam features relevant to quality, which are then acquired through image processing algorithms, thereby converting the physical laws into a prior information model. Subsequently, this prior information model guides the CNN model for quality recognition, and the CNN model's attention to features is explained through visualization methods to elucidate the relationship between features and quality recognition. Experimental results demonstrate that under the guidance of the prior information model, the CNN model not only automatically focuses on features relevant to quality but also achieves a differential feature attention strategy, thereby improving the recognition accuracy of different outcomes. This research provides a new perspective for deep learning in the field of welding quality recognition.

Harmony in the brain: A narrative review on the shared neural substrates of emotion regulation and creativity

The contribution of creativity in overall well-being through regulating emotions has sparkled research interest in employing artistic interventions recently for the improvement of mental health. Although the behavioural links between emotion regulation and creativity have been established, the neural networks reflecting these relations are yet to be investigated. In this mini review, we describe the neural underpinnings of all forms of creativity and of the emotion regulation strategies. Given the complexity of both of these constructs, we separate creativity in its various forms and report the regions and the neural networks involved. Similarly, we distinguish between the differential emotion regulation strategies and describe the relevant brain areas and networks. We then proceed to a critical exploration of common regions of interest and of neural pathways among these important functions. The studies included in this review point towards certain brain regions that are shared among creativity and affective control, such as the prefrontal cortex, the anterior cingulate cortex, the dorsolateral prefrontal cortex, the medial temporal lobe and the inferior parietal lobule. The common neural networks of emotion regulation and creativity mainly focus on the default mode, the executive control and the salience networks. We then suggest a shared neural mechanism that may underlie emotion regulation and creativity, involving both control and affective processing. Drawing on the limitations of the studies reviewed, directions for future research are suggested that could significantly inform the field.

An adapted Black Widow Optimization Algorithm for Financial Portfolio Optimization Problem with cardinalty and budget constraints

Financial Portfolio Optimization Problem (FPOP) is a cornerstone in quantitative investing and financial engineering, focusing on optimizing assets allocation to balance risk and expected return, a concept evolving since Harry Markowitz’s 1952 Mean-Variance model. This paper introduces a novel meta-heuristic approach based on the Black Widow Algorithm for Portfolio Optimization (BWAPO) to solve the FPOP. The new method addresses three versions of the portfolio optimization problems: the unconstrained version, the equality cardinality-constrained version, and the inequality cardinality-constrained version. New features are introduced for the BWAPO to adapt better to the problem, including (1) mating attraction and (2) differential evolution mutation strategy. The proposed BWAPO is evaluated against other metaheuristic approaches used in portfolio optimization from literature, and its performance demonstrates its effectiveness through comparative studies on benchmark datasets using multiple performance metrics, particularly in the unconstrained Mean-Variance portfolio optimization version. Additionally, when encountering cardinality constraint, the proposed approach yields competitive results, especially noticeable with smaller datasets. This leads to a focused examination of the outcomes arising from equality versus inequality cardinality constraints, intending to determine which constraint type is more effective in producing portfolios with higher returns. The paper also presents a comprehensive mathematical model that integrates real-world constraints such as transaction costs, transaction lots, and a dollar-denominated budget, in addition to cardinality and bounding constraints. The model assesses both equality/inequality cardinality constraint versions of the problem, revealing that the inequality constraint tends to offer a wider range of feasible solutions with increased return potential.

Reevaluating the Most Favored-Nation Treatment: Addressing Challenges in Modern Trade

As global trade continues to evolve, the foundational principles that govern international commerce are increasingly scrutinized. The Most Favored-Nation (MFN) principle, a cornerstone of the World Trade Organization (WTO), was designed to foster fairness by mandating equal trade concessions for all members, thereby ensuring non-discriminatory trading behaviors. This research paper focused on the challenges of the Most Favored-Nation (MFN) principle within the World Trade Organization (WTO), a clause that mandates equal trade concessions for all members, ensuring non-discrimination. The research question investigates whether the MFN principle effectively promotes fairness in international trade or if it unintentionally exacerbates economic inequalities, particularly for developing countries. Findings reveal that MFN clauses often reduce market competition and lead to higher prices, while also disadvantaging developing countries that struggle to compete with developed nations. The paper underscores the need for MFN principle reform, such as advocating for differential pricing strategies for enhancing economic competition, setting exceptions for the poorest countries to implement tailored trade policies, and empowering these nations to challenge unfair practices by richer countries. These reforms aim to create a fairer and more balanced global trade system that supports inclusive economic growth for the whole world.