Journal articles: 'PSO'

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Quah, Titus, Derek Machalek, and Kody M. Powell. "Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process." Processes 8, no. 11 (November 19, 2020): 1497. http://dx.doi.org/10.3390/pr8111497.

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One popular method for optimizing systems, referred to as ANN-PSO, uses an artificial neural network (ANN) to approximate the system and an optimization method like particle swarm optimization (PSO) to select inputs. However, with reinforcement learning developments, it is important to compare ANN-PSO to newer algorithms, like Proximal Policy Optimization (PPO). To investigate ANN-PSO’s and PPO’s performance and applicability, we compare their methodologies, apply them on steady-state economic optimization of a chemical process, and compare their results to a conventional first principles modeling with nonlinear programming (FP-NLP). Our results show that ANN-PSO and PPO achieve profits nearly as high as FP-NLP, but PPO achieves slightly higher profits compared to ANN-PSO. We also find PPO has the fastest computational times, 10 and 10,000 times faster than FP-NLP and ANN-PSO, respectively. However, PPO requires more training data than ANN-PSO to converge to an optimal policy. This case study suggests PPO has better performance as it achieves higher profits and faster online computational times. ANN-PSO shows better applicability with its capability to train on historical operational data and higher training efficiency.

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Mills, Emily S., Kevin Mertz, Ethan Faye, Jennifer A. Bell, Andy T. Ton, Jeffrey C. Wang, Ram K. Alluri, and Raymond J. Hah. "Complication Rates and Utilization Trends of 3-Level Posterior Column Osteotomy Compared to Single-Level Pedicle Subtraction Osteotomy." Neurospine 20, no. 2 (June 30, 2023): 662–68. http://dx.doi.org/10.14245/ns.2346222.111.

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Objective: The objective of this study is to assess differences in complication profiles between 3-level posterior column osteotomy (PCO) and single-level pedicle subtraction osteotomy (PSO) as both are reported to provide similar degrees of sagittal correction.Methods: The PearlDiver database was queried retrospectively using International Classification of Disease, 9th and 10th edition and Current Procedural Terminology codes to identify patients who underwent PCO or PSO for degenerative spine disease. Patients under age 18 or with history of spinal malignancy, infection, or trauma were excluded. Patients were separated into 2 cohorts, 3-level PCO or single-level PSO, matched at a 1:1 ratio based on age, sex, Elixhauser comorbidity index, and number of fused posterior segments. Thirtyday systemic and procedure-related complications were compared.Results: Matching resulted in 631 patients for each cohort. PCO patients had decreased odds of respiratory (odds ratio [OR], 0.58; 95% confidence interval [CI], 0.43–0.82; p = 0.001) and renal complications (OR, 0.59; 95% CI, 0.40–0.88; p = 0.009) compared to PSO patients. There was no significant difference in cardiac complications, sepsis, pressure ulcer, dural tear, delirium, neurologic injuries, postoperative hematoma, postoperative anemia, or overall complications.Conclusion: Patients who undergo 3-level PCO have decreased respiratory and renal complications compared to single-level PSO. No differences were found in the other complications studied. Considering both procedures achieve similar sagittal correction, surgeons should be aware that 3-level PCO offers an improved safety profile compared to single-level PSO.

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Lourenço, Nuno, and Francisco Baptista Pereira. "PSO-CGO." International Journal of Natural Computing Research 2, no. 1 (January 2011): 1–20. http://dx.doi.org/10.4018/jncr.2011010101.

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In this paper the authors present PSO-CGO, a novel particle swarm algorithm for cluster geometry optimization. The proposed approach combines a steady-state strategy to update solutions with a structural distance measure that helps to maintain population diversity. Also, it adopts a novel rule to update particles, which applies velocity only to a subset of the variables and is therefore able to promote limited modifications in the structure of atomic clusters. Results are promising, as PSO-CGO is able to discover all putative global optima for short-ranged Morse clusters between 30 and 50 atoms. A comprehensive analysis is presented and reveals that the proposed components are essential to enhance the search effectiveness of the PSO.

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Tran, Hai Nguyen. "Differences between Chemical Reaction Kinetics and Adsorption Kinetics: Fundamentals and Discussion." Journal of Technical Education Science, no. 70B (June 28, 2022): 33–47. http://dx.doi.org/10.54644/jte.70b.2022.1154.

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Adsorption kinetics is an essential part in adsorption studies. The pseudo-first-order (PFO) and pseudo-second-order (PSO) models are frequently used to model the experimental dataset of time-dependent adsorption. The differential equations (based on reaction rate and rate law) of the PFO and PSO models are similar to those of chemical reactions (i.e., first and second order-kinetic reactions). The adsorption kinetics is illustrated through the plot of qt (the amount of adsorbate adsorbed by adsorbent at time t) vs. time. This plot includes two important regions (kinetic and equilibrium). The adsorption rate constant (k1(PFO) or k2(PSO), respectively) of the PFO or PSO models needs to be calculated from two regions. The appropriate selection of initial adsorbate concentrations for studying adsorption kinetics should be based on adsorption isotherm to ensure that adsorption sites in adsorbent (material) are highly (nearly fully) covered by adsorbate (solute). Only in this case, the rate constant of the adsorption is accurately obtained.

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Dynhora-Danheyda, Ramírez-Ochoa, Pérez-Domínguez Luis Asunción, and Martínez-Gómez Erwin Adán. "Comparison of PSO with the Hybrid Algorithms MOORA-PSO and DA-PSO for Decision Making." Advances in Modelling and Analysis B 66, no. 1-4 (October 17, 2023): 26–30. http://dx.doi.org/10.18280/ama_b.661-404.

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Gottlieb, Alice B., April W. Armstrong, Joseph F. Merola, Andrew Napoli, Miroslawa Nowak, Subhashis Banerjee, Thomas Lehman, and Philip J. Mease. "Impact of Oral, Selective, Allosteric Tyrosine Kinase 2 Inhibitor, Deucravacitinib, on Psoriasis in Patients with Active Psoriatic Arthritis: Results from a Phase 2 Trial." SKIN The Journal of Cutaneous Medicine 8, no. 2 (March 18, 2024): s383. http://dx.doi.org/10.25251/skin.8.supp.383.

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Introduction: Deucravacitinib (DEUC), an oral, selective, allosteric tyrosine kinase 2 inhibitor, is approved in the US, EU, and other countries for treatment of adults with moderate-to-severe plaque psoriasis (PsO) who are candidates for systemic therapy. DEUC was superior to apremilast and placebo (PBO) in two phase 3 trials in patients with moderate to severe PsO. DEUC was efficacious on multiple measures of arthritis severity vs PBO in a phase 2 trial in patients with active psoriatic arthritis (PsA) who had ≥1 PsO lesion (≥2 cm). In patients with body surface area (BSA) involvement ≥3% at baseline (80% of patients in this trial), a greater proportion achieved PASI 75 with DEUC (6 mg QD: 42.4%, P=0.01; 12 mg QD: 59.6%, P<0.0001) vs PBO (20.4%) at Week 16. This analysis evaluated the impact of DEUC on PsO in patients with PsA in the phase 2 trial (NCT03881059). Methods: The phase 2 double-blind PsA trial randomized patients (N=203) 1:1:1 to PBO, DEUC 6 mg once daily (QD), or DEUC 12 mg QD. After Week 16, patients could enroll in an optional, double-blind period until Week 52. DEUC-treated patients achieving minimal disease activity at Week 16 continued DEUC to Week 52. PsO disease activity measurements were assessed. Results: Baseline PsO characteristics were comparable across groups. At Week 16, significant decreases from baseline in mean PASI were observed with DEUC vs PBO in patients with baseline BSA ≥3%-<10% or PASI ≤12 even with very low baseline PASI scores, and also in those with baseline BSA ≥10% and PASI >12. Significant decreases in PASI from baseline were observed with DEUC vs PBO in patients with background csDMARD use (DEUC 6 mg, -4.0 and 12 mg, -4.9 vs PBO, -2.3; both P<0.05) and those without csDMARD use (-3.7 and -4.0 vs -2.5, respectively; both P<0.001). At Week 16, greater proportions of DEUC vs PBO patients achieved PASI ≤1 with baseline BSA ≥3% (DEUC 6 mg: 32.2%; DEUC 12 mg: 44.2%; PBO: 18.5%) and in patients with baseline BSA ≥10% and PASI >12 (23.1%; 28.6%; 0.0%). In patients with baseline BSA ≥3%, decreases in mean PASI at Week 16 were maintained through Week 52 in patients continuing DEUC 6 mg (absolute PASI score, Week 16: 2.39, Week 52: 1.22) and 12 mg (Week 16: 0.64, Week 52: 0.24). Conclusion: DEUC significantly improved PsO in patients with PsA, regardless of baseline PsO severity and background csDMARD use. Improvement was comparable to that observed in the phase 3 POETYK PSO-1 trial.

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Quezada Aguilar, Victor, José Carlos Quezada Quezada, Myriam Coronado Meneses, Josefina Rodríguez Torres, and Ernesto Flores García. "PSO PSO, Herramienta de Optimización en el Área Turística." Boletín Científico INVESTIGIUM de la Escuela Superior de Tizayuca 5, no. 9 (July 5, 2019): 1–5. http://dx.doi.org/10.29057/est.v5i9.3883.

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Las ciencias administrativas presentan problemáticas de carácter complejo donde se requiere de herramientas especificas que permitan obtener soluciones factibles garantizando la mejor o las mejores soluciones. La optimización es uno de los problemas con mayor frecuencia y que hasta hace unos años se habían resuelto con técnicas tradicionales, sin embargo este tipo de técnicas están enfocadas en optimizar un solo objetivo. El avance tecnológico ha permitido el desarrollo de numerosos métodos de optimización reduciendo tiempo de análisis, optimización de más de un objetivo, obtención de una gama de soluciones optimas, entre otras características. Durante el desarrollo del artículo se expone la importancia económica que tiene la localización correcta de un centro de distribución (CEDIS) en una región turística. La optimización de la función objetivo utilizada en la ubicación del CEDIS se realiza por el método PSO (Particle Swarm Optimization), método de optimización considerado de nueva generación.

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Fernández Martínez, J. L., and E. García Gonzalo. "The Generalized PSO: A New Door to PSO Evolution." Journal of Artificial Evolution and Applications 2008 (May 12, 2008): 1–15. http://dx.doi.org/10.1155/2008/861275.

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A generalized form of the particle swarm optimization (PSO) algorithm is presented. Generalized PSO (GPSO) is derived from a continuous version of PSO adopting a time step different than the unit. Generalized continuous particle swarm optimizations are compared in terms of attenuation and oscillation. The deterministic and stochastic stability regions and their respective asymptotic velocities of convergence are analyzed as a function of the time step and the GPSO parameters. The sampling distribution of the GPSO algorithm helps to study the effect of stochasticity on the stability of trajectories. The stability regions for the second-, third-, and fourth-order moments depend on inertia, local, and global accelerations and the time step and are inside of the deterministic stability region for the same time step. We prove that stability regions are the same under stagnation and with a moving center of attraction. Properties of the second-order moments variance and covariance serve to propose some promising parameter sets. High variance and temporal uncorrelation improve the exploration task while solving ill-posed inverse problems. Finally, a comparison is made between PSO and GPSO by means of numerical experiments using well-known benchmark functions with two types of ill-posedness commonly found in inverse problems: the Rosenbrock and the “elongated” DeJong functions (global minimum located in a very flat area), and the Griewank function (global minimum surrounded by multiple minima). Numerical simulations support the results provided by theoretical analysis. Based on these results, two variants of Generalized PSO algorithm are proposed, improving the convergence and the exploration task while solving real applications of inverse problems.

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Kumar, Sanjeev, and Amarjeet Prajapati. "PSO-MoSR: a PSO-based multi-objective software remodularisation." International Journal of Bio-Inspired Computation 15, no. 4 (2020): 254. http://dx.doi.org/10.1504/ijbic.2020.10030551.

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Prajapati, Amarjeet, and Sanjeev Kumar. "PSO-MoSR: a PSO-based multi-objective software remodularisation." International Journal of Bio-Inspired Computation 15, no. 4 (2020): 254. http://dx.doi.org/10.1504/ijbic.2020.108593.

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Bakic, Mirjana, Aleksandra Klisic, and Vesna Karanikolic. "Comparative Study of Hematological Parameters and Biomarkers of Immunity and Inflammation in Patients with Psoriasis and Atopic Dermatitis." Medicina 59, no. 9 (September 8, 2023): 1622. http://dx.doi.org/10.3390/medicina59091622.

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Background and Objectives: There are no studies regarding comparative analysis of hematological parameters in patients with psoriasis (PsO) and atopic dermatitis (AD), whereas studies examining serum biomarkers of immunity and inflammation in these entities are scarce and contradictory. We aimed to compare such parameters in patients with PsO and AD. Materials and Methods: Patients with PsO (n = 40) and AD (n = 40) were consecutively included in this cross-sectional study. Hematological parameters and biomarkers of immunity and inflammation (interferon-gamma (IFN-γ), interleukine (IL)-22 and C-reactive protein (CRP)) were determined. Results: While the mean corpuscular volume (MCV) was higher in the PsO group vs. the AD group (p < 0.05), there was no difference in the other examined parameters between groups. A higher neutrophil-to-lymphocyte ratio (NLR) was found in patients with AD > 50 years vs. patients with PsO of similar age (p < 0.05). Higher IL-22 levels were found in patients with AD < 50 years vs. patients with PsO of similar age (p < 0.05). Lower IL-22 levels were found in patients with AD > 50 years vs. patients with AD < 50 years (p < 0.05). Patients with PsO and with comorbidities had lower platelets (PLT), plateletcrit (PCT) and platelet-to-lymphocyte ratio (PLR), whereas lymphocytes, red cell distribution width-to-PLT ratio (RPR) and mean platelet volume/PLT ratio (MPR) were higher vs. PsO patients without comorbidities. Patients with AD and with comorbidities had lower PCT and PLR, whereas RPR was higher vs. AD patients without comorbidities. Conclusions: A higher pro-inflammatory state (i.e., higher NLR and IL-22) was found in AD vs. PsO in age-specific groups. A higher pro-inflammatory state (i.e., as reflected by platelet indexes) was found in both diseases with comorbidities.

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Gottlieb, A. B., A. Armstrong, J. F. Merola, A. Napoli, M. Nowak, S. Banerjee, T. Lehman, and P. J. Mease. "POS1550 IMPACT OF ORAL, SELECTIVE, ALLOSTERIC TYROSINE KINASE 2 (TYK2) INHIBITOR, DEUCRAVACITINIB, ON PSORIASIS IN PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS: RESULTS FROM A PHASE 2 TRIAL." Annals of the Rheumatic Diseases 82, Suppl 1 (May 30, 2023): 1145–46. http://dx.doi.org/10.1136/annrheumdis-2023-eular.1683.

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BackgroundTYK2 mediates signaling of key cytokines (eg, IL-23) involved in plaque psoriasis (PsO) and psoriatic arthritis (PsA) pathogenesis. Deucravacitinib (DEUC) is a first-in-class, oral, selective, allosteric TYK2 inhibitor approved in multiple countries for the treatment of adults with PsO. This was based on superiority of DEUC to apremilast and placebo (PBO) in a variety of PsO disease activity measures in 2 phase 3 trials in patients with moderate to severe PsO[1,2]. In addition, DEUC was efficacious on multiple measures of arthritis severity compared with PBO in a phase 2 trial in patients with active PsA who had ≥1 PsO lesion (≥2 cm)[3]. In patients with body surface area (BSA) ≥3% at baseline (mild to severe; 80% of patients in this trial), a greater proportion of patients achieved a ≥75% reduction in Psoriasis Area and Severity Index (PASI 75) with DEUC treatment (6 mg QD: 42.4%,P=0.01; 12 mg QD: 59.6%,P<0.0001) vs PBO (20.4%) at week (wk) 16.ObjectivesThis analysis further evaluated the impact of DEUC on PsO in patients with PsA in the phase 2 trial.MethodsThe phase 2 double-blind PsA trial randomized patients (N=203) 1:1:1 to PBO, DEUC 6 mg once daily (QD), or 12 mg QD. After wk 16 (Part A), patients could enroll in an optional, double-blind period until wk 52 (Part B). In Part B, patients receiving DEUC who achieved minimal disease activity (MDA) at wk 16 continued DEUC treatment to wk 52. Measurements of PsO disease activity, including mean BSA, mean PASI score, and achievement of PASI and BSA thresholds, were assessed.ResultsAt baseline (BL), PsO characteristics were generally comparable across treatment groups, with most patients (≥74%) having mild to moderate PsO (BSA <10% and PASI ≤12;Table 1). At wk 16, significant decreases from BL in mean PASI score were observed with DEUC treatment vs PBO both in patients with mild to moderate PsO as well as in those with moderate to severe PsO (BSA ≥10%; PASI >12) (Figure 1). These significant changes in PASI were observed in the mild to moderate PsO population even with very low BL PASI scores. Significant decreases in PASI from BL were observed with DEUC treatment vs PBO in both patients with background csDMARD use (DEUC 6 mg, -4.0 and 12 mg, -4.9 vs PBO, -2.3;P<0.05 for both) and those without csDMARD use (-3.7 and -4.0 vs -2.5, respectively;P<0.001, for both). At wk 16, a greater proportion of patients treated with DEUC vs PBO achieved PASI ≤1 in patients with mild to severe PsO (DEUC 6 mg, 32.2% and 12 mg, 44.2% vs PBO, 18.5%) and in patients with moderate to severe PsO (23.1% and 28.6% vs 0.0%, respectively). In mild to severe PsO, decreases in mean PASI score at wk 16 were maintained through wk 52 in patients who continued treatment with DEUC 6 mg (absolute PASI score, wk 16: 2.39, wk 52: 1.22) and 12 mg (wk 16: 0.64, wk 52: 0.24).ConclusionTreatment with DEUC significantly improved PsO in patients with PsA, regardless of BL PsO severity and background csDMARD use. Of note, improvement in the subgroup of patients with moderate to severe PsO in this trial was comparable to that observed in the phase 3 POETYK PSO-1 trial in patients with moderate to severe PsO[1].References[1]Armstrong A, et al.J Am Acad Dermatol. 2023;88(1):29-39.[2]Strober B, et al.J Am Acad Dermatol. 2023;88(1):40-51.[3]Mease PJ, et al.Ann Rheum Dis.2022;81:815-822.Table 1.Baseline psoriasis characteristicsPlacebo n=66Deucravacitinib 6 mg QD n=70Deucravacitinib 12 mg QD n=67BSA SeverityBSA <3%, n (%)9 (14)11 (16)14 (21)BSA ≥3%–<10%, n (%)32 (49)37 (53)29 (43)BSA ≥10%, n (%)22 (33)22 (31)23 (34)PASI SeverityPASI ≤5, n (%)30 (46)33 (47)29 (43)PASI >5–≤12, n (%)18 (27)23 (33)30 (45)PASI >12, n (%)15 (23)14 (20)7 (10)NR, n (%)3 (5)01 (2)BSA ≥3%(mild to severe)n (%)54 (82)59 (84)52 (78)BSA <10%, PASI ≤12(mild to moderate)n (%)49 (74)57 (81)59 (88)BSA ≥10%, PASI >12(moderate to severe)n (%)14 (21)13 (19)7 (10)BSA, body surface area; NR, not reported; PASI, Psoriasis Area and Severity Index; QD, once daily; SD, standard deviatiAcknowledgementsThis study was sponsored by Bristol Myers Squibb.Disclosure of InterestsAlice B Gottlieb Consultant of: AnaptysBio, Amgen, Avotres, Boehringer Ingelheim, Bristol Myers Squibb, Dermavant, Eli Lilly, Janssen, Novartis, Pfizer, Sanofi, Sun Pharma, and Xbiotech (stock options for an RA project), Grant/research support from: AnaptysBio, Janssen, Novartis, Ortho, Sun Pharma, and UCB, April Armstrong Consultant of: AbbVie, Bristol Myers Squibb, Eli Lilly, Janssen, Leo Pharma, and Novartis, Boehringer Ingelheim/Parexel, Celgene, Dermavant, Genentech, GlaxoSmithKline, Menlo Therapeutics, Merck, Modernizing Medicine, Ortho Dermatologics, Pfizer, Regeneron, Sanofi Genzyme, Science 37, Sun Pharma, and Valeant, Grant/research support from: AbbVie, Bristol Myers Squibb, Eli Lilly, Janssen, Leo Pharma, Dermira, Kyowa Hakko Kirin, and UCB, Joseph F. Merola Consultant of: AbbVie, Amgen, Biogen, Bristol Myers Squibb, Dermavant, Eli Lilly, Janssen, Leo Pharma, Novartis, Pfizer, Regeneron, Sanofi, Sun Pharma, and UCB, Andrew Napoli Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Miroslawa Nowak Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Subhashis Banerjee Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Thomas Lehman Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Philip J Mease Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Janssen, Novartis, Pfizer, SUN Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Janssen, Novartis, Pfizer, Sun Pharma, and UCB.

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Ali, Omer, Qamar Abbas, Khalid Mahmood, Ernesto Bautista Thompson, Jon Arambarri, and Imran Ashraf. "Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems." Mathematics 11, no. 21 (October 24, 2023): 4406. http://dx.doi.org/10.3390/math11214406.

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Particle swarm optimization (PSO) is a population-based heuristic algorithm that is widely used for optimization problems. Phasor PSO (PPSO), an extension of PSO, uses the phase angle θ to create a more balanced PSO due to its increased ability to adjust the environment without parameters like the inertia weight w. The PPSO algorithm performs well for small-sized populations but needs improvements for large populations in the case of rapidly growing complex problems and dimensions. This study introduces a competitive coevolution process to enhance the capability of PPSO for global optimization problems. Competitive coevolution disintegrates the problem into multiple sub-problems, and these sub-swarms coevolve for a better solution. The best solution is selected and replaced with the current sub-swarm for the next competition. This process increases population diversity, reduces premature convergence, and increases the memory efficiency of PPSO. Simulation results using PPSO, fuzzy-dominance-based many-objective particle swarm optimization (FMPSO), and improved competitive multi-swarm PPSO (ICPPSO) are generated to assess the convergence power of the proposed algorithm. The experimental results show that ICPPSO achieves a dominating performance. The ICPPSO results for the average fitness show average improvements of 15%, 20%, 30%, and 35% over PPSO and FMPSO. The Wilcoxon statistical significance test also confirms a significant difference in the performance of the ICPPSO, PPSO, and FMPSO algorithms at a 0.05 significance level.

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GULGENOVA, AYUNA, and MIKHAIL POTAPOV. "Two new species of Onychiuridae (Collembola) from East Siberia (Russia)." Zootaxa 5336, no. 1 (August 21, 2023): 125–34. http://dx.doi.org/10.11646/zootaxa.5336.1.6.

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Two new species of Onychiuridae (Collembola) from Buryatia (Russia, East Siberia), Supraphorura chernovae sp. nov. and Thalassaphorura transbaikalica sp. nov., are herein described and illustrated. Supraphorura chernovae sp. nov. differs from its closest species, S. furcifera (Börner, 1901), by its dorsal pso formula as 32/022/23232, presence of 1 pso on the subcoxae 1 of all legs, and a larger set of vesicles on the PAO. Thalassaphorura transbaikalica sp. nov. is most similar to T. tibiotarsalis Sun, Chen & Deharveng, 2010 by the same dorsal pso formula (32/233/33343) and presence of 7 chaetae on the distal whorl of all tibiotarsi, but differs from the latter species specially by the presence of 1 pso on the subcoxa 1 of leg 1 (T. tibiotarsalis has 2 pso), number of vesicles on the PAO (13–14 vs. 17–18), number of chaetae on the subcoxa 1 of legs 1–3 (4, 4, 4 vs. 3, 3, 3), and ratio of the empodial appendage and claw (0.8–0.9 vs. 0.5). After our study there are now 47 recorded species of Onychiuridae recorded from East Siberia.

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Sotelo-Figueroa, Marco Aurelio, Héctor José Puga Soberanes, Juan Martín Carpio, Héctor J. Fraire Huacuja, Laura Cruz Reyes, and Jorge Alberto Soria-Alcaraz. "Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/545191.

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In recent years Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP) which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI). Particle Swarm Optimisation (PSO) is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO) is a recent and novel algorithm which is also part of SI. PESO uses two perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP); it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO.

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Kapasi, O. A., L. K. Ruby, and K. Calney. "The pso-riatichand." Plastic and Reconstructive Surgery 75, no. 4 (April 1985): 614. http://dx.doi.org/10.1097/00006534-198504000-00067.

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Wu, J., C. Pelletier, B. Ung, Q. Ni, and M. Tian. "Treatment Patterns PSO." Value in Health 21 (May 2018): S243—S244. http://dx.doi.org/10.1016/j.jval.2018.04.1645.

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Zhang, Wei, Jiao Liu, Lv-bin Fan, Ya-hong Liu, and Di Ma. "Control strategy PSO." Applied Soft Computing 38 (January 2016): 75–86. http://dx.doi.org/10.1016/j.asoc.2015.09.030.

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NISBET, A. J., A. MacKELLAR, K. McLEAN, G. P. BRENNAN, and J. F. HUNTLEY. "Eukaryotic expression of recombinant Pso o 1, an allergen from Psoroptes ovis, and its localization in the mite." Parasitology 134, no. 1 (September 18, 2006): 83–89. http://dx.doi.org/10.1017/s0031182006001235.

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A cDNA encoding the immunogen Pso o 1 from Psoroptes ovis was obtained by polymerase chain reaction (PCR) amplification. The amplicon contained the entire coding sequence for the prepro-enzyme in an open reading frame (ORF) of 966 bp. This gene encoded a predicted protein of 322 amino acids (aa) with 64% aa identity (80% similarity) to the major house dust mite faecal allergen Der f 1. The pro-enzyme form of Pso o 1 was expressed as a recombinant protein in the Pichia pastoris-eukaryotic expression system. Maturation of the recombinant pro-enzyme by autocatalytic activation was not observed, and such maturation could not be achieved using a number of techniques known to activate recombinant Der p 1 and Der f 1 expressed in the same system. Serum raised against recombinant Pso o 1 cross-reacted with mature Der p 1 and allowed Pso o 1 to be immunolocalized to the gut of P. ovis.

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Tapia-Fonllem, César, Blanca Fraijo-Sing, Víctor Corral-Verdugo, and Anais Ortiz Valdez. "Education for Sustainable Development in Higher Education Institutions." SAGE Open 7, no. 1 (January 2017): 215824401667629. http://dx.doi.org/10.1177/2158244016676295.

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The role that higher education plays in the promotion of sustainable development outstands in the declarations on Education for Sustainable Development (ESD), besides being a research priority in higher education. However, few studies exist that evaluate sustainable lifestyles among university students. The aim of this study was to analyze the mission and vision, processes and actions undertaken to promote sustainability in higher education institutions, and to compare the pro-sustainability orientation (PSO) reported by 360 students coursing first or last semesters at college. The study was intended to evaluate the influence that four higher education institutions in Sonora, Mexico, have on students’ PSO. Results of the study indicate that a coherent PSO factor emerges from the interrelations among pro-environmental dispositional and behavioral variables reported by students. However, university programs and actions do not produce statistically significant differences between freshmen and senior students. Possible reasons explaining the lack of positive influence of those universities on students’ PSO are discussed.

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BinHannan, Nabil, Md Abdul Mottalib, Shaikh Jeeshan Kabeer, and Arif Muhammad Sultan. "MFS-PSO: A Modified PSO Method for Optimizing Gene Selection." International Journal of Computer Applications 67, no. 1 (April 18, 2013): 38–42. http://dx.doi.org/10.5120/11363-6595.

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Agarwal, Shikha, Akshay Dhyani, and Prabhat Ranjan. "Newton’s second law based PSO for feature selection: Newtonian PSO." Journal of Intelligent & Fuzzy Systems 37, no. 4 (October 25, 2019): 4923–35. http://dx.doi.org/10.3233/jifs-181177.

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Tchie, Andrew E. Yaw. "African-Led Peace Support Operations in a declining period of new UN Peacekeeping Operations." Global Governance: A Review of Multilateralism and International Organizations 29, no. 2 (June 8, 2023): 230–44. http://dx.doi.org/10.1163/19426720-02902006.

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Abstract The United Nations is known to be the preeminent body to authorize and oversee international peacekeeping missions; however, new forms of African-led Peace Support Operations (PSO s) are increasingly common, innovative, and context-specific. This paper examines the evolution of African-led PSO s and argues that African-led PSO s are filling a vacuum and taking on responsibilities once assumed by the United Nations Peacekeeping Operations (UN PKO). The paper posits that the rise of African-led PSO s is due to the growing need for security and respond to the changing nature of conflict, the spread of insecurity and terrorism, and cross-border violence. Finally, the paper explores the implications of these operations for future missions in Africa, arguing that the future of African-led PSO s may be the preferred choice, with Regional Economic Communities and Ad-hoc Security Initiatives leading the way.

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Novikova, Tatyana, Svetlana Evdokimova, and Roman Medvedev. "Computer-aided design of the location of wireless cellular base stations." Modeling of systems and processes 16, no. 4 (December 18, 2023): 61–70. http://dx.doi.org/10.12737/2219-0767-2023-16-4-61-70.

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The planning of wireless base stations is a multi-purpose task of optimizing combinations, and the main goals of optimization include cost reduction, increased coverage and quality preservation. The paper pays attention to the consideration of the coverage radius, the quality of signal transmission and two types of signal interference. To implement the tasks set for the location of the base station, the following algorithms were used: PMET-PSO and PPSO-GA, which became the basis for the module for solving problems of designing the location of base stations in the proposed automated design system. Using the C/S architecture and 3D CAD based on ACIS and Google Earth, based on an in-depth analysis of the actual requirements for the base station, a CAD for the location of wireless cellular base stations is proposed. The main technical modules of the system are the technology of rapid modeling involving objects in the planning of base stations, the technology of multi-purpose modeling and solving problems of planning base stations and the technology of distributed joint integration of program modules. The technology for solving planning problems uses the PMET-PSO and PPSO-GA algorithms. Both algorithms compensate for the disadvantages of their original algorithms (MET-PSO and PSO-GA) and retain their respective advantages: time - PMET-PSO, and if the user does not care about time and wants to get the optimal possible solution, then the PPSO-GA algorithm. Both algorithms combine memory computing with good parallelism, which plays a crucial role in solving multi-purpose optimization problems.

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Meng, Zhenyu, Yuxin Zhong, Guojun Mao, and Yan Liang. "PSO-sono: A novel PSO variant for single-objective numerical optimization." Information Sciences 586 (March 2022): 176–91. http://dx.doi.org/10.1016/j.ins.2021.11.076.

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Dohare, Indu, and Karan Singh. "PSO-DEC: PSO based deterministic energy efficient clustering protocol for IoT." Journal of Discrete Mathematical Sciences and Cryptography 22, no. 8 (November 17, 2019): 1463–75. http://dx.doi.org/10.1080/09720529.2019.1695898.

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Klisic, Aleksandra, Mirjana Bakic, and Vesna Karanikolic. "Comparative Analysis of Redox Homeostasis Biomarkers in Patients with Psoriasis and Atopic Dermatitis." Antioxidants 12, no. 10 (October 18, 2023): 1875. http://dx.doi.org/10.3390/antiox12101875.

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There are no studies regarding comparative analysis of serum biomarkers of oxidative stress in patients with psoriasis (PsO) and atopic dermatitis (AD). We aimed to compare the serum redox homeostasis parameters in patients with PsO vs. AD in an attempt to find the sensitive and specific oxidative stress biomarker that could best reflect the existence of one of these disease entities. Methods: Forty patients with PsO and forty patients with AD were consecutively included in this cross-sectional study. Parameters of redox homeostasis, i.e., pro-oxidants [malondialdehyde (MDA) and advanced oxidation protein products (AOPP)] and antioxidants [catalase (CAT) and superoxide dismutase (SOD)] were determined. Results: There was no difference in oxidative stress biomarkers between the PsO and AD group, except for higher CAT activity in the AD group (p < 0.001). Among all examined redox homeostasis biomarkers, ROC analysis showed that only CAT exhibited good diagnostic accuracy (AUC = 0.719) in the discrimination of patients with PsO vs. AD, with 0.436 U/L as the cut-off value of CAT activity. Conclusions: The CAT exhibited good diagnostic accuracy in the discrimination of patients with AD from those with PsO. The obtained results could suggest the importance of the use of antioxidants as a potential therapeutic strategy in the treatment of these two skin inflammatory diseases.

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Li, Zhaobo, Yimin Deng, and Shuanglei Sun. "Adaptive Cruise Predictive Control Based on Variable Compass Operator Pigeon-Inspired Optimization." Electronics 11, no. 9 (April 26, 2022): 1377. http://dx.doi.org/10.3390/electronics11091377.

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A vehicle adaptive cruise system can control the speed and the safe distance between vehicles rapidly and effectively, which is an integral part of an intelligent driver assistance system. Adaptive cruise predictive control algorithms based on variable compass operator pigeon-inspired optimization (PIO) and PSO are proposed to improve the time response characteristics of multi-objective adaptive cruise system predictive control. Firstly, a longitudinal kinematic model of an adaptive cruise system was established and linearly discretized. Secondly, the multi-objective optimal cost function and parameter constraints were designed by integrating factors such as distance error, relative speed, acceleration and impact, and a mathematical model of the adaptive cruise predictive control optimization problem was constructed. Finally, PIO and PSO were used to solve the optimal control law for MPC and simulated by Matlab. The results show that the adaptive cruise system can reach a steady state quickly with the control laws of PIO or PSO. However, due to the global optimization and fast convergence characteristic, variable compass operator PIO has better time response characteristics.

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Li, Zhaobo, Yimin Deng, and Shuanglei Sun. "Adaptive Cruise Predictive Control Based on Variable Compass Operator Pigeon-Inspired Optimization." Electronics 11, no. 9 (April 26, 2022): 1377. http://dx.doi.org/10.3390/electronics11091377.

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A vehicle adaptive cruise system can control the speed and the safe distance between vehicles rapidly and effectively, which is an integral part of an intelligent driver assistance system. Adaptive cruise predictive control algorithms based on variable compass operator pigeon-inspired optimization (PIO) and PSO are proposed to improve the time response characteristics of multi-objective adaptive cruise system predictive control. Firstly, a longitudinal kinematic model of an adaptive cruise system was established and linearly discretized. Secondly, the multi-objective optimal cost function and parameter constraints were designed by integrating factors such as distance error, relative speed, acceleration and impact, and a mathematical model of the adaptive cruise predictive control optimization problem was constructed. Finally, PIO and PSO were used to solve the optimal control law for MPC and simulated by Matlab. The results show that the adaptive cruise system can reach a steady state quickly with the control laws of PIO or PSO. However, due to the global optimization and fast convergence characteristic, variable compass operator PIO has better time response characteristics.

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Ali, Zain, Bharat Lal Harijan, Tayab Din Memon, Nazmus Naf, and Ubed-u.-Rahman Memon. "Digital FIR Filter Design by PSO and its variants Attractive and Repulsive PSO(ARPSO) & Craziness based PSO(CRPSO)." International Journal of Recent Technology and Engineering 9, no. 6 (March 30, 2021): 136–41. http://dx.doi.org/10.35940/ijrte.f5515.039621.

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Digital filters play a major role in signal processing that are employed in many applications such as in control systems, audio or video processing systems, noise reduction applications and different systems for communication. In this regard, FIR filters are employed because of frequency stability and linearity in their phase response. FIR filter design requires multi-modal optimization problems. Therefore, PSO (Particle Swarm Optimization) algorithm and its variants are more adaptable techniques based upon particles’ population in the search space and a great option for designing FIR filter. PSO and its different variants improve the solution characteristic by providing a unique approach for updating the velocity and position of the swarm. An optimized set of filter coefficient is produced by PSO and its variant algorithms which gives the optimized results in passband and stopband. In this research paper, Digital FIR filter is effectively designed by using PSO Algorithm and its two variants ARPSO and CRPSO in MATLAB. The outcomes prove that the filter design technique using CRPSO is better than filter design by PM algorithm. PSO and ARPSO Algorithms in the context of frequency spectrum and RMS error.

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Ab. Aziz, Nor Azlina, Nor Hidayati Abd Aziz, Tasiransurini Ab Rahman, Norrima Mokhtar, and Marizan Mubin. "Random Synchronous Asynchronous PSO – A Particle Swarm Optimization Algorithm with a New Iteration Strategy." Mekatronika 1, no. 2 (July 15, 2019): 81–92. http://dx.doi.org/10.15282/mekatronika.v1i2.4989.

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Particle swarm optimisation (PSO) is a population-based stochastic optimisation algorithm. Traditionally the particles update sequence for PSO can be categorized into two groups, synchronous (S-PSO) or asynchronous (A-PSO) update. In S-PSO, the particles’ performances are evaluated before their velocity and position are updated, while in A-PSO, each particle’s velocity and position is updated immediately after individual performance is evaluated. In another study, a random asynchronous PSO (RA-PSO) has been proposed. In RA-PSO, particles are randomly chosen to be updated asynchronously, the randomness improves swarm’s exploration. RA-PSO belongs to the asynchronous group. In this paper, a new category; hybrid update sequence is proposed. The new update sequence exploits the advantages of synchronous, asynchronous, and random update methods. The proposed sequence is termed as, random synchronous-asynchronous PSO (RSA-PSO). RSA-PSO divides the particles into groups. The groups are subjected to random asynchronous update, while the particles within a chosen group are updated synchronously. The performance of RSA-PSO is compared with the existing S-PSO, A-PSO, and RA-PSO using CEC2014’s benchmark functions. The results show that RSA-PSO is superior to both A-PSO and RA-PSO, and as good as S-PSO

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Nazarpour, Mohammad, navid nezafati, and Sajjad Shokouhyar. "Detection of Attacks and Anomalies in the Internet of Things System using Neural Networks Based on Training with PSO Algorithms, Fuzzy PSO, Comparative PSO and Mutative PSO." Journal of Information Systems and Telecommunication (JIST) 10, no. 40 (October 19, 2022): 270–78. http://dx.doi.org/10.52547/jist.16307.10.40.270.

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Balasaraswathi M and Kalpana B. "Fast and Effective Classification using Parallel and Multi-start PSO." Journal of Information Technology Research 11, no. 2 (April 2018): 13–30. http://dx.doi.org/10.4018/jitr.2018040102.

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PSO being a swarm based algorithm, can efficiently lend itself to operate on huge data. This article presents a technique that performs classification using PSO. An initial discussion is carried out describing PSO as a classifier. Three variants of PSO are proposed here; the first variant hybridizes PSO using Simulated Annealing and the next two variants parallelizes PSO. The two parallel variants of PSO are; Parallel PSO and Multistart PSO. Parallel PSO operates by parallelizing the operation of each of the particles and Multistart PSO runs several normal versions of PSO embedded with Simulated Annealing in parallel. The multi-start version is implemented to eliminate the problem of local optima. Experiments were conducted to identify the scalability and efficiency of PSO and its variants on huge and imbalanced data.

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Narang, Anmol, Neelam R. Prakash, and Amit Arora. "Text Summarization using PSO." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 6 (June 30, 2017): 528–31. http://dx.doi.org/10.23956/ijarcsse/v7i6/0242.

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YANG, Hui-xian, Zi-wen LIU, Jun WANG, Xu-si WANG, and Peng-he XIE. "Modified PSO hybrid algorithm." Journal of Computer Applications 30, no. 6 (June 23, 2010): 1516–18. http://dx.doi.org/10.3724/sp.j.1087.2010.01516.

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GRIMALDI, PAUL L. "PSO Requirements Taking Shape." Nursing Management (Springhouse) 29, no. 2 (February 1998): 14???17. http://dx.doi.org/10.1097/00006247-199802000-00002.

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Bratton, Dan, and Tim Blackwell. "A Simplified Recombinant PSO." Journal of Artificial Evolution and Applications 2008 (February 19, 2008): 1–10. http://dx.doi.org/10.1155/2008/654184.

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Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding how a particle swarm optimization (PSO) swarm functions. One of these forms, PSO with discrete recombination, is extended and analyzed, demonstrating not just improvements in performance relative to a standard PSO algorithm, but also significantly different behavior, namely, a reduction in bursting patterns due to the removal of stochastic components from the update equations.

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Tempola, Firman. "Implemented PSO-NBC and PSO-SVM to Help Determine Status of Volcanoes." Jurnal Penelitian Pos dan Informatika 9, no. 2 (December 30, 2019): 97. http://dx.doi.org/10.17933/jppi.2019.090202.

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<p class="JGI-AbstractIsi">This research is a continuation of previous research that applied the Naive Bayes classifier algorithm to predict the status of volcanoes in Indonesia based on seismic factors. There are five attributes used in predicting the status of volcanoes, namely the status of the normal, standby and alerts. The results Showed the accuracy of the resulted prediction was only 79.31%, or fell into fair classification. To overcome these weaknesses and in order to increase accuracy, optimization is done by giving criteria or attribute weights using particle swarm optimization. This research compared the optimization of Naive Bayes algorithm to vector machine support using particle swarm optimization. The research found improvement on system after application of PSO-NBC to that of 91.3 % and 92.86% after applying PSO-SVM.</p>

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Tempola, Firman. "Implemented PSO-NBC and PSO-SVM to Help Determine Status of Volcanoes." Jurnal Penelitian Pos dan Informatika 9, no. 2 (December 30, 2019): 97–103. http://dx.doi.org/10.17933/jppi.v9i2.198.

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This research is a continuation of previous research that applied the Naive Bayes classifier algorithm to predict the status of volcanoes in Indonesia based on seismic factors. There are five attributes used in predicting the status of volcanoes, namely the status of the normal, standby and alerts. The results Showed the accuracy of the resulted prediction was only 79.31%, or fell into fair classification. To overcome these weaknesses and in order to increase accuracy, optimization is done by giving criteria or attribute weights using particle swarm optimization. This research compared the optimization of Naive Bayes algorithm to vector machine support using particle swarm optimization. The research found improvement on system after application of PSO-NBC to that of 91.3 % and 92.86% after applying PSO-SVM.

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Dharmawan, Weiskhy Steven. "KOMPARASI ALGORITMA KLASIFIKASI SVM-PSO DAN C4.5-PSO DALAM PREDIKSI PENYAKIT JANTUNG." I N F O R M A T I K A 13, no. 2 (January 11, 2022): 31. http://dx.doi.org/10.36723/juri.v13i2.301.

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Rameshkumar, K. "Extension of PSO and ACO-PSO algorithms for solving Quadratic Assignment Problems." IOP Conference Series: Materials Science and Engineering 377 (June 2018): 012192. http://dx.doi.org/10.1088/1757-899x/377/1/012192.

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Jamali, Shahram, and Gholam Shaker. "PSO-SFDD: Defense against SYN flooding DoS attacks by employing PSO algorithm." Computers & Mathematics with Applications 63, no. 1 (January 2012): 214–21. http://dx.doi.org/10.1016/j.camwa.2011.11.012.

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Wu, Jui-Yu. "Solving Constrained Global Optimization Problems by Using Hybrid Evolutionary Computing and Artificial Life Approaches." Mathematical Problems in Engineering 2012 (2012): 1–36. http://dx.doi.org/10.1155/2012/841410.

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This work presents a hybrid real-coded genetic algorithm with a particle swarm optimization (RGA-PSO) algorithm and a hybrid artificial immune algorithm with a PSO (AIA-PSO) algorithm for solving 13 constrained global optimization (CGO) problems, including six nonlinear programming and seven generalized polynomial programming optimization problems. External RGA and AIA approaches are used to optimize the constriction coefficient, cognitive parameter, social parameter, penalty parameter, and mutation probability of an internal PSO algorithm. CGO problems are then solved using the internal PSO algorithm. The performances of the proposed RGA-PSO and AIA-PSO algorithms are evaluated using 13 CGO problems. Moreover, numerical results obtained using the proposed RGA-PSO and AIA-PSO algorithms are compared with those obtained using published individual GA and AIA approaches. Experimental results indicate that the proposed RGA-PSO and AIA-PSO algorithms converge to a global optimum solution to a CGO problem. Furthermore, the optimum parameter settings of the internal PSO algorithm can be obtained using the external RGA and AIA approaches. Also, the proposed RGA-PSO and AIA-PSO algorithms outperform some published individual GA and AIA approaches. Therefore, the proposed RGA-PSO and AIA-PSO algorithms are highly promising stochastic global optimization methods for solving CGO problems.

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Wu, Jui-Yu. "Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches." Mathematical Problems in Engineering 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/256180.

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Stochastic global optimization (SGO) algorithms such as the particle swarm optimization (PSO) approach have become popular for solving unconstrained global optimization (UGO) problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient. These parameters are tuned by using trial and error. To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO) method and an artificial immune algorithm-based PSO (AIA-PSO) method. The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems. Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms. Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems.

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Kien, Chong Woon, and Neoh Siew Chin. "Improved Particle Swarm Optimization (PSO) for Performance Optimization of Electronic Filter Circuit Designs." Applied Mechanics and Materials 229-231 (November 2012): 1643–50. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1643.

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This article discusses and analyzes particle swarm optimization (PSO) approach in the design and performance optimization of a 4th-order Sallen Key high pass filter. Three types of particle swarm features are studied: basic PSO, PSO with regrouped particles (PSO-RP) and PSO with diversity embedded regrouped particles (PSO-DRP). PSO-RP and PSO-DRP are proposed to solve the stagnation problem of basic PSO. Based on the developed PSO approaches, LTspice is employed as the circuit simulator for the performance investigation of the designed filter. In this paper, 12 design parameters of the Sallen Key high pass filter are optimized to satisfy the required constraints and specifications on gain, cut-off frequency, and pass band ripples. Overall results show that PSO with diversity embedded regrouped particles improve the conventional search of basic PSO and has managed to achieve the design objectives.

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"PSO AND POLICY STUDIES DEVELOPMENTS: THE PSO MEMBERSHIPSURVEY." Review of Policy Research 6, no. 1 (August 1986): i—xvi. http://dx.doi.org/10.1111/j.1541-1338.1986.tb00639.x.

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Dasgupta, Hirak, and Sanjay Krishnapratap Pawar. "Impact of higher education imparted by Indian universities on the pro-sustainability orientation of students." Journal of Applied Research in Higher Education ahead-of-print, ahead-of-print (October 29, 2020). http://dx.doi.org/10.1108/jarhe-12-2019-0318.

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PurposeThe purpose of this study is to test a model on pro-sustainability orientation (PSO) among the students and comparison of the levels of PSO among the first-year and the second-year MBA/PGDM students studying in Indian higher education institutions.Design/methodology/approachThe study comprised of quantitative analysis of PSO through a questionnaire that was distributed among MBA/PGDM students of four prominent higher education institutions in the state of Maharashtra. A total of 425 students responded to the survey. The items of this instrument included actions such as reuse, recycling, energy, conservation and so on. Likert scale was used to measure the dimensions and structural equation modelling was applied to test the PSO model.FindingsThe study revealed significant difference in the PSO among male and female students with respect to equity dimension and significant differences among the first- and second-year MBA students with respect to pro-environmental deliberations. A significant difference was found among the four institutions with respect to the pro-environmental deliberations, altruistic behaviour and affinity towards diversity. The study showed positive influence of PSO over SB. The study revealed that the students enrolled in MBA/PGDM showed positive attitude towards SD and the environment.Practical implicationsThere has been a dearth of studies conducted on PSO of students enrolled in Indian higher education. This study is an attempt to show the impact of the Indian higher education on the pro-sustainable lifestyles among the students of MBA/PGDM that can provide valuable insights for the millennials of the emerging economies.Originality/valueThis study is an attempt to depict the role played by the Indian higher education institutions in the development of PSO among the students of MBA/PGDM. In order to promote sustainable development within one’s society, one needs to educate future leaders about the importance of sustainability.

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Ruscitti, Piero, Maria Esposito, Ilenia Di Cola, Cristina Pellegrini, Andrea De Berardinis, Mirco Mastrangelo, Camilla Gianneramo, Antonio Barile, Maria Concetta Fargnoli, and Paola Cipriani. "Cytokine profile characterization of naïve patients with psoriasis and psoriatic arthritis: implications for a pathogenic disease continuum." Frontiers in Immunology 14 (July 13, 2023). http://dx.doi.org/10.3389/fimmu.2023.1229516.

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BackgroundThe idea of psoriatic disease continuum has been progressively prompted based on the advances of the knowledge about the pathogenic steps underpinning the occurrence of psoriasis (PSO) and psoriatic arthritis (PSA). To evaluate biomolecules (inflammatory cytokines, inflammatory chemokines, cell adhesion and cellular mediators) in naïve patients with PSO, PSA with PSO, and PSA sine PSO. To stratify the results considering the presence of psoriatic nail involvement, extensive skin disease and obesity evaluating all involved patients.MethodsBy multiplex technology, 20 serum biomolecules were assessed with the inclusion of pro-inflammatory cytokines (GM-CSF, IFN-γ, IL-1α, IL-1β, IL-6, IL-8, IL-12p70, IL-17A, IL-23, TNF), anti-inflammatory cytokines (IFN-α, IL-4, IL-10, IL-13), inflammatory chemokines (IP-10, MCP-1, MIP-1α, MIP-1β), cell adhesion and cellular mediators (ICAM-1, E-selectin, P-selectin). The assessment of possible statistical differences between the means of the three groups was performed by One-Way ANOVA. In addition, by non-parametric T-tests, we stratified the results according to selected clinical characteristics (psoriatic nail involvement, PASI ≥ 10, BMI ≥ 30).ResultsIn 80 assessed naïve patients, patients with PSO showed significant increases of E-selectin (p=0.021) and IL-8 (0.041) than other groups. In patients with PSA with PSO, significant higher levels of ICAM-1 were observed (p=0.009) than other groups. We did not observe further differences comparing pro-inflammatory and anti-inflammatory cytokines, inflammatory chemokines, and cell adhesion and cellular mediators in patients with PSO, PSA with PSO, and PSA sine PSO. Patients with psoriatic onychopathy showed significant increased levels of ICAM-1 (p=0.010) and IP-10 (0.030) than others. In patients with PASI ≥ 10, significantly enhanced values of IL-8 (p=0.004), TNF (p=0.013), E-selectin (p=0.004), MIP-1α (p=0.003), and MIP-1β (p=0.039). In patients with BMI ≥ 30, significantly higher levels of E-selectin were pointed out (p=0.035) than others.ConclusionOur findings may suggest that a similar cytokine profile may characterize naïve patients with PSO, PSA with PSO, and PSA sine PSO, reinforcing the concept of psoriatic disease continuum. However, some differences may be also shown, underlying possible pathogenic differences and leading to the clinical heterogeneity of these patients.

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Khajebishak, Yaser, Laleh Payahoo, Hamed Hamishehkar, Mohammadreza Alivand, Mahdieh Alipour, Mohammad Solhi, and Beitullah Alipour. "Effect of pomegranate seed oil on the expression of PPAR-γ and pro-inflammatory biomarkers in obese type 2 diabetic patients." Nutrition & Food Science ahead-of-print, ahead-of-print (February 28, 2019). http://dx.doi.org/10.1108/nfs-10-2018-0298.

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Purpose Diabetes is one of the most prevailed chronic diseases in the world. Pro-inflammatory cytokines play a key role in the type 2 diabetes mellitus. Pomegranate seed oil (PSO) has potential anti-inflammatory properties. The purpose of this study is to evaluate the antidiabetic effects of the use of PSO on the expression of peroxisome proliferator-activated receptor-gamma (PPAR-γ), pro-inflammatory biomarkers and lipid profile levels in obese type 2 diabetic patients. Design/methodology/approach In total, 52 patients were randomly assigned to the PSO (n = 26) and placebo (n = 26) groups. Subjects received daily PSO 3 g placebo (paraffin) in 1 g soft-gel capsules (along with breakfast, lunch and dinner meals) for eight weeks. Findings Serum levels of fasting blood sugar (FBS) decreased from 161.46 ± 34.44 to 143.50 ± 24.2 mg/dL (p = 0.008), IL-6 decreased from 5.17 ± 2.25 to 4.52 ± 1.90 (p = 0.049) and tumor necrosis factor alpha (TNF-α) significantly decreased from 9.17 ± 4.13 to 7.74 ± 2.44 pmol/mL in PSO group (p = 0.030). However, changes in the expression of PPAR-γ gene, serum levels of hs-CRP and lipid profile levels were not significant. Research limitations/implications Lack of PSO concentration measurements and the short duration of the study were the key limitations. Future randomized clinical trials with a longer period of follow-up are needed to assess the potential anti-diabetic effects of PSO. Originality/value Administration of PSO in obese type 2 diabetic patients reduced the levels of FBS, interleukin 6 and TNF-α; nevertheless, changes in the insulin, lipid profiles and hs-CRP were not significant.

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Tran, Le Thi Bao. "Adjusting Parametersin Optimize Function PSO." International Journal of Social Science And Human Research 05, no. 03 (March 21, 2022). http://dx.doi.org/10.47191/ijsshr/v5-i3-30.

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Particel Swarm Optimization (PSO) is a form of population evolutionary algorithm introduced in the early 1995 by two American scientists, sociologist James Kennedy and electrical engineer. Russell. This thesis mainly deals with the PSO optimization algorithm and the methods of adaptive adjustment of the parameters of the PSO optimization. The thesis also presents some basic problems of PSO, from PSO history to two basic PSO algorithms and improved PSO algorithms. Some improved PSO algorithms will be presented in the thesis, including: airspeed limit, inertial weighting, and coefficient limit. These improvements are aimed at improving the quality of PSO, finding solutions to speed up the convergence of PSO. After presenting the basic problems of the PSO algorithm, the thesis focuses on studying the influence of adjusting parameters on the ability to converge in PSO algorithms. PSO algorithms with adaptively adjusted parameters are applied in solving real function optimization problems. The results are compared with the basic PSO algorithm, showing that the methods of adaptive adjustment of the parameters improve the efficiency of the PSO algorithm in finding the optimal solutions.

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Journal articles on the topic 'PSO'

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